Carbon Dioxide Emission Factors Research Articles

Impacts of hydrogen price and carbon dioxide emission factor on bi-objective optimizations of absorption and subsequent methanation processes of carbon dioxide capture, utilization, and storage

Impacts of hydrogen price and carbon dioxide emission factor on bi-objective optimizations of absorption and subsequent methanation processes of carbon dioxide capture, utilization, and storage

Analysis of Factors Affecting Carbon Emissions based on Gray Correlation Analysis

In order to explore the influencing factors of carbon dioxide emissions, this paper collects data on carbon emissions and several influencing factors that may be related to its changes from 2004 to 2021, considering that too many indicators may make the data redundant and lack of representativeness, using the K-Means clustering algorithm to classify the indicators and defining the names of the corresponding first-level indicators, and analyzing the elbow diagrams for the determination of the model K-value. For the determination of the K value of the model, the optimal number of clusters was analyzed using the elbow diagram, so that the center of each cluster as the first-level indicator data was spliced with the normalized emission data to form a new dataset, and the correlation coefficients between the first-level indicators and the carbon emissions were finally calculated using the grey correlation analysis method. The results show that the ability to mitigate carbon dioxide content has the greatest impact on carbon content, and the correlation coefficient between the two reaches 0.756, which indicates that increasing the green area is very effective in achieving the goal of energy saving and emission reduction.

Energy-related carbon dioxide emission factors from combustion of gaseous methane fuels

Abstract The topic of this paper concerns energy-related carbon dioxide emission factors from the combustion of gaseous methane fuels. It is a very current subject, as methane fuels, mainly of fossil origin, are one of the primary energy carriers used in industrial and municipal applications for energy purposes. Consequently, the emitted carbon dioxide contributes to the intensification of the greenhouse effect in the atmosphere. The problem of the quantities characterising emissions from the combustion of methane fuels is complicated by the diversity of these fuels. This paper presents background information on the gaseous hydrocarbon fuels and the composition of the main types of natural gas. The structure of natural gas consumption in Poland, depending on the sectors of application for particular types of natural gas, is presented. On the basis of the existing data, aggregated energy-related carbon dioxide emission factors were estimated and analysed. The analysis was carried out for high-methane natural gas (HM), colliery gas (CG) and nitrified natural gas (N)—for the years 1988–2021. Trends in fuel consumption and energy-related carbon dioxide emission factors were determined for the years 1988–2021.

Prediction intervals for carbon dioxide emissions in china by extreme learning machine ensemble based on particle swarm optimization

<p>The emission of carbon dioxide is the major cause of the greenhouse effect, which has a negative impact on human survival and sustainable economic development; therefore, it is very significant for discovering the underlying influential factors of carbon dioxide emissions. In this paper, an extreme learning machine ensemble based on particle swarm optimization approach(PSO-ELMEnsemble) are applied to predict the emission of carbon dioxide, which provide estimated values as well as the corresponding reliability. In addition, the particle swarm optimization approach is used to optimize the connection weights of extreme learning machine. The performance of the proposed PSO-ELMEnsemble is experimentally validated by carbon dioxide emissions data during the period 1978-2016 by using state-of-the-art comparative method, where the proposed approach outperforms the others in achieving the best trade-off between accuracy and simplicity.</p>

Temporally dynamic carbon dioxide and methane emission factors for rewetted peatlands

Rewetting drained peatlands is recognized as a leading and effective natural solution to curb greenhouse gas emissions. However, rewetting creates novel ecosystems whose emission behaviors are not adequately captured by currently used emission factors. These emission factors are applied immediately after rewetting, thus do not reflect the temporal dynamics of greenhouse gas emissions during the period wherein there is a transition to a rewetted steady-state. Here, we provide long-term data showing a mismatch between actual emissions and default emission factors and revealing the temporal patterns of annual carbon dioxide and methane fluxes in a rewetted peatland site in northeastern Germany. We show that site-level annual emissions of carbon dioxide and methane approach the IPCC default emission factors and those suggested for the German national inventory report only between 13 to 16 years after rewetting. Over the entire study period, we observed a source-to-sink transition of annual carbon dioxide fluxes with a decreasing trend of −0.36 t CO2-C ha−1 yr−1 and a decrease in annual methane emissions of −23.6 kg CH4 ha−1 yr−1. Our results indicate that emission factors should represent the temporally dynamic nature of peatlands post-rewetting and consider the effect of site characteristics to better estimate associated annual emissions.

