Carbon Dioxide Emission Factors Research Articles

A decomposition and decoupling analysis of carbon dioxide emissions from electricity generation: Evidence from the EU-27 and the UK

In this paper the driving factors of carbon dioxide emissions from electricity generation in the European Union are examined for the years 2000–2018, separated into three time periods using decomposition analysis, and particularly LMDI-I. Seven driving factors are examined, namely the economic activity effect, the population effect, the electricity intensity effect, the electricity trade effect, the energy intensity effect, the generation structure effect, and the emissions factor effect. The results showed that the main driving factor leading to increased carbon dioxide emissions is the economic activity effect counterbalanced mainly by the contribution of the generation structure effect. Moreover, a decoupling analysis between economic growth and carbon dioxide emissions was carried out aiming to identify the state of each country for each period. In the third examined period (2013–2018) most countries in the EU-27 are in a state of strong decoupling.

Research on application of a hybrid heuristic algorithm in transportation carbon emission.

China's transportation industry is entering a stage of high-quality development. Carbon emissions and environmental protection issues have put pressure on the construction of a green and low-carbon transportation system, and the transportation industry has become one of the industries with the fastest growth in carbon emissions. Therefore, it is of great significance to study the influencing factors of carbon dioxide emissions in the transportation industry and predict its carbon emissions. This article first thoroughly analyzes the main sources of carbon emissions in the transportation industry, including nine major energy consumption sources such as coal, gasoline, and diesel, and obtains the carbon emission values ​​from 2000 to 2017. Secondly, a linear regression analysis was performed on 13 pre-selected influencing factors and CO2 emissions in the transportation industry. In order to obtain the potential similarities between the factors, factor 13 is divided into four categories: economic scale, population size, transportation structure, and energy consumption. Each category and factor analysis is divided into four potential factors. Third, a training model was established based on the data from 2000 to 2012. Four algorithms, neural network (BP), extreme learning machine (ELM), genetic algorithm optimized neural network (GA-BP), and genetic algorithm optimized extreme learning machine (GA-ELM) are used to predict 2013 to 2017 and compare the predicted value of its respective algorithm with the actual value. Finally, the results show that the genetic algorithm optimized extreme learning machine (GA-ELM) hybrid heuristic algorithm has the highest degree of fit between the predicted value and the true value, which further illustrates the carbon emissions of the hybrid heuristic algorithm in the transportation industry. For the superiority of the prediction, the study also shows that the four influencing factors seriously affect the carbon emissions of the transportation industry. Therefore, accelerating the upgrading of the transportation structure and changing the proportion of energy consumption will be important measures for the transportation sector to control carbon emissions in the next step, so as to promote the sustainable development of the transportation system.

Soil Physical Properties, Carbon dioxide Emissions and Their Relationships under Different Management Systems in Semi-arid Region of Eastern Tunisia

ABSTRACT In agricultural systems, soil carbon dioxide emissions and physical properties are thought to depend largely on management practices. This field study was carried out in a semi-arid region of eastern Tunisia to evaluate the effects of tillage management on soil carbon dioxide emissions and related physical properties; bulk density (BD), penetration resistance (PR), total porosity (TP) and air-filled porosity (AFP). Tillage management treatments included plowing with a moldboard plow or a disk plow to different depths, described here as shallow (10 cm), medium (15 cm) and deep (25 cm). No-tillage was also considered as a control plot. Correlation analysis was used to explore how soil carbon dioxide emissions (CO2) were related to the other studied properties. The results showed higher carbon dioxide (CO2) emissions (p < .05) from tilled soil compared to no-till (NT), regardless of the tillage management. No significant differences in carbon dioxide (CO2) emissions were found between moldboard and disk plow tillage at the same tillage depth. Soil carbon dioxide release was the highest after deep tillage (moldboard = 0.101 t ha−1 and disk plow = 0.107 t ha−1) suggesting that deeper tillage to 25 cm promoted higher carbon dioxide (CO2) emissions. Significant differences with tillage were observed in bulk density (BD) and penetration resistance (PR) compared to no-tillage. Correlations of carbon dioxide emissions to soil physical properties across all the tillage treatments indicated significant negative relationships between carbon dioxide (CO2) emissions and soil bulk density (BD) and penetration resistance (PR) and significant positive relationships between carbon dioxide (CO2) and total porosity (TP) and air-filled porosity (WFP) suggesting that these soil attributes are important controlling factors of carbon dioxide (CO2) emissions.

