scholarly journals Evaluating the Causal Relations between the Kaya Identity Index and ODIAC-Based Fossil Fuel CO2 Flux

Energies ◽  
2020 ◽  
Vol 13 (22) ◽  
pp. 6009
Author(s):  
YoungSeok Hwang ◽  
Jung-Sup Um ◽  
JunHwa Hwang ◽  
Stephan Schlüter
Keyword(s):  
Co2 Emissions ◽  
Fossil Fuel ◽  
Co2 Flux ◽  
Causal Relationships ◽  
Mediating Effects ◽  
Multiple Mediation ◽  
Open Source Data ◽  
Carbon Dioxide Co2 ◽  
The Individual ◽  
Kaya Identity

The Kaya identity is a powerful index displaying the influence of individual carbon dioxide (CO2) sources on CO2 emissions. The sources are disaggregated into representative factors such as population, gross domestic product (GDP) per capita, energy intensity of the GDP, and carbon footprint of energy. However, the Kaya identity has limitations as it is merely an accounting equation and does not allow for an examination of the hidden causalities among the factors. Analyzing the causal relationships between the individual Kaya identity factors and their respective subcomponents is necessary to identify the real and relevant drivers of CO2 emissions. In this study we evaluated these causal relationships by conducting a parallel multiple mediation analysis, whereby we used the fossil fuel CO2 flux based on the Open-Source Data Inventory of Anthropogenic CO2 emissions (ODIAC). We found out that the indirect effects from the decomposed variables on the CO2 flux are significant. However, the Kaya identity factors show neither strong nor even significant mediating effects. This demonstrates that the influence individual Kaya identity factors have on CO2 directly emitted to the atmosphere is not primarily due to changes in their input factors, namely the decomposed variables.

scholarly journals Evaluating the Mutual Relationship between IPAT/Kaya Identity Index and ODIAC-Based GOSAT Fossil-Fuel CO2 Flux: Potential and Constraints in Utilizing Decomposed Variables

2020 ◽  
Vol 17 (16) ◽  
pp. 5976
Author(s):  
YoungSeok Hwang ◽  
Jung-Sup Um ◽  
Stephan Schlüter
Keyword(s):  
Cluster Analysis ◽  
Co2 Emissions ◽  
Fossil Fuel ◽  
Driving Forces ◽  
Explanatory Power ◽  
Co2 Flux ◽  
Mutual Relationship ◽  
Explanatory Variables ◽  
Relationship Of ◽  
Kaya Identity

The IPAT/Kaya identity is the most popular index used to analyze the driving forces of individual factors on CO2 emissions. It represents the CO2 emissions as a product of factors, such as the population, gross domestic product (GDP) per capita, energy intensity of the GDP, and carbon footprint of energy. In this study, we evaluated the mutual relationship of the factors of the IPAT/Kaya identity and their decomposed variables with the fossil-fuel CO2 flux, as measured by the Greenhouse Gases Observing Satellite (GOSAT). We built two regression models to explain this flux; one using the IPAT/Kaya identity factors as the explanatory variables and the other one using their decomposed factors. The factors of the IPAT/Kaya identity have less explanatory power than their decomposed variables and comparably low correlation with the fossil-fuel CO2 flux. However, the model using the decomposed variables shows significant multicollinearity. We performed a multivariate cluster analysis for further investigating the benefits of using the decomposed variables instead of the original factors. The results of the cluster analysis showed that except for the M factor, the IPAT/Kaya identity factors are inadequate for explaining the variations in the fossil-fuel CO2 flux, whereas the decomposed variables produce reasonable clusters that can help identify the relevant drivers of this flux.

scholarly journals ARE THE PARIS AGREEMENT EFFORTS EQUALLY SHARED? GDP AND CO2 PRODUCTION REGULARITIES

2019 ◽  
Vol 78 (310) ◽  
pp. 103
Author(s):  
Adalmir Antonio Marquetti ◽  
Gabriel Mendoza Pichardo ◽  
Guilherme De Oliveira
Keyword(s):  
Co2 Emissions ◽  
Capital Accumulation ◽  
Developed Countries ◽  
Paris Agreement ◽  
Co2 Production ◽  
Unequal Distribution ◽  
Economic Framework ◽  
Definition Of ◽  
The Individual ◽  
Kaya Identity