Assessment of carbon dioxide emission factors from power generation in Burkina Faso

Power generation is the second largest source of greenhouse gases (GHGs), particularly carbon dioxide (CO2), in Burkina Faso's energy sector. When preparing the National Communications on Climate Change, Burkina Faso uses the default emission factors of the Intergovernmental Panel on Climate Change (IPCC) to estimate emissions from power generation. This study presents an assessment of CO2 emission factors from power generation for 2018 in Burkina Faso and an assessment of the contribution of renewable energy to the reduction of CO2 emissions. The national electricity company of Burkina Faso was chosen as the scope for this study. The estimation of emission factors for combustion is based on an analysis of fuels which are characterised in terms of molecular composition, density and water content. For CO2, the emission factor is 76 903 kg/TJ for the combustion of Heavy Fuel Oil (HFO) and 73 525 kg/TJ for that of Distillate Diesel Oil (DDO). Using these emission factors, the CO2 emissions attributable to power generation in 2018 were estimated at almost 580 Gg. The CO2 emission factor for thermal power generation was estimated at 0.663 kg/kWh and that of the electricity generation mix at 0.569 kg/kWh. Finally, the use of 14.25% renewable energy in electricity generation avoided 16.7% of CO2 emissions in 2018. The emission factor of electricity production decreases with the increase in the share of renewable energy in the energy mix.

Exergy Analysis and Modeling of Pilot-Scale Pyrolysis for Magnesium Oxide Preparation from Salt Lake Bischofite Industrial Waste.

Currently, the traditional magnesium oxide production process is facing exceptional challenges arising from carbon emission restrictions and environmental protection. Waste bischofite pyrolysis has attracted much attention as a promising technology to address these challenges. Nonetheless, this process has primarily been demonstrated on a laboratory scale, with limited studies on an industrial scale. A comprehensive exergy analysis was conducted for the entire process and individual subunits within the pyrolysis process to identify potential areas for process enhancement. A FORTRAN subroutine based on empirical correlations of pyrolysis product yields was developed considering the impact of decomposition reactions on the simulation. Furthermore, the optimization of energy and exergy efficiency of the system was discussed in terms of the carbon dioxide emission factor, equivalence ratio, and pyrolysis temperature. The results show that the primary energy bottleneck lies in the combustion phase. In addition, the optimal energy and exergy efficiency conditions are a carbon dioxide emission factor of 5.3, an equivalent ratio of 1.15, and a pyrolysis temperature of 1100 °C. In comparison to the pilot-scale conditions, the energy efficiency and exergy efficiency increase by 2.55 and 3.61%, respectively. At this time, the MgO yield is 100%, and the HCl concentration is above 9.33%.

Structural characteristics and drivers of greenhouse gas emissions at county-level and long-time scales: A case study of the Anji County, China

To achieve carbon neutrality, the Chinese government needs to gain a comprehensive understanding of the sources and drivers of greenhouse gas (GHG) emissions, particularly at the county level. Anji County in eastern China is a typical example of an industrial transformation from quarrying to a low-carbon economy. This study analyzed the decoupling types and structural characteristics of GHG emissions and the driving factors of carbon dioxide (CO2) emissions in the Anji from 2006 to 2019, and explored the differences between county-level and provincial-level or city-level results. It was observed that energy-related activities are the main source of GHG emissions in Anji and that economic development is the driving factor behind the increasing CO2 emissions. However, industrial transformation and upgradation coupled with the alternative use of clean energy limit the growth of GHG emissions. This study details the GHG emissions of county during the industrial transformation stage and provides corresponding policy recommendations for county governments.

