Greenhouse Gas Emission Factors Research Articles

A BIM-Based construction and demolition waste information management system for greenhouse gas quantification and reduction

Excessive greenhouse gas (GHG) emissions have caused serious global warming and climate change. As the construction and demolition waste (CDW) end of life disposal process is a source of GHG emissions, it is vital to precisely estimate and reduce CDW end of life GHG emissions. The precise estimation of CDW information is essential for GHG estimation and reduction. However, as current CDW information and associated GHG estimation methods tend to be inaccurate, in this paper, a building information modeling (BIM)-based CDW information management system (IMS) is constructed that provides precise CDW information and a detailed information estimation process. To facilitate the accurate quantification of CDW disposal GHG emissions, mathematical formulae that include several GHG emission factors are proposed. A practical case study from China is then given to demonstrate the efficiency and effectiveness of the proposed methodology in quantifying and reducing CDW disposal GHG emissions. Accurate GHG emission estimation are determined for fifteen CDW disposal types and a comparison are conducted. Concrete was found to have the highest carbon dioxide, methane, and nitrous oxide emissions during transportation and the highest carbon dioxide emissions during handling, and organics were found to have the highest decomposition methane emissions. Targeted suggestions are given for source-separated CDW disposal GHG emission reduction based on the different emission characteristics. The combination of the BIM-based CDW IMS and the developed mathematical formulae were found to efficiently and accurately determine the CDW information and the associated GHG emissions, and therefore provide a practical guide to reducing the serious environmental damage generated by CDW disposal.

Life cycle greenhouse gas emissions of Electric Vehicles in China: Combining the vehicle cycle and fuel cycle

Electric Vehicles (EVs) are known as the future vehicles that have the potential to provide environmental benefits all over the world. The Greenhouse Gas (GHG) emissions of EVs have already been estimated for each phase in the life cycle. However, the dedicated estimations in China are not complete enough to reveal the systematic impacts of real manufacturing technologies, driving cycle and recycling processes. This study has analyzed the GHG emissions of the Cradle-to-Gate (CTG) phase, Well-to-Wheel (WTW) phase and Grave-to-Cradle (GTC) phase for different vehicles in different time to figure out the key drivers and reduction opportunities, which are based on the well-selling A0-A class compact sedan model currently in China. The results indicate that the life cycle GHG emissions of an EV are about 41.0 t CO2eq in 2015, 18% lower than those of an Internal Combustion Engine Vehicle (ICEV). This value will decrease to only 34.1 t CO2eq in 2020 due to the reduction of GHG emission factor of electricity. Although the WTW phase is the largest contributor of GHG emissions for both vehicles, the proportions of each phase are quite different. The GHG emissions of the WTW phase of an EV are decreasing rapidly, but the CTG phase will not be improved at the same speed, which may become a barrier to fully take the environmental benefits of an EV. There are two major opportunities for reduction in the entire life cycle besides fuel economy development. One is EV recycling that can reduce the GHG emissions of the CTG phase by about a half. The other is the improvement of clean power grid that can further reduce the GHG emissions of the WTW phase.

Creating a “family tree” on fertilizer inventory use in life cycle assessment of oil palm: visualization of earlier studies and its implications

This review develops and applies a visualization method for understanding how chemical fertilizer inventory data, including greenhouse gas (GHG) emission factors and fossil fuel energy requirements, have been used in previous life cycle assessment (LCA) studies of oil palm and discusses how inconsistencies detected in previous studies can be decreased. As a visualization method for previous publications, a “family tree” was constructed using a directed graph (digraph) representation. Each node in the graph indicates an article, and an arrow from the source to a destination illustrates that the former article was cited in the latter article as a source of the background inventories. Bibliographical data extracted from the Web of Science were used for constructing the genealogy of fertilizer inventory use. Several groups (“families”) were identified through creation of the family tree. The most noticeable group was formed around the LCA database ecoinvent, which has the maximum number of out-flows (arrows from the node), suggesting a considerable influence of ecoinvent in the LCA of oil palm. In addition, temporal and spatial inconsistencies (outdated technological assumptions and substitutional use of European data) were detected in the visualization; therefore, the severity of the inconsistencies was discussed through an analysis of scenario uncertainty in nitrogen fertilizer production. The importance of devoting attention to fertilizer production technologies rather than simply to regional differences was clarified. This study demonstrates the usefulness of applying visualization methods in understanding the overall configuration of earlier studies. It is expected that the visualization and its implications constitute a way forward to good practices in inventory analysis.