<p><strong>ABSTRACT</strong></p><p><strong></strong>This study investigates regularities in the production of GDP and CO2 emissions for 84 countries between 1980-2014. The empirical strategy is derived from an ecological-economic framework in which both outputs are produced employing capital, energy and labor. Moreover, we propose an expanded version of the Kaya identity, which creates a link between the growth rate of CO2 emissions and capital accumulation to evaluate the distribution of abatement efforts under the Paris Agreement. By using a new dataset, we found evidence of relative decoupling in developing countries and absolute decoupling in some developed countries. Our findings show that the individual voluntary definition of the emission targets under the Agreement resulted in an unequal distribution of the abatement efforts among developing and developed countries. In the absence of higher energy or environment-saving technical changes, the required reductions in capital accumulation are sharper for developing than developed countries.</p><p> </p><p>¿SE COMPARTEN LOS ESFUERZOS DEL ACUERDO DE PARÍS IGUALMENTE? <br />REGULARIDADES DE PRODUCCIÓN DEL PIB Y CO2<br /><strong></strong></p><p><strong>RESUMEN</strong><br />Este trabajo investiga las regularidades en la producción del PIB y las emisiones de CO2 en 84 países entre 1980 y 2014. La estrategia empírica deriva de un marco ecológico-económico en el cual los dos bienes se producen utilizando capital, energía y trabajo. Proponemos una versión expandida de la identidad de Kaya que crea un vínculo entre la tasa de crecimiento de las emisiones de CO2 y la acumulación de capital para evaluar la distribución de los esfuerzos de abatimiento del Acuerdo de París. Mediante el uso de una nueva base de datos, encontramos un desacoplamiento relativo en los países en desarrollo y un desacoplamiento absoluto en algunos países desarrollados. Nuestros hallazgos muestran que la definición individual voluntaria de las metas de emisiones del Acuerdo resulta en una distribución desigual de los esfuerzos de abatimiento entre los países en desarrollo y desarrollados. En ausencia de un mayor cambio técnico ahorrador de energía o del ambiente, las reducciones requeridas en la acumulación de capital son más agudas para los países en desarrollo que para los desarrollados.</p>

scholarly journals Joint inverse estimation of fossil fuel and biogenic CO2 fluxes in an urban environment: An observing system simulation experiment to assess the impact of multiple uncertainties

Elem Sci Anth ◽  
2018 ◽  
Vol 6 ◽  
Author(s):  
Kai Wu ◽  
Thomas Lauvaux ◽  
Kenneth J. Davis ◽  
Aijun Deng ◽  
Israel Lopez Coto ◽  
...  
Keyword(s):  
High Resolution ◽  
Urban Environment ◽  
Co2 Emissions ◽  
Fossil Fuel ◽  
Atmospheric Transport ◽  
System Simulation ◽  
Co2 Flux ◽  
Point Sources ◽  
Co2 Fluxes ◽  
The Impact

The Indianapolis Flux Experiment aims to utilize a variety of atmospheric measurements and a high-resolution inversion system to estimate the temporal and spatial variation of anthropogenic greenhouse gas emissions from an urban environment. We present a Bayesian inversion system solving for fossil fuel and biogenic CO2 fluxes over the city of Indianapolis, IN. Both components were described at 1 km resolution to represent point sources and fine-scale structures such as highways in the a priori fluxes. With a series of Observing System Simulation Experiments, we evaluate the sensitivity of inverse flux estimates to various measurement deployment strategies and errors. We also test the impacts of flux error structures, biogenic CO2 fluxes and atmospheric transport errors on estimating fossil fuel CO2 emissions and their uncertainties. The results indicate that high-accuracy and high-precision measurements produce significant improvement in fossil fuel CO2 flux estimates. Systematic measurement errors of 1 ppm produce significantly biased inverse solutions, degrading the accuracy of retrieved emissions by about 1 µmol m–2 s–1 compared to the spatially averaged anthropogenic CO2 emissions of 5 µmol m–2 s–1. The presence of biogenic CO2 fluxes (similar magnitude to the anthropogenic fluxes) limits our ability to correct for random and systematic emission errors. However, assimilating continuous fossil fuel CO2 measurements with 1 ppm random error in addition to total CO2 measurements can partially compensate for the interference from biogenic CO2 fluxes. Moreover, systematic and random flux errors can be further reduced by reducing model-data mismatch errors caused by atmospheric transport uncertainty. Finally, the precision of the inverse flux estimate is highly sensitive to the correlation length scale in the prior emission errors. This work suggests that improved fossil fuel CO2 measurement technology, and better understanding of both prior flux and atmospheric transport errors are essential to improve the accuracy and precision of high-resolution urban CO2 flux estimates.