Buriti (Mauritia flexuosa L.) wastes as potential lignocellulosic feedstock for bioenergy production: Physicochemical properties, thermal behavior, and emission factors

Biomass is one of the most feasible renewable alternatives to fossil fuels. Given this fact, this study aims to demonstrate that buriti fruit wastes (pits and husks) are a promising new feedstock for bioenergy production via thermochemical conversion. To evaluate the suitability of buriti pits and husks for bioenergy generation, their physicochemical properties, performance indices, and pyrolytic behavior were evaluated based on ultimate, proximate, and calorimetry analyses, emission factors, X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM images), energy dispersive spectrometry (EDS), thermal analysis (TG/DTG/DSC curves), and devolatilization index (DI). In addition, the mineral and metal contents were quantified by inductively coupled plasma optical emission spectroscopy (ICP-OES). The buriti wastes exhibited low ash (< 3.2%) and moisture contents (< 13.0%), a high heating value (HHV) of 16.11–20.61 MJ kg−1, negligible nitrogen content (< 1.2%) and sulfur (non-detected), and lower carbon dioxide (1236–1244 g kg−1) and nitrogen oxides (3.32–3.95 g kg−1) emission factors than those of reference fuels. The physicochemical properties of buriti pits and husks were consistent with those of other agroforestry and agroindustrial biomasses. The DI of 1.47 and 5.27 × 10−8 mg min−1 °C−3 (or 0.35 and 1.17 ×10−6% min−1 °C−3) of buriti pits and husks, respectively, revealed the main difference between the thermal performance and suitability of buriti pits and husks for the partial or total replacement of fossil fuels. The buriti wastes displayed exceptional performance under direct combustion, and a comparison of buriti pits and husks highlighted the remarkable suitability of buriti pits for pyrolysis, probably due to the oleaginous nature of the material.

Statistical data-based prediction of carbon dioxide emission factors of China's power generation at carbon peak in 2030

China has promised to reach its peak carbon dioxide emissions by 2030. Power generation emits the most carbon dioxide in China. Fossil fuels are currently still the largest sources of energy for electricity generation in China. As the most commonly used method for estimating emissions from thermal power generation, emission factor data has a statistical lag, which brings inconvenience to carbon emission accounting and the formulation of energy conservation and emission reduction policies. It is of great significance to predict the emission factors of fossil fuel-fired power generation in China. In this paper, the correlations between the data of coal-fired, gas-fired, and oil-fired power generation from 2012 to 2019 and the emission factors of China's power generation and the correlations between the data of fossil fuel-fired power generation from 2006 to 2019 and the emission factors of China's power generation are evaluated using SPSS. On this basis, two models for predicting emission factors of China's power generation from 2020 to 2030 are established and the relevant data are obtained. The calculation results show that when the carbon peak reaches in 2030, the emission factor of China's power generation may be 0.6290 and 0.7363 corresponding to the two prediction models.

Evolution Characteristics and Main Influencing Factors of Carbon Dioxide Emissions in Chinese Cities from 2005 to 2020

Based on the carbon emission database of the China Urban Greenhouse Gas Working Group, this paper analyzed the spatiotemporal evolution characteristics and main influencing factors of urban carbon dioxide emissions in China using ArcGIS spatial analysis and SPSS statistical analysis methods, in order to provide a reference for the formulation of the national “double-carbon” strategy and the construction of low-carbon urbanization. The results showed that (1) the urban carbon dioxide emissions in China exhibit a “point-line-area” spreading spatial grid. Carbon dioxide emissions form a planar emission pattern surrounded by the Beijing–Tianjin–Hebei urban agglomeration, Yangtze River Delta urban agglomeration, and Central Plains urban agglomeration. A high per capita and high-intensity emission belt from Xinjiang to Inner Mongolia has been formed. (2) The proportion of industrial emissions continues to decrease, and the range of high industrial emissions has gradually crossed the “Hu Huan-yong Line”, spreading from eastern China to the whole country. The emissions from transportation, the service industry, and households have become new growth points, and high-value emissions from households have also shown a nationwide spreading trend. (3) The main factors influencing the spatial distribution of carbon dioxide emissions are urbanization, the economy, industry, investment, and household energy consumption.