Estimating direct CO2 and CO emission factors for industrial rare earth metal electrolysis

Rare earth (RE) metals are now being widely applied in new material industries with rapidly increasing production. However, there is limited research on greenhouse gas emission factors from RE metals production by electrolysis and currently no method exists to account for this industry’s CO2 emissions in the ‘2006 IPCC Guidelines for National Greenhouse Gas Inventories’. This study employed two independent methods to determine direct carbon emission factors for industrial rare earth metals production by molten-salt electrolysis: continuous emissions monitoring by FTIR and time-integrated sampling with offline lab analysis, both measuring CO2 and CO concentrations in the exhaust gases from Pr-Nd, Dy-Fe and La potlines in three companies in China. The study confirmed that CO2 contributes >97% of the total carbon emission factor. Direct carbon emissions per tonne RE metal electrolysis is equivalent to one tenth to a half of the emission factor for aluminum production (due to much lighter molar mass of Al) but is similar on a mole basis (carbon emissions per mole metal). Emission factors vary with the type of rare earth metal or alloy produced and from one facility to another, ranging from 165.0 to 672.4 kg/t-RE metal for CO2, 3.00 to 8.23 kg/t-RE metal for CO and 46.4 to 186.8 kg/t-RE metal for total carbon. Direct CO2 and CO emission factors from the two exhaust gas monitoring methods agreed well considering the uncertainties.

Estimating greenhouse gases emissions from horticultural peat soils using a DNDC modelling approach

Peat soils represent an important global carbon (C) sink, but can also provide a highly fertile medium for growing horticultural crops. Sustainable crop production on peat soils involves a trade-off between ensuring food security and mitigating typically high greenhouse gas (GHG) emissions and rates of soil C loss. An alternative approach to resource intensive field-based monitoring of GHG fluxes for all potential management scenarios is to use a process-based model driven by existing field data to estimate emissions. The aim of this study was to evaluate the suitability of the Denitrification-Decomposition (DNDC) model for estimating emissions of CO2, N2O and CH4 from horticultural peat soils. The model was parameterised using climatic, soil, and crop management data from two intensively cultivated sites on soils of contrasting soil organic matter (SOM) contents (∼35% and ∼70% SOM content). Simulated emissions of CO2, N2O and CH4, and simulated soil physical and crop output values, were compared to actual GHG, soil and crop measurements. Model performance was assessed using baseline parameterisation (i.e. model defaults), then calibrated using pre-simulation and sensitivity analysis processes. Under baseline parameterisation conditions, DNDC proved poor at predicting GHG emissions and soil/crop variables. Calibration and validation improved DNDC performance in estimating the annual magnitude of emissions, but model refinement is still required for reproducing seasonal GHG patterns in particular. Key constraints on model functioning appear to be its ability to reliably model soil moisture and some aspects of C and nitrogen dynamics, as well as the quality of input data relating to water table dynamics. In conclusion, our results suggest that the DNDC (v. 9.5) model cannot accurately reproduce or be used to replace actual field measurements for estimation of GHG emission factors under different management scenarios for horticultural peat soils, but may be able to do so with further modification.

철도수송부문 온실가스 배출 요인 분해분석

In this paper, I analyze the GHG (greenhouse gas) emission factor of the domestic railway transportation sector using the LMDI (Log Mean Divisia Index) methodology. These GHG factors are the emission factor effect, energy intensity effect, transportation intensity effect, and economic activity effect. The analysis period was from 2011 to 2016, and the analysis objects were an intercity railway, wide area railway, and urban railway. The results show that the GHG emission of railway transportation sector decreased during these 6 years. The factors decreasing the GHG emission are the emission factor effect, energy intensity effect, and transportation intensity effect, while the factor increasing the GHG emission is the economic activity effect.

Relationships between field-measured hydrometeorological variables and satellite-based land surface temperature in a hemiboreal raised bog

Temperature regime is one of the main controlling factors of greenhouse gas (GHG) emissions from peat bogs. Remotely sensed land surface temperature (LST) has a potential to become an efficient instrument in environmental monitoring of carbon dioxide and methane emissions from peat bogs. This paper examines the relationships between field-measured hydrometeorological variables and MODIS LST data in a hemiboreal raised bog for a period from May to September (2008–2016). The Pearson product-moment correlation was used to reveal the relationship between the field-measured parameters and LST over years and months. A multiple linear regression was chosen to model relationships between the hydrometeorological variables and LST by month. It was found that the relationships between the studied parameters and LST were year- and month-dependent. The main factor of LST was air temperature, and the correlation between LST and air temperature was the strongest during the entire period of study. This study has shown that the hydrometeorological factors of LST can explain 67%–81 % of the variance in LST in a hemiboreal raised bog. The relationships between the hydrometeorological variables and LST may be implemented in more accurate GHG emissions estimation from bogs.