Soil CO2 flux affected by Aporrectodea caliginosa earthworms

2010 ◽  
Vol 5 (3) ◽  
pp. 364-370 ◽  
Author(s):  
Miloslav Šimek ◽  
Václav Pižl
Keyword(s):  
Co2 Emissions ◽  
Co2 Flux ◽  
Co2 Concentration ◽  
Experimental Period ◽  
Aporrectodea Caliginosa ◽  
Soil Co2 Flux ◽  
Soil Air ◽  
Soil Microbial ◽  
Carbon Dioxide Co2 ◽  
Soil Accumulation

AbstractThe effects of Aporrectodea caliginosa earthworms on both carbon dioxide (CO2) accumulation in and emissions from soil, as well as the simultaneous impact of earthworms on soil microbiological properties were investigated in a microcosm experiment carried out over 5.5 months. Concentration of CO2 in soil air was greater at a depth of 15 cm when compared with a depth of 5 cm, but varied during the season both in control and earthworm-inhabited chambers. Peaks of CO2 concentrations at both depths occurred in both treatments during August, approximately 80 days after the experiment started. Generally, the presence of earthworms increased the CO2 concentration at 15-cm depth. Larger CO2 emissions were consistently recorded in conjunction with higher amounts of CO2 in soil air when chambers were inhabited by earthworms. The total CO2 emissions during the experimental period covering 161 days were estimated at 118 g CO2-C m−2 and 99 g CO2-C m−2 from chambers with and without earthworms respectively. Moreover, the presence of earthworms increased microbial biomass in the centre and at the bottom of chambers, and enhanced both dehydrogenase activity and nitrifying enzyme activity in the soils. We suggest that the effect of earthworms on both the enhanced soil accumulation of CO2 as well as emissions of CO2 was mostly indirect, due to the impacts of earthworms on soil microbial community.

scholarly journals Decomposition of Carbon Dioxide (CO2) Emissions in ASEAN Based on Kaya Identity

2021 ◽  
Vol 4 (2) ◽  
pp. 101-114
Author(s):  
Vivid Amalia Khusna ◽  
Deni Kusumawardani
Keyword(s):  
Co2 Emissions ◽  
Economic Activity ◽  
Energy Intensity ◽  
Carbon Intensity ◽  
Middle Income ◽  
Middle Income Countries ◽  
Brunei Darussalam ◽  
Asean Countries ◽  
Carbon Dioxide Co2 ◽  
Kaya Identity

ASEAN is a region with high carbon dioxide (CO2) emissions, accompanied by an increase in population, gross domestic product (GDP) and energy consumption. Population, GDP, and energy consumption can be linked to CO2 emissions through an identity equation called the Rich Identity. This research is based on Kaya identity to describe CO2 emissions to calculate the impact of population, economic activity, energy intensity and carbon intensity on CO2 emissions in ASEAN and 8 ASEAN countries (i.e., Indonesia, Malaysia, Singapore, Thailand, Philippines, Vietnam, Myanmar and Brunei Darussalam) from 1990 to 2017. The method used is the Logarithmic Mean Division Index (LMDI). The data used are from the International Energy Agency (IEA) and the World Bank. Four effects measured and main findings showed that population, economic activity and carbon intensity factor increased by 293.02 MtCO2, 790.0 MtCO2, and 195.51 MtCO2, respectively. Meanwhile, energy intensity effect made ASEAN's CO2 emissions decrease by 283.13 MtCO2. Regarding contributions to the increase in CO2 emissions in all ASEAN countries, the population effect increases CO2 emissions in all countries in ASEAN and the economic activity effect is also the same, except in Brunei Darussalam which makes CO2 emissions in this country decreased by 1.07 MtCO2. Meanwhile, the effects of energy and carbon intensity are different. The effect of energy intensity causes CO2 emissions in lower-middle income countries to decrease, while in upper-middle and high-income countries, it increases carbon emissions. In contrast to the effect of carbon intensity, that actually makes CO2 emissions increase in lower-middle income countries and reduces carbon emissions in upper-middle and high-income countries.