Multiple Objective Social Group Optimization for Time–Cost–Quality–Carbon Dioxide in Generalized Construction Projects

This study presents a novel approach named as “multiple objective social group optimization” (MOSGO) to tradeoff time, cost, quality, and carbon dioxide emission (TCQC) factors in generalized construction projects. The proposed algorithm modifies the operation mechanism to balance the exploration and exploitation abilities of the optimization process. The TCQC tradeoff problem considers all types of logical relationships between project activities. Two practical case studies demonstrate the ability of MOSGO-generated, non-dominated solutions. In addition, evidential reasoning is applied to select a compromise solution for project implementation. Comparisons between the MOSGO and four well-known algorithms (MODE, MOABC, MOPSO, and NSGA-II) to verify the efficiency and effectiveness of the developed algorithm. According to the statistical analysis, the proposed algorithm generated the highest values of diversification measurement (DM) of 26.113 and 40.27; the highest values of hyper-volume (HV) of 0.875 and 0.881 in case 1 and case 2, respectively. The proposed algorithm also found solutions with lowest mean ideal distance (MID) and spread (SP) values of 0.872 and 0.462 in the first case and of 0.754 and 0.689 in the second case. MOSGO showed had better diversify and convergence, gained wider spread, and yielded higher uniformity of solutions than the compared multiple objective evolutionary algorithms.

Measurement Models for Carbon Dioxide Emission Factors of Passenger Cars Considering Characteristics of Roads and Traffic.

In order to effectively control carbon dioxide emissions of motorized vehicles, it is very important to measure their carbon dioxide emission factors. The objective of this paper was to develop measurement models for the carbon dioxide emission factors of passenger cars. Road systems of downtown areas of four typical Chinese counties were explored and 12 types of basic road networks were recognized and defined. With PTV Vissim, microscopic traffic simulation models were set up for every type of basic road network, average speeds of the simulated cars were collected, and carbon dioxide emissions were calculated using MOVES (Motor Vehicle Emission Simulator) software. For model development, the paper put forth two compound explanatory variables: the weighted average of segment lengths and the sum of critical ratios of volume to saturation flow rate. Six functional relationships for the variables were tested and the double exponential function was proven to be the most appropriate. Finally, for each of the 12 types of basic road networks, a measurement model for carbon dioxide emission factors was calibrated using the double exponential function for the variables. The measurement models can be used to estimate the carbon dioxide emissions of passenger cars concerning potential improvement schemes impacting traffic demand and/or traffic supply.

Establishment and research of combined forecasting model of carbon

This paper analyzes the influencing factors of carbon dioxide emissions from four aspects: Population, economy, industrial structure and energy, then from the carbon emissions, economic development, industrial structure, energy consumption structure to show the status quo of carbon emissions in Hubei Province. Based on the analysis of the influencing factors, the main influencing factors of carbon emission are population, regional gross product and coal consumption The multivariate linear regression model and the polynomial curve model are established and the error analysis is carried out. The combination weight coefficients of two single models are obtained through the linear programming model and the combination forecasting model is established, finally, the corresponding countermeasures to reduce carbon emissions are put forward.

Energy and environmental performance of a heat pump in different power grid scenarios

Electric-driven heat pumps are one of the most encouraging systems that could support the raising of renewables contribution in civil sector energy consumption, especially with reference to the Countries with a high contribution of renewable energy sources in electricity generation mix. However, the evaluation of the effective energy and environmental performance of an electric heat pump meeting the space heating and cooling requests of a building has to consider several factors that affect the results. Among them great attention should be given to the variability of weather conditions in which the system operates, the changeability of the efficiency and environmental indicators of power grid. The analysis becomes more complex if the variation of these parameters is considered in terms of time and geographic location but it leads to the actual evaluation of the energy conversion system performance that is neglected by European Regulations. This paper presents an energy and environmental analysis of an electric-driven air-source heat pump providing the space heating and cooling needs of the same building located in two different geographical locations by means of a dynamic simulation performed in TRNSYS 17. The analysis is carried out considering the average and time-dependent values of the carbon dioxide emission factors for electricity and the power grid efficiency indicators evaluated by means of the real electricity generation data. In addition, the paper proposes the evaluation of the average and hourly energy and environmental parameters referred only to the electricity market zones in which the buildings are located. These indices are considered in the analysis too. The results have highlighted that the assessment based on average and high-resolution parameters, as well the evaluation based on indicators referred to electricity market zones only, could return very different outcomes leading to a significant overestimation or underestimation of the energy and environmental performance of the system based on the electric-driven heat pump. Finally, a further analysis has been carried out to determine how the results can vary considering the average value of the electric efficiency indicator suggested by Italian Regulations and that of other European Countries.