Investigation of the physical properties and environmental impact of lemongrass biobriquettes

At present, biobriquettes have become an attractive fuel option due to the increasing price of fossil fuels. However, the effective raw materials for biobriquettes require specific physical properties to obtain desired shapes and net calorific value. Therefore, the objective of this study was to explore a raw material with appropriate physical properties for biobriquettes. In this study, lemongrass was used as raw material for biobriquettes which formed by mixing with charcoal and a binder. The basic physical properties of biobriquettes, such as density, moisture content, net calorific value, ash content, volatile matter, and carbon content, were analyzed. Moreover, the net calorific value and the carbon content were determined to find the carbon dioxide (CO2) emission factor. The amount of CO2 emission when using lemongrass biobriquettes substituted for the conventional biomass sources was also determined. The results showed that the density varied depending on the pressure compaction conditions. Besides, the carbon content was successfully determined and reported. The average net calorific value under all compression pressures was reported. The net calorific value of lemongrass biobriquettes was higher than that of firewood and most agricultural wastes, with an average CO2 emission factor of 77,362 kgcarbon/TJ. Based on all the results obtained, it can be concluded that lemongrass biobriquettes produce a lower CO2 emission than firewood, most agricultural wastes, and charcoal; therefore, it is suitable as an alternative biofuel in the future.

Environmental impact assessment of an air-to-water heat pump through the expanded total equivalent warming impact index based on experimental data

The Total Equivalent Warming Impact (TEWI) index is conventionally used to evaluate emissions from heating, ventilation, air conditioning and refrigeration (HVAC&R) systems. However, a more comprehensive indicator, the Expanded Total Equivalent Warming Impact (ETEWI), has been proposed to overcome the limits of TEWI index and to consider additional environmental impacts such as the effects of urban heat island and the natural gas losses along high-pressure pipelines supplying gaseous fuel to natural gas-fired power plants. In this study, TEWI and ETEWI metrics are applied to an air-to-water electric heat pump (EHP) serving a university building in Benevento (Southern Italy). Real data on EHP electricity consumption have been derived from the entire building's electric load and compared to measured data. ETEWI index has been estimated in two scenarios, one considering the impact of refrigerant charge variation on the performance of the EHP and its environmental impact and the other disregarding it. Additionally, carbon dioxide emission factors for electricity production have been forecasted from 2021 to 2033 to determine the indirect term of ETEWI index. Over the lifetime of the EHP, ETEWI increases by 4.8% compared to TEWI when the variation of refrigerant charge level is not considered. However, this percentage growth is equal to 6.5% when the impact of refrigerant charge change is accounted too. In the latter scenario, the indirect term of ETEWI decreases from 22.7 tCO2 in 2015 to 12.0 tCO2 in 2033 when the forecast of the carbon dioxide emission factors for electricity production is taken into account.

Techno-Economic Analysis of Electrochemical Refineries Using Solid Oxide Cells for Oxidative Coupling of Methane