Are Free Trade Agreements Good for the Environment? A Panel Data Analysis

This study attempts to empirically re-examine the relationship between free trade agreements (FTAs) and greenhouse gas (GHG) emissions. For this aim, we chose three different free trade agreements: Southern Common Market (MERCOSUR), North American Free Trade Agreement (NAFTA), and the Australia-United States Free Trade Agreement (AUSFTA). These FTAs are between developing countries, developed and developing countries, and only developed countries, respectively. Panel unit root, panel cointegration, and fully modified OLS (FMOLS) estimators are employed to find the long-run relationship between GHG emission, trade liberalization, and other economic factors. The results indicate that the environmental effect of a free trade agreement depends on the agreement type. When the agreement is among only developed or only developing countries, there is no environmental damage to the world and these types of FTAs can be beneficial for the world environment. However, when developing and developed countries are in the agreement, world GHG emissions increase.

An Investigation of the Uncertainty of Handbook of Emission Factors for Road Transport (HBEFA) for Estimating Greenhouse Gas Emissions: A Case Study in Beijing

The reliability of estimation of emissions under the Handbook of Emission Factors for Road Transport (HBEFA) model has not been thoroughly studied and the mechanics of the model have not been fully investigated. The HBEFA structure of typical driving cycles is analyzed in this study regarding its uncertainty for estimating greenhouse gas (GHG) emissions. The key parameter of the HBEFA model, relative positive acceleration (RPA), is analyzed for its sensitivity to GHG emissions. The results indicate that uncertainties in estimation of GHG emissions do exist in the HBEFA model. The uncertainties are derived from the adoption of selected driving cycles and from the parameter of RPA. Given the uncertainties of estimating the GHG emission factors of driving cycles, it might be difficult for the selected driving cycles to represent all traffic situations. Uncertainties also exist in the selection of the top-20 driving cycles. For the selected samples of driving cycles, the sensitivity of RPA is found to be insufficient in describing the emission differences, due to the insensitivity of RPA to acceleration and deceleration. Different driving characteristics may still yield the same RPA, which indicates the potential inability of RPA to capture specific traffic scenarios such as intersections, ramps, and congestion.

Cradle-to-cooked-edible-meat analysis of greenhouse gas emissions

This study aimed to synthesize mean cradle-to-cooked-edible-meat greenhouse gas emission factors for bovine, ovine, pig, and poultry meat through a systematic review process. 64 studies involving 42 countries/regions, published between 1997 and 2015 were identified meeting selection criteria using Cochrane search strategies. Major emission activities were identified and synthesised into a cradle-to-cooked-edible-meat lifecycle system. 270 emission factors were identified from publications and taken as baselines. 67% baseline evaluation boundaries stopped at farm-gate, only 3% at home/restaurants. Baseline emission gaps were identified by comparing baseline evaluation systems against the synthesised system. Emission factors for major activities were identified from publications and mean values were used to fill emission gaps in order to obtain the cradle-to-cooked-edible-meat emission factors, which were then grouped to obtain a mean cradle-to-cooked-edible-meat emission factor for each meat type. Mean emission factors (kg CO2-eq/kg) for adjusted cradle-to-cooked-edible-meats were: Bovine 61.3 (n = 124), Ovine 61.2 (n = 38), Pig 15.8 (n = 56) and Poultry 9.4 (n = 52), which are significantly higher than the adjusted means for carcass at regional-distribution-centre: Bovine 23.9 (n = 118), Ovine 23.3 (n = 9), Pig 5.0 (n = 51) and Poultry 3.6 (n = 45); or the adjusted means for saleable meat at retail: Bovine 32.3 (n = 122), Ovine 31.2 (n = 36), Pig 7.9 (54), and Poultry 4.2 (n = 50). This study confirmed current meat emission evaluations reflect only a fraction of dietary impacts. Emission factors for cooked-edible-meat could be three times the amount of meat commodities at farm-gate. Emission factors vary significantly within and between meat types.