Study on the threshold effect of China’s industrial structure on carbon emission

In recent years, carbon emission has become the focus of environmental governance and protection. Countries have made active efforts to reduce carbon emission while economic development. As the largest developing country and the second largest economy, China promises to reduce carbon emissions to the world in the critical period of industrial transformation, so the impact of industrial structure on carbon emissions has become an important problem to be solved. Based on the provincial panel data of China from 1999 to 2016, this paper estimates the influencing factors of carbon dioxide emissions, constructs threshold regression model, and studies the non-linear impact of industrial structure on carbon emissions in China. The results show that carbon emissions are increasing, while the industrial structure has a single threshold effect. And based on the empirical conclusion, according to the actual situation of our country, this paper put forward brief policy recommendations, including low-carbon technology development and the use of renewable clean energy, in order to better promote carbon emission reduction in our country.

Driving forces of China's multisector CO2 emissions: a Log-Mean Divisia Index decomposition.

To figure out which factor contributes more on carbon emissions caused by energy consumption, this research took multisector analysis based on the Log-Mean Divisia Index Method (LMDI) and decoupling theory to assess the driving factors of carbon dioxide (CO2) emissions in China's six sectors from 2003 to 2016. Our empirical results reveal that China's economy can be divided as three decoupling stages and exhibited a distinct tendency toward strong decoupling with a turning point in 2008. Thus, we discuss the impact of 2008 economic crisis on carbon emissions based on decomposition results. The empirical results of our study show the following five conclusions. (1) Most sectors in China are in weak decoupling state due to the inhibition of energy intensity on carbon emissions. (2) Different factors contribute differently to reducing emissions in different sectors, economic output has the most prominent effect, followed by energy intensity and population scale. (3) China's current carbon emission reduction measures benefit more on energy efficiency. (4) The economic crisis has greatly reduced energy efficiency and has no significant impact on other factors. (5) If all industries adjust their energy mix, carbon emissions in China can be reduced by almost 17% every year.

The clarification for the features, temporal variations, and potential factors of global carbon dioxide emissions

In recent decades, global carbon dioxide emissions have increased dramatically, slowing down the carbon emissions increase in the atmosphere has become a realistic challenge for all countries in the world. In this context, clarifying the features, temporal variations, and potential factors of global carbon emissions are of great significance for controlling its carbon emissions. This paper adopted the logarithmic mean Divisia index to investigate the potential factors of CO2 emissions from the global, region, and country perspectives, with the purpose of providing more accurate information for the emissions mitigation efforts of various countries. The results show that during 1990–2014 period, the Asia-Pacific countries were the largest CO2 emitters worldwide, while the Europe and Eurasian countries were contributors to global carbon abatement. Economic output growth contributed the most in promoting global CO2 emissions growth, especially in Asia-Pacific, Europe and Eurasia, and North America. Population scale expansion was another determinant leading to emissions growth, and particularly for the countries in Middle East and Africa. Fortunately, the significant decline of energy intensity contributed most to global emission-reduction. However, except for European and Eurasian countries, the inhibiting effects resulted from energy intensity decline cannot completely counteract the promoting effects in most cases. From the perspective of country level, the performances of different factors were distinctly various in different countries, thus emission-mitigation policies should be established and implemented tailored to their characteristics.

Investigating the Influencing Factors of Carbon Dioxide Emissions from Residents’ Energy Consumption in China

With the rapid development of the social economy and the accelerated process of urbanization, the consumption level of residents, especially the demand of energy consumption has increased greatly, which will become an important reason for the growth of carbon dioxide emissions in the future. China has concentrated on policies and measures for energy conservation and emission reduction in the industrial and industrial sectors, ignoring the issue of carbon emissions in the residents’ consumption for a long time. This paper calculates the carbon dioxide emissions of residents’ energy consumption for 1991-2017 based on thirteen kinds of energy consumption. Then constructing the STIRPAT model to divide the influence factors into 5 factors: population, urbanization rate, GDP per capita, energy structure and energy intensity. This paper constructed the cointegration equation and the VEC model through the unit root test, Johansen cointegration test and Grainger causality test by Eviews8 software. Finally, this paper purposed the measures of reducing the carbon dioxide emissions of residents’ energy consumption by reducing the carbon dioxide emissions per capita and adjusting the energy structure.