With the shift away from fossil resources, there is a need for alternative pathways to carbon-based commodities such as ethylene. The electrochemical oxidative coupling of methane (OCM) enables the synthesis of higher hydrocarbons from simple organic molecules i.e., methane and has the potential to replace conventional ethylene production in the future. However, current solid oxide OCM cell development is still in an early stage and more comprehensive system-level analyses are needed to better understand operating conditions and economics to guide research and development. For this purpose, process models and new integration strategies for the electrochemical OCM process were developed. The integration of the electrochemical OCM unit into the plant revealed to be challenging based on current solid oxide cell designs and will be discussed as part of this presentation. The performance of the OCM plant is benchmarked against current state-of-the-art ethane steam cracker plants. In this context, key performance metrics are efficiency, direct and indirect carbon dioxide emissions, power consumption, plant cost and cost of ethylene. Of particular interest are aspects of hydrogen co-production and carbon dioxide utilization as well as the impact of carbon dioxide emission factors from the grid, which have shown to be of particular importance for electrochemical processes. Moreover, critical aspects of heat integration will be discussed including fuel pre-heating, carbon deposition and thermal cell management. The analysis will provide new insights into economic cost driving factors and the impact of cell cost, current density, overpotentials and Faraday efficiency upon the cost of ethylene. Based upon this information, performance targets will be recommended that will allow electrochemical OCM to become economically competitive in a free market environment.

The last quarter for sustainable environment in basketball: the carbon footprint of basketball teams in Türkiye and Lithuania

Today, the sports industry is one of the most important sources of concern due to its negative environmental effects. Especially due to the intense competition schedule, teams and fans have to travel constantly. In this context, the aim of this study, which aims to fill the gap in the literature, is to calculate the carbon footprints of the teams in the Turkish and Lithuanian national basketball leagues based on their travels in the 2021–22 season. The research was limited to Turkey and Basketball national basketball league teams. In the study, the travel distances of the teams in both countries during the 2021–22 basketball season were used as a data set. In the study, the values used in the carbon footprint calculation of 2022 by the United Kingdom Government GHG Conversion Factors for Company Reporting and accepted as the IPCC carbon dioxide emission factor were used. While the carbon footprint, which is obtained by multiplying the emission factor directly by the distance covered by the vehicle type, is presented in tons; The average value calculated for each person was calculated in kg. In the sports sector, basketball is one of the most important sources of transportation-related carbon footprint due to its being one of the team sports and its intense competition schedule. According to the results of this research conducted specifically for Turkey and Lithuania, the total carbon footprint calculated for both countries is 53,029 tons. To make an assessment for both countries, in order to reduce travel based on sports; Arranging league calendars to include less travel, dissemination of green and clean energy-using (electric) vehicles, raising the awareness of club managers, developing environmental assessment policies specific to basketball federations, and increasing cooperation through awareness and training activities seem feasible for sustainable environment and basketball goals.

Analysis of carbon emission drivers and multi-scenario projection of carbon peaks in the Yellow River Basin

The Yellow River Basin is a key ecological barrier and commercial zone in China, as well as an essential source of energy, chemicals, raw materials, and fundamental industrial foundation, the achievement of its carbon peaking is of great significance for China’s high-quality development. Based on this, we decomposed the influencing factors of carbon dioxide emissions in the Yellow River Basin using the LMDI method and predicted the carbon peaking in the Yellow River Basin under different scenarios using the STIRPAT model. The results show that (1) the energy intensity effect, economic activity effect and population effect play a positive role in promoting carbon emissions during 2005–2020. The largest effect on carbon emissions is the population size effect, with a contribution rate of 65.6%. (2) The STIRPAT model predicts that the peak of scenarios “M–L”, “M–M” and “M–H” will occur in 2030 at the earliest. The “M–H” scenario is the best model for controlling carbon emissions while economic and social development in the Yellow River Basin. The results of this paper can provide a theoretical basis for the development of a reasonable carbon peak attainment path in the Yellow River Basin and help policy makers to develop a corresponding high-quality development path.