Improved region-specific emission factors for enteric methane emissions from cattle in smallholder mixed crop: livestock systems of Nandi County, Kenya

National greenhouse-gas (GHG) inventories in most developing countries, and in countries in Sub-Saharan Africa in particular, use default (Tier I) GHG emission factors (EFs) provided by the Intergovernmental Panel on Climate Change (IPCC) to estimate enteric methane (CH4) emissions from livestock. Because these EFs are based on data primarily from developed countries, there is a high degree of uncertainty associated with CH4 emission estimates from African livestock systems. Accurate Tier II GHG emission reporting from developing countries becomes particularly important following the Paris Climate agreement made at COP21, which encourages countries to mitigate GHG emissions from agricultural sources. In light of this, the present study provides improved enteric CH4 emission estimates for cattle in Nandi County, Western Kenya, representing a common livestock production system found in East Africa. Using the data from measurements of liveweight and liveweight change, milk production and locomotion collected from 1143 cattle in 127 households across 36 villages over three major agro-ecological zones covering a full year, we estimated total metabolic energy requirements. From this and assessments of digestibility from seasonally available feeds, we estimated feed intake and used this to calculate daily CH4 production by season, and, subsequently, created new EFs. Mean EFs were 50.6, 45.5, 28.5, 33.2 and 29.0 kg CH4/head.year for females (>2 years), males (>2 years), heifers (1–2 years), young males (1–2 years) and calves (<1 year) respectively, and were lower than the IPCC Tier I estimates for unspecified African adult cattle, but higher for calves and young males. Thus, using IPCC Tier 1 EFs may overestimate current enteric CH4 emissions in some African livestock systems.

Monitoring of greenhouse gas emissions from farm-scale anaerobic piggery waste-water digesters

AbstractPig manure management systems in Taiwan differ from the model representing the Asian region developed by the Intergovernmental Panel on Climate Change (IPCC). The current study was undertaken to update greenhouse gas (GHG) emission factors of anaerobically treated piggery waste water by operating the conventional three-step piggery waste-water treatment system from selected pig farms located in northern, central and southern Taiwan. Biogas mass flow meters were installed to the outlet of anaerobic basins prior to the biogas pressure stabilizers for direct and reliable biogas measurement. The analytic results showed that average GHG emissions were 0.088, 0.128 and 0.066 m3/head/day in the northern, central and southern pig farms, respectively. Thus, the average emission levels of methane and nitrous oxide were 14.38 and 0.055 kg/head/year, respectively, from anaerobic digestion of piggery waste water for the three pig farms. The average removal efficiency of chemical oxygen demand, biochemical oxygen demand and suspended solids by anaerobic digestion process from the three pig farms was about 77, 93 and 70%, respectively.

Temporal variations in the primary energy use and greenhouse gas emissions of electricity provided by the Swiss grid

It is a frequent practice nowadays to use mean annual conversion factors (CFs) when performing life-cycle assessment (LCA) of processes and products that use electricity supplied by the grid. In this paper, we conduct an hourly assessment of the greenhouse gas (GHG) emission factor, along with the conversion factors for the cumulative energy demand (CED) and its non-renewable part (CEDnr), of electricity supplied by the Swiss grid and its direct neighboring countries (France, Germany, and Austria; Italy being neglected). Based on an hourly inventory of energy flows during a one-year period (2015–2016), this attributional approach allows performance of various certification procedures of process or product manufacturing, and comparison of energy and carbon intensities of different national mixes. Hourly calculation allows evaluation of the order of magnitude of errors made when considering an annual mix. Visualization techniques are used to better understand the obtained data and to detect when strategies involving timing optimization of electricity use may be efficient. A case study is chosen to illustrate the relevance of hourly CFs when performing LCA associated to the exploitation of a given building. Moreover, mean annual CFs of interest are discriminated by electricity end-use sectors. This could be of great help for system designers willing to improve the assessment accuracy when hourly CFs are not readily available.

Technological Response Options after the VW Diesel Scandal: Implications for Engine CO2 Emissions

In the VW diesel scandal, automakers were found to be cheating with emission data, by e.g., tampering with on-board detection systems. We have calculated changes in the energy use and emissions of carbon dioxide equivalents that would arise through several options open to automakers, to ensure that the emission of nitrogen oxides is kept within the standards. Several studies show how manufacturers have also significantly underreported vehicles’ actual fuel consumption. We explain our derivation of new factors for energy consumption and greenhouse gas emissions from diesel- and gasoline-powered passenger cars, as well as their electric hybrid varieties. The results of the analysis show that energy consumption and emissions of carbon dioxide equivalents will increase in the range of 18–21% for passenger cars with diesel and hybrid diesel engines, while for cars with gasoline and hybrid gasoline, the addition is 9–10%. The analysis highlights an environmental dilemma of current car technology, but also the path-dependent ways of thinking that have been prevalent within the automotive sector. From a sociotechnical sustainability transitions perspective, Dieselgate can be viewed as a case of “regime resistance”, whereby incumbent actors seek to maintain the status quo.