Order Acceptance and Scheduling Problem with Carbon Emission Reduction and Electricity Tariffs on a Single Machine

Order acceptance and scheduling (OAS) problems are realistic for enterprises. They have to select the appropriate orders according to their capacity limitations and profit consideration, and then complete these orders by their due dates or no later than their deadlines. OAS problems have attracted significant attention in supply chain management. However, there is an issue that has not been studied well. To our best knowledge, no prior research examines the carbon emission cost and the time-of-use electricity cost in the OAS problems. The carbon emission during the on-peak hours is lower than the one in mid-peak and off-peak hours. However, the electricity cost during the on-peak hours is higher than the one during mid-peak and off-peak hours when time-of-use electricity (TOU) tariff is used. There is a trade-off between sustainable scheduling and the electricity cost. To calculate the objective value, a carbon tax and carbon dioxide emission factor are included when we evaluate the carbon emission cost. The objective function is to maximize the total revenue of the accepted orders and then subtract the carbon emission cost and the electricity cost under different time intervals on a single machine with sequence-dependent setup times and release date. This research proposes a mixed-integer linear programming model (MILP) and a relaxation method of MILP model to solve this problem. It is of importance because the OAS problems are practical in industry. This paper could attract the attention of academic researchers as well as the practitioners.

Analysis of influencing factors of the carbon dioxide emissions in China's commercial department based on the STIRPAT model and ridge regression.

Commercial department assumes the vital part in energy conservation and carbon dioxide emission mitigation of China. This paper applies the time-series data covering 2001-2015 and introduces the STIRPAT method to research the factors of commercial department's carbon dioxide emissions in China. The combination of STIRPAT method and ridge regression is first adopted to research carbon dioxide emissions of commercial department in China. Potential influencing factors of carbon dioxide emission, including economic growth, level of urbanization, aggregate population, energy intensity, energy structure and foreign direct investment, are selected to establish the extended stochastic impacts by regression on population, affluence and technology (STIRPAT) model, where ridge regression is adopted to eliminate multicollinearity. The estimation consequences show that all forces were positively related to carbon dioxide emissions in China's commercial department except for energy structure. Energy structure is the only negative factor and aggregate population is the maximal influencing factor of carbon dioxide emissions. The economic growth, urbanization level, energy intensity and foreign direct investment all positively contribute to carbon dioxide emissions of commercial department. The findings have significant implications for policy-makers to enact emission reduction policies in commercial sector. Therefore, the paper ought to take into full consideration these different impacts of above influencing factors to abate carbon dioxide emissions of commercial sector.

Electric efficiency indicators and carbon dioxide emission factors for power generation by fossil and renewable energy sources on hourly basis

The power system has faced unprecedented challenges in the last decade caused by many factors: the introduction of the open electricity market, the diffusion of distributed generation, the growth of the electricity production from the no-programmable renewable energy sources, etc. In this framework the power generation system turned extremely varied and complex. In addition, European Union highlighted the need to increase the energy efficiency to achieve the goal of 30% savings in primary energy consumption by 2030 compared to 1990 levels. Since the goal is defined on primary energy level, the electric efficiency indicators are central issues in the assessment of energy saving measures that affect electricity demand and involve different processes. However, all European Directives set electric efficiency indicators and carbon dioxide emission factors to fixed values neglecting their intrinsic time-variability due to the primary energy source mix used in the electricity generation hour by hour. The use of a fixed value for these parameters could lead to inaccurate or wrong results in various processes. Indeed, they have an impact on the choice of electricity versus fossil-fuel based technologies. In particular, they affect both the possibility for a product to retain access to European market, and its specific rating in an energy labelling class. Moreover, in the case of the modelling of any energy system that interacts with the power grid the electric efficiency indicators are the crucial factors to assess the potential primary energy savings of the technologies. On another level, the use of an incorrect value of energy and environmental indicators in the evaluation of the primary energy savings of those energy efficiency measures supported by economic mechanisms (as white certificates, policy mechanisms supporting renewables, tax deductions), could lead to an underestimation or an overestimation of the economic support. In this context, the aim of this paper is to perform an hourly analysis of the Italian electricity production mix evaluating the contribution of each primary energy source. The novelty of this approach is related to the evaluation of the electricity produced by each fossil and renewable source with an hourly time resolution over the years 2016 and 2017. Four power plant efficiency indicators and three carbon dioxide emission factors have been also defined and determined on hourly basis for both considered years. The results of the analysis have showed the strongly variability of the considered parameters across the years, the seasons and hour by hour depending upon the hourly power generation mix.