scholarly journals Observing carbon dioxide emissions over China's cities and industrial areas with the Orbiting Carbon Observatory-2

2020 ◽  
Vol 20 (14) ◽  
pp. 8501-8510 ◽  
Author(s):  
Bo Zheng ◽  
Frédéric Chevallier ◽  
Philippe Ciais ◽  
Grégoire Broquet ◽  
Yilong Wang ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Co2 Emissions ◽  
Carbon Dioxide Emissions ◽  
Power Plants ◽  
Global Climate ◽  
Large Cities ◽  
Independent Evaluation ◽  
Paris Climate Agreement ◽  
Carbon Dioxide Co2 ◽  
The Individual

Abstract. In order to track progress towards the global climate targets, the parties that signed the Paris Climate Agreement will regularly report their anthropogenic carbon dioxide (CO2) emissions based on energy statistics and CO2 emission factors. Independent evaluation of this self-reporting system is a fast-growing research topic. Here, we study the value of satellite observations of the column CO2 concentrations to estimate CO2 anthropogenic emissions with 5 years of the Orbiting Carbon Observatory-2 (OCO-2) retrievals over and around China. With the detailed information of emission source locations and the local wind, we successfully observe CO2 plumes from 46 cities and industrial regions over China and quantify their CO2 emissions from the OCO-2 observations, which add up to a total of 1.3 Gt CO2 yr−1 that accounts for approximately 13 % of mainland China's annual emissions. The number of cities whose emissions are constrained by OCO-2 here is 3 to 10 times larger than in previous studies that only focused on large cities and power plants in different locations around the world. Our satellite-based emission estimates are broadly consistent with the independent values from China's detailed emission inventory MEIC but are more different from those of two widely used global gridded emission datasets (i.e., EDGAR and ODIAC), especially for the emission estimates for the individual cities. These results demonstrate some skill in the satellite-based emission quantification for isolated source clusters with the OCO-2, despite the sparse sampling of this instrument not designed for this purpose. This skill can be improved by future satellite missions that will have a denser spatial sampling of surface emitting areas, which will come soon in the early 2020s.

Utilization of Ocean Water for CO2 Capture via Amine Scrubbing

2020 ◽  
Author(s):  
Abhishek P. Ratanpara ◽  
Alexander Shaw ◽  
Sanat Deshpande ◽  
Myeongsub Kim
Keyword(s):  
Co2 Emissions ◽  
Co2 Capture ◽  
Carbon Capture ◽  
Power Plants ◽  
Fossil Fuel ◽  
Building Blocks ◽  
Ocean Water ◽  
Marine Animals ◽  
Co2 Capturing ◽  
Carbon Dioxide Co2

Abstract As the consumption of fossil fuel resources has continuously increased to meet global fuel demands for power generation, atmospheric emissions of greenhouse gases, particularly carbon dioxide (CO2), have rapidly increased over the last century. Increased CO2 emissions have caused serious international concerns about global warming, sea-level rise, and ocean acidification. Although post-combustion carbon capture technology that separates CO2 from flue gas in fossil fuel-fired power plants has contributed to significant migration of atmospheric CO2 emissions, this approach generates considerable amounts of toxic wastewater containing a heavy chemical which is difficult to treat, raises concerns about acute corrosion of metal structures in the facility, and waste of significant amounts of freshwater. In this research, we are particularly interested in reducing the use of freshwater for CO2 capture and generating carbonate minerals, byproducts of CO2 with calcium (Ca2+) or magnesium ions (Mg2+) in ocean water which are useful building blocks for marine animals, such as seashells and coral reefs. In our experimental approach, we attempted to use ocean water with different monoethanolamine (MEA) concentrations and compared the CO2 capturing efficiency with that in DI water. We found that there are considerable benefits of the use of ocean water in CO2 dissolution, showing that a replacement of freshwater with ocean water would be a possible option. In the future, we will further enhance the dissolution of CO2 in ocean water by using nanoparticle catalysts without using MEA, which will be an environmentally friendly method for CO2 capture.