Dynamic Prediction and Driving Factors of Carbon Emission in Beijing, China, under Carbon Neutrality Targets

China has made remarkable achievements in reducing carbon emissions in recent years. However, there is still much reduction room before achieving carbon neutrality. In Beijing, the capital of China, it is a strategic choice to respond to global climate change by promoting green and low-carbon development. This paper calculates the carbon dioxide emissions of key industries in Beijing and analyzes the temporal evolution trend of carbon emissions. Carbon dioxide emissions in Beijing before 2030 are predicted based on the grey prediction GM (1,1) and BP neural network model. The effects of factors of carbon dioxide emissions are discussed using the threshold regression model under different economic conditions. The results show that energy consumption intensity, GDP per capita, and the ownership of civil cars have a positive impact on carbon dioxide emissions, while the number of permanent residents and urban green space areas have a negative impact on carbon dioxide emissions. These findings of carbon emission prediction and influencing factors contribute to carbon reduction path design. Related policy implications on carbon emission reduction are put forward from the aspects of promoting industrial upgrading, accelerating the construction of advanced economic structures, optimizing transportation structures, and strengthening green building development.

Spatio-temporal Evolution and Driving Factors of Carbon Dioxide Emissions from Energy Consumption in the Yellow River Basin

Based on the data of 57 cities in the Yellow River Basin (YRB) during 2006-2019, this study analyzes its spatio-temporal evolution of carbon dioxide (CO2) emissions from energy consumption, and decomposes its CO2 emissions changes into five factors. The results indicate that: (1) The CO2 emissions in the Yellow River Basin has increased but did not reach the peak from 2006 to 2019, which showed a spatial distribution pattern of “high in the east and low in the west”, with Baotou city, Taiyuan city, Xi’an City, Zhengzhou City and Zibo city forming several high CO2 emission centers of different sizes and spreading to the periphery; (2) The CO2 emission center of gravity in the Yellow River Basin shifted to the southeast as a whole. Ningxia Hui Autonomous Region and Lanzhou city were the main engine provinces and cities to promote its shift. (3) The driving factors of CO2 emissions growth are economic development level, population size and energy efficiency, while industrial structure and clean energy utilization level (CO2 emission coefficient) factors restrained the growth of CO2 emissions in the Yellow River Basin.

The spillover effects of uncertainty and globalization on environmental quality in India: Evidence from combined cointegration test and augmented ARDL model

Environmental quality and climate change have become hot topics among academics in all scientific fields in recent decades due to their impact on human health and economic development. Hence, this paper investigates the key factors of carbon dioxide emissions in India from 1970–2020 through the Bayer-Hanck test and Augmented ARDL framework on an augmented STIRPAT model, introducing uncertainty and globalization. We employ a set of unit-root tests and a combination of cointegration techniques (DOLS and FMOLS), which permit us to estimate the long-run and short-run relationships. Empirical findings confirmed that the series is I(1) series and there is the existence of a long-run relationship between our variables using three cointegration tests, meaning that the variables have the same behavior in the long run term. The findings revealed that India has an inverse U shape of the Environmental Kuznets curve (EKC) due to the positive association between GDP per capita and CO2 emissions until reaching a threshold, after which the link becomes inverse due to the negative impact of GDP square on CO2 emissions. Furthermore, the findings demonstrated a positive influence of uncertainty and a negative impact of globalization on long-term environmental degradation. Besides, energy consumption and population density are positively associated with CO2 emissions in the long and short run. We advocate for policies that promote more trade openness by entering new markets and cooperating with new trading partners.

Research on the Impact of Industrial enterprises Participation in the Peak shaving Auxiliary Service Market on Carbon Dioxide Emission

With the advancement of the construction of new power systems, industrial enterprises participation in the peak shaving auxiliary service market is increasing. This paper proposes a model to estimate the impact of industrial enterprises participation in the peak shaving auxiliary service market on carbon dioxide emission, and designs a blockchain-based platform architecture to ensure the credibility of transaction of the aggregation trade. The simulation results indicate that most enterprises in the magnesia park use electricity during normal and valley hours. Transferring electricity consumption from peak hours to flat and low periods will reduce carbon dioxide emissions. Moreover, a blockchain-based aggregation platform architecture is an attractive measure too. Besides, the carbon dioxide emission factor of the power grid and the production proportion of primary energy show an opposite trend.