Emission Factors Derived from 13 Euro 6b Light-Duty Vehicles Based on Laboratory and On-Road Measurements

Tailpipe emissions of a pool of 13 Euro 6b light-duty vehicles (eight diesel and five gasoline-powered) were measured over an extensive experimental campaign that included laboratory (chassis dynamometer), and on-road tests (using a portable emissions measurement system). The New European Driving Cycle (NEDC) and the Worldwide harmonised Light-duty vehicles Test Cycle (WLTC) were driven in the laboratory following standard and extended testing procedures (such as low temperatures, use of auxiliaries, modified speed trace). On-road tests were conducted in real traffic conditions, within and outside the boundary conditions of the regulated European Real-Driving Emissions (RDE) test. Nitrogen oxides (NOX), particle number (PN), carbon monoxide (CO), total hydrocarbons (HC), and carbon dioxide (CO2) emission factors were developed considering the whole cycles, their sub-cycles, and the first 300 s of each test to assess the cold start effect. Despite complying with the NEDC type approval NOX limit, diesel vehicles emitted, on average, over the WLTC and the RDE 2.1 and 6.7 times more than the standard limit, respectively. Diesel vehicles equipped with only a Lean NOX trap (LNT) averaged six and two times more emissions over the WLTC and the RDE, respectively, than diesel vehicles equipped with a selective catalytic reduction (SCR) catalyst. Gasoline vehicles with direct injection (GDI) emitted eight times more NOX than those with port fuel injection (PFI) on RDE tests. Large NOX emissions on the urban section were also recorded for GDIs (122 mg/km). Diesel particle filters were mounted on all diesel vehicles, resulting in low particle number emission (~1010 #/km) over all testing conditions including low temperature and high dynamicity. GDIs (~1012 #/km) and PFIs (~1011 #/km) had PN emissions that were, on average, two and one order of magnitude higher than for diesel vehicles, respectively, with significant contribution from the cold start. PFIs yielded high CO emission factors under high load operation reaching on average 2.2 g/km and 3.8 g/km on WLTC extra-high and RDE motorway, respectively. The average on-road CO2 emissions were ~33% and 41% higher than the declared CO2 emissions at type-approval for diesel and gasoline vehicles, respectively. The use of auxiliaries (AC and lights on) over the NEDC led to an increase of ~20% of CO2 emissions for both diesel and gasoline vehicles. Results for NOX, CO and CO2 were used to derive average on-road emission factors that are in good agreement with the emission factors proposed by the EMEP/EEA guidebook.

Dynamic analysis of carbon dioxide emissions in China's petroleum refining and coking industry

As one of the energy-intensive industries, the CO2 emissions of China's petroleum refining and coking industry (PRCI) put significant pressure on China's emission reduction target. Considering this, the main purpose of this paper is to clarify the determinants of emission growth in China's PRCI and predict the emissions change trend in future. Based on an extended Kaya identity, this paper dynamically analyzed the impacts of investment scale, investment efficiency, energy intensity, energy structure and carbon dioxide emission factor on CO2 emissions from China's PRCI. Then, the possible CO2 emissions of China's PRCI in 2030 were dynamically predicted based on the application of probabilistic scenario analysis. The main findings include: (1) during 2000–2016, China's PRCI suffered 130.82 million tons CO2 emissions growth, investment scale expansion was the principal contributor stimulating CO2 emissions. Fortunately, the promoting effects exerted from this factor showed a downtrend along with the decreasing fixed-asset investment growth rate since China's economy has entered a new normal. (2) During the entire study period, energy structure adjustment brought about a mild increase in CO2 emissions rather than taking an active role in emission abatement. (3) Investment efficiency and energy intensity were two crucial inhibitors, followed by the carbon dioxide emission factor. However, the inhibiting effects resulting from these factors cannot completely counteract the promoting effects in most cases. (4). Scenario analysis indicates that the mitigation pathway of CO2 emissions in China's PRCI include properly curbing the fixed-asset investment, increasing the green investment for technology and equipment, paying more attention to the R&D of energy saving technology in PRCI and its downstream industry, and facilitating the use of clean electricity.