The greenhouse gas emission factor of dairy cows in Hubei Province, China

The methodology 1 provided by the Intergovernmental Panel on Climate Change (IPCC) guidelines is widely used for estimating CH4 and N2O production by cattle, but the reference value is only distinguished between developed and developing countries, with large uncertainties. The objective of this study was to calculate the CH4 and N2O emissions factor by dairy and beef cattle, and the emissions in the Hubei province according to the recommendations of IPCC (2006) using regional farm management data. CH4 emission factors from enteric fermentation in Hubei province was 81.560 kg CH4/head/year for dairy cattle with CH4 emissions of 930.844 kg. CH4 and N2O emission factors from manure management were 0.408 kg/head/year and 1.307 kg/head/year for dairy cattle. The greenhouse gas emissions from breeding cows in Hubei Province in 2012 was 950.418 kg. In general, CH4 emission estimates using Tier 2 (IPCC, 2006) were slightly lower than those from Tier 1, mainly due to use of lower emission factors, especially for CH4 emitted from manure management The main CH4 emission source was enteric fermentation, being 99.512% of livestock CH4 emissions. Direct emission was the largest component of total N2O emission (56.695%).

The Role of Industrial Parks in Mitigating Greenhouse Gas Emissions from China.

This study uncovered the direct and indirect energy-related greenhouse gas (GHG) emissions of 213 Chinese national-level industrial parks, providing 11% of China's gross domestic product, from a life-cycle perspective. Direct emissions are sourced from fuel combustion, and indirect emissions are embodied in energy production. The results indicated that in 2015, the direct and indirect GHG emissions of the parks were 1042 and 181 million tonne CO2 equiv, respectively, totally accounting for 11% of national GHG emissions. The total energy consumption of the parks accounted for 10% of national energy consumption. Coal constituted 74% of total energy consumption in these parks. Baseline and low-carbon scenarios are established for 2030, and five GHG mitigation measures targeting energy consumption are modeled. The GHG mitigation potential for these parks in 2030 is quantified as 111 million tonne, equivalent to 9.1% of the parks' total emission in 2015. The measures that increase the share of natural gas consumption, reduce the GHG emission factor of electricity grid, and improve the average efficiency of industrial coal-fired boilers, will totally contribute 94% and 98% in direct and indirect GHG emissions reductions, respectively. These findings will provide a solid foundation for the low carbon development of Chinese industrial parks.

광물산업의 국가온실가스배출계수 정량 · 평가항목 가중치에 관한 연구

『The Framework Act on Low-Carbon Green Growth』 specifies the requirements for the development and verification of emission factors for establishing reliable national greenhouse gas statistics. The scope of the regulations covers the development and validation of energy, industrial processes, solvents and other product use, agriculture, land use, land use change and emission and absorption coefficients of the forestry and waste sector as defined in the 1996 IPCC Guideline and GPG 2000, The minerals sector to be covered in this study belongs to industrial processes.BR As a representative method for quantifying and evaluating GHG emission factors, there are emission grade quality grading and DARS (Data Rating Rating System) in the ‘Procedures for Preparing Emission Factor Documents (1997)’ reported by US-EPA. However, the above two methods are not specific and comprehensive, and lack the details for accurate emission factor verification. Therefore, there is a need for a method for verifying and quantifying certified greenhouse gas emission factors that reflects characteristics of each industry sector in Korea and accord with IPCC G/L and GHG target management.BR In this study, we conducted a weighted study on quantitative and evaluation lists of emission factor using questionnaires to develop a more accurate methodology for quantifying national greenhouse gas emission factors in the mineral sector. Quantification and evaluation of emission factor are classified into essential verification and quality evaluation. The essential verifications are : administrative compatibility, method of determining emission factors, emission characteristics, sampling methods and analysis methods, representativeness of data. The quality evaluations consisted of the quality control of the data, the accuracy of the measurement and analysis, the level of uncertainty, not directly affect the emission factor, but consisted of factors that determine data quality.