Exploratory Analysis of Fossil-Fuel CO2 Emissions Time Series Using Independent Component Analysis

2011 ◽  
pp. 1520-1538
Author(s):  
Sargam Parmar ◽  
Bhuvan Unhelkar
Keyword(s):  
Time Series ◽  
Independent Component Analysis ◽  
Co2 Emissions ◽  
Fossil Fuel ◽  
Multivariate Time Series ◽  
Component Analysis ◽  
Linear Mapping ◽  
Independent Component ◽  
Carbon Dioxide Co2 ◽  
Ica Algorithm

Carbon dioxide (CO2) is one of the most important gases in the atmosphere, and is necessary for sustaining life on Earth. However, it is also a major greenhouse gas out of the six that contribute to global warming and climate change. During the last decade technologists, economists and sociologists are taking substantial interest in studying the impact of greenhouse phenomenon. Scientists are trying to find solutions to reduce CO2 emissions by changes in structure of energy production and consumption. Every attempt is being made to use new models and methods to estimate measure and monitor greenhouse gases in the future. Independent Component Analysis (ICA) is a method for automatically identifying a set of underlying factors in a given data set. This chapter describes the use of the ICA algorithm in Environmentally Intelligent (EI) applications. EI applications have a wide ranging responsibilities including collection, analysis and reporting of environmental data related to the organization. ICA algorithm opens up the opportunity to improve the quality of data being analyzed by these EI applications. ICA finds application in several fields of interest and it is a tempting alternative to try ICA on multivariate time series such as a CO2 emission from fossil fuel for the period 1950 to 2006. This chapter describes the linear mapping of the observed multivariate time series into a new space of statistically independent components (ICs) that might reveal driving mechanisms for CO2 emissions that may otherwise remain hidden.

scholarly journals Observing carbon dioxide emissions over China's cities with the Orbiting Carbon Observatory-2

2020 ◽  
Author(s):  
Bo Zheng ◽  
Frederic Chevallier ◽  
Philippe Ciais ◽  
Gregoire Broquet ◽  
Yilong Wang ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Co2 Emissions ◽  
Carbon Dioxide Emissions ◽  
Power Plants ◽  
Global Climate ◽  
Large Cities ◽  
Independent Evaluation ◽  
Paris Climate Agreement ◽  
Carbon Dioxide Co2 ◽  
The Individual

Abstract. In order to track progress towards the global climate targets, the parties that signed the Paris Climate Agreement will regularly report their anthropogenic carbon dioxide (CO2) emissions based on energy statistics and CO2 emission factors. Independent evaluation of this self-reporting system is a fast-growing research topic. Here, we study the value of satellite observations of the column CO2 concentrations to estimate CO2 anthropogenic emissions with five years of the Orbiting Carbon Observatory-2 (OCO-2) retrievals over and around China. With the detailed information of emission source locations and the local wind, we successfully observe CO2 plumes from 60 cities and industrial regions over China and quantify their CO2 emissions from the OCO-2 observations, which add up to a total of 1.6 Gt CO2 yr−1 that account for 17 % of mainland China's annual emissions. The number of cities whose emissions are constrained by OCO-2 here is three to ten times larger than previous studies that only focused on large cities and power plants in different locations around the world. Our satellite-based emission estimates are broadly consistent with the independent values from the detailed China's emission inventory MEIC, but are more different from those of two widely used global gridded emission datasets (i.e., EDGAR and ODIAC), especially for the emission estimates for the individual cities. These results demonstrate some skill in the satellite-based emission quantification for isolated source clusters with the OCO-2, despite the sparse sampling of this instrument not designed for this purpose. This skill can be improved by future satellite missions that will have a denser spatial sampling of surface emitting areas, which will come soon in the early 2020s.

Sign in / Sign up

Export Citation Format

Share Document