Research on the Carbon Dioxide Emission Factor as a Result of Fuel Combustion

The present paper first discusses a calculation model for the complete combustion of fuels � with the minimum amount of air needed � whose volumetric and mass composition is known. It then describes evaluation models for the heat resulting from fuel combustion, i.e., superior and inferior caloric power value. In this context, the carbon dioxide emission factors for fuel and biofuel combustion, respectively, are evaluated. The results obtained have allowed a comparative analysis regarding carbon dioxide emission.

Examining the influencing factors of CO2 emissions at city level via panel quantile regression: evidence from 102 Chinese cities

ABSTRACTAscertaining the influencing factors of carbon dioxide emissions in Chinese cities is an important issue for policy-makers. This paper investigates the effect of several determinants on carbon emissions per capita in Chinese cities. Non-normally distributed and heterogeneous features of carbon emissions per capita in Chinese cities are considerably important. The empirical results demonstrate that GDP per capita has an increasingly positive impact on carbon emissions per capita due to the growth in household consumption. Urbanization has a slightly decreasing positive effect on carbon emissions per capita with a quantile increase resulting from continuous highway construction. Industrialization has a decreasing positive effect with carbon emission per capita quantile increases because of increasing energy efficiency and lower costs related to carbon reductions. The population has a decreasing negative effect on carbon emissions because of people’s increasing demand for environmental safety. The distributions of emissions per capita conditional on the 10th and 90th quantiles of independent variables also vary considerably. Specific policy implications are provided based on these results.

The evaluation methodology on carbon emissions of passenger cars based on travel characteristics

In recent years, with the rapid growth of economy, China’s urbanization and urban transport are undergoing a rapid development, resulting in a large amount of travels. But at the same time, due to “blowout” growth of passenger cars, a series of serious social problems occurred, such as traffic congestion and fast increase in Carbon Dioxide emissions, which posed a great challenge to the sustainable development of urban transport in China. Studies on the evaluation methodology of Carbon Dioxide emissions based on travel characteristics are to be conducted to effectively improve the evaluation accuracy of emission quantification of passenger cars, and find out the place and source of Carbon Dioxide emissions, so as to further promote accurate emission reduction. This paper first analyzes the main characteristics of Carbon Dioxide emissions of passenger cars in China, optimizes the evaluation methodology of Carbon Dioxide emissions of passenger cars from the perspectives of scoping, road grading and classification evaluation, and conducts a test on Carbon Dioxide emission factors in some cities. The paper also takes Harbin city as an example to evaluate its Carbon Dioxide emissions of passenger cars from 2014 to 2016, so as to testify the feasibility of the methodology.

Evaluation of the carbon dioxide (CO2) emission factor from lime applied in temperate upland soil

Agricultural liming materials are broadly utilized in the world to ameliorate soil acidity. Crushed limestone (CaCO3) and dolomite (MgCO3·CaCO3) are most common liming materials. The CO2 emission coefficient was proposed as 0.12 Mg C per Mg for CaCO3 by the Intergovernmental Panel on Climate Change (IPCC), which indicates that 100% of C in CaCO3 is eventually released to the atmosphere in the form of CO2. However, this assumption appears unlikely based on current knowledge of the very low solubility of CaCO3 and carbonate transport through soil. To develop the direct CO2 emission factor from the C-containing liming materials applied in moderately acidic soil, CaCO3 was applied in a typical temperate upland soil at different levels (0–2 Mg ha−1 yr−1), and the CO2 emission rates were determined using the closed chamber method for two years. The direct CO2 emission factor from CaCO3 was estimated using 13CO2 fluxes from 13C-CaCO3 applied soils. Approximately 0.026 Mg C per Mg of CaCO3 was emitted as CO2 from CaCO3 in cropping lands. This value was much lower than the IPCC default value (0.12 Mg C per Mg of CaCO3). The current CO2 emission coefficient was comparable with the United States Environmental Protection Agency (EPA) emission factor (0.046 Mg C per Mg of CaCO3) from the agricultural field. If the EPA emission factor from the ocean (0.013 Mg C per Mg of CaCO3) is added to the currently developed CO2 emission factor of the inner agricultural field, the total CO2 emission factor from lime may be close to 0.039 Mg C per Mg of CaCO3 in the selected upland soil. Therefore, we propose that the current IPCC guideline value of the CO2 emission factor from lime should be revised as 0.039 Mg C per Mg of CaCO3 for Korean upland soils.