Emission Inventory of Greenhouse Gas from Urban Wastewater Treatment Plants and Its Temporal and Spatial Distribution in China

Urban wastewater treatment plants are considered important greenhouse gas resources with massive emissions of carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) during operation. Based on the emission factor approach of pollutant reduction, the 2014 emission inventory of greenhouse gases (CO2, CH4, and N2O) from urban wastewater treatment plants in China was established. In addition, the temporal and spatial distribution and influencing factors of greenhouse gas emissions were analyzed in this study. The results showed that total emissions of greenhouse gas from urban wastewater treatment plants in China was 7348.60 Gg (CO2-eq) in 2014, which included CO2, CH4, and N2O emissions of 6054.57 Gg, 27.47 Gg (769.08 Gg, CO2-eq), and 1.98 Gg (524.95 Gg, CO2-eq), respectively. The difference in greenhouse gas emissions among provinces was significant:high emissions appeared in the eastern areas of China, low emissions were observed in the northwest, and hardly any emissions were found in Xizang. From 2005 to 2014, annual greenhouse gas emissions from urban sewage treatment plants in China increased by 229.4%, and the rates of CO2, CH4, and N2O increased by 217.9%, 217.9%, and 520.3%, respectively. The regional economic development level and number of wastewater treatment plants were correlated the most with the emissions of greenhouse gasses, and the per-capita protein supply was closely related with the N2O emission.

Characterizing Redox Potential Effects on Greenhouse Gas Emissions Induced by Water‐Level Changes

Core Ideas Dynamics of redox potential were induced by water‐table changes in a lysimeter. The redox potential measurements well reflected the different GHG emission sources. Redox potential monitoring is a viable tool for better understanding of GHG emissions. Soil greenhouse gas (GHG) emissions contribute to global warming. To support mitigation measures against global warming, it is important to understand the controlling processes of GHG emissions. Previous studies focusing mainly on paddy rice fields or wetlands showed a strong relationship between soil redox potential and GHG emission (e.g., N2O). However, the interpretation of redox potentials for the understanding of the controlling factors of GHG emission is limited due to the low number of continuous redox measurements in most ecosystems. Recent sensor developments open the possibility for the long‐term monitoring of field‐scale soil redox potential changes. We performed laboratory lysimeter experiments to investigate how changes in the redox potential, induced by changes in the water level, affect GHG emissions from agricultural soil. Under our experimental conditions, we found that N2O emissions followed closely the changes in redox potential. The dynamics of redox potential were induced by changing the water‐table depth in a laboratory lysimeter. Before fertilization during saturated conditions, we found a clear negative correlation between redox potentials and N2O emission rates. After switching from saturated to unsaturated conditions, N2O emission quickly decreased, indicating denitrification as the main source of N2O. In contrast, the emissions of CO2 increased with increasing soil redox potentials. After fertilization, N2O emission peaked at high redox potential, suggesting nitrification as the main production pathway, which was confirmed by isotope analysis of N2O. We propose that redox potential measurements are a viable method for better understanding of the controlling factors of GHG emissions, for the differentiation between different source processes, and for the improvement of process‐based GHG models.

A model to control environmental performance of project execution process based on greenhouse gas emissions using earned value management

In response to recent climate change, which is believed to be attributed to the release of greenhouse gas (GHG) emissions, many countries are placing CO2 abatement programs such as carbon tax and cap-and-trade. Projects do have a significant share in GHGs and therefore their environmental performance, like their schedule and cost performance, should be monitored and controlled. Although many large projects would pass an environmental assessment in the project evaluation phase, the issue of environmental performance monitoring during the project execution phase has not been addressed in project management methodologies. The objective of this paper is to develop a model to estimate project GHG emissions, and to measure project GHG performance using the developed metrics, which can be used at any point in time over the life of a project. A comprehensive study is conducted to collect information on GHG emission factors of various project activity data (such as material use, energy and fuel consumption, transportation, etc.), and a user form interface is developed to calculate the total GHG of an activity. Also, a breakdown structure is proposed which supports managing all the project GHG accounts. The monitoring and control model is formulated based on the logic used in earned value management (EVM) methodology. The proposed model is then implemented to a work package of a real construction project. The results present the project initial GHG plan and show that the model is able to calculate project GHG variance by the reporting date and predict project final GHG based on a project GHG performance index. The method presented in this paper is general and can be applied to any type of projects in an organization that aims to reduce its carbon footprint. The same structure can be applied to monitor and control any other environmental impact associated with project execution process.