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 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 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.

scholarly journals Energy foresight: Exploration of CO2 reduction policy scenario for Ecuador during 2016–2030

Energetika ◽  
2019 ◽  
Vol 65 (1) ◽  
Author(s):  
Gabriela Araujo ◽  
Andrés Robalino-López ◽  
Natalia Tapia
Keyword(s):  
Co2 Emissions ◽  
Energy Demand ◽  
Fossil Fuels ◽  
Driving Forces ◽  
Early Stage ◽  
Life Quality ◽  
Industrial Sector ◽  
Energy Matrix ◽  
Alternative Scenario ◽  
Kaya Identity

The energy sector is an important factor that influences life quality and economic prosperity. Differences in infrastructure, technology and even in culture of each country make it imperative to include their own characteristics into energy analyses, making it necessary to identify the different types of sources of CO2 emissions and their magnitudes. The aim of this paper is to present a foresight analysis of the productive and energy matrices dynamics in Ecuador for the period 2016–2030 and to propose public policy that contributes to sustainable development. In a first stage, the research has an explanatory character, referring to construction of a model, which uses an extended variation of the Kaya Identity where the volume of CO2 emissions may be examined quantifying contributions of productive sectors activity, sectorial energy intensity, energy matrix, and CO2 emission features. Subsequently, the research acquires a predictive-experimental nature, using exploratory scenarios. That allows linking historic and present events with hypothetical futures. In consequence, driving forces of the scenario can be explained and analysed using quantitative modelling based on the Kaya Identity and qualitative narratives. Within this study two scenarios were built. The Business as Usual scenario, without modifying the structure of productive and energy matrices, and the Alternative scenario that seeks to reduce the consumption of oil derivatives in land transport, which consumes 50% of the country’s energy demand. The Alternative scenario, which promotes the use of biofuels, projects to reduce the CO2 emissions from 45.58 to 43.41 Mt of CO2 equivalent for 2030. The policy on biofuels in Ecuador is at an early stage. So, biofuels offer important opportunities: i) diversification of the energy matrix, ii) contribution to energy security, iii) promotion of the growth of the industrial sector, and iv) substitution of fossil fuels and mitigation of the greenhouse gas effects.

scholarly journals Determinants of Energy-Based CO2 Emissions in Ethiopia: A Decomposition Analysis from 1990 to 2017

2020 ◽  
Vol 12 (10) ◽  
pp. 4175 ◽  
Author(s):  
Gideon Nkam Taka ◽  
Ta Thi Huong ◽  
Izhar Hussain Shah ◽  
Hung-Suck Park
Keyword(s):  
Co2 Emissions ◽  
Emission Intensity ◽  
Energy Intensity ◽  
Fossil Fuel ◽  
Driving Forces ◽  
Economic Effect ◽  
Decomposition Analysis ◽  
Rapid Urbanization ◽  
Intensity Effect ◽  
Decomposition Approach

Ethiopia, among the fastest growing economies worldwide, is witnessing rapid urbanization and industrialization that is fueled by greater energy consumption and high levels of CO2 emissions. Currently, Ethiopia is the third largest CO2 emitter in East Africa, yet no comprehensive study has characterized the major drivers of economy-wide CO2 emissions. This paper examines the energy-related CO2 emissions in Ethiopia, and their driving forces between 1990 and 2017 using Kaya identity combined with Logarithmic Mean Divisia Index (LMDI) decomposition approach. Main findings reveal that energy-based CO2 emissions have been strongly driven by the economic effect (52%), population effect (43%), and fossil fuel mix effect (40%) while the role of emission intensity effect (14%) was less pronounced during the study period. At the same time, energy intensity improvements have slowed down the growth of CO2 emissions by 49% indicating significant progress towards reduced energy per unit of gross domestic product (GDP) during 1990-2017. Nonetheless, for Ethiopia to achieve its 2030 targets of low-carbon economy, further improvements through reduced emission intensity (in the industrial sector) and fossil fuel share (in the national energy mix) are recommended. Energy intensity could be further improved by technological innovation and promotion of energy-frugal industries.

scholarly journals Global and regional drivers of power plant CO2 emissions over the last three decades

2022 ◽  
Author(s):  
Xinying Qin ◽  
Dan Tong ◽  
Fei Liu ◽  
Ruili Wu ◽  
Bo Zheng ◽  
...  
Keyword(s):  
United States ◽  
Power Plant ◽  
Co2 Emissions ◽  
Power Plants ◽  
Fossil Fuel ◽  
Driving Forces ◽  
The United States ◽  
The Past ◽  
Power Plant Emissions ◽  
Plant Emissions

The past three decades have witnessed the dramatic expansion of global biomass- and fossil fuel-fired power plants, but the tremendously diverse power infrastructure shapes different spatial and temporal CO2 emission characteristics. Here, by combining Global Power plant Emissions Database (GPED v1.1) constructed in this study and the previously developed China coal-fired power Plant Emissions Database (CPED), we analyzed global and regional changes in generating capacities, age structure, and CO2 emissions by fuel type and unit size, and further identified the major driving forces of these global and regional structure and emission trends over the past 30 years. Accompanying the growth of fossil fuel- and biomass-burning installed capacity from 1,774 GW in 1990 to 4,139 GW in 2019 (a 133.3% increase), global CO2 emissions from the power sector relatively increased from 7.5 Gt to 13.9 Gt (an 85.3% increase) during the same period. However, diverse developments and transformations of regional power units in fuel types and structure characterized various regional trends of CO2 emissions. For example, in the United States and Europe, CO2 emissions from power plants peaked before 2005, driven by the utilization of advanced electricity technologies and the switches from coal to gas fuel at the early stage. It is estimated the share of identified low-efficiency coal power capacity decreased to 4.3% in the United States and 0.6% in Europe with respectively 2.1% and 13.2% thermal efficiency improvements from 1990-2019. In contrast, CO2 emissions in China, India, and the rest of world are still steadily increasing because the growing demand for electricity is mainly met by developing carbon-intensive but less effective coal power capacity. The index decomposition analysis (IDA) to identify the multi-stage driving forces on the trends of CO2 emissions further suggests different global and regional characteristics. Globally, the growth of demand mainly drives the increase of CO2 emissions for all stages (i.e. 1990-2000, 2000-2010 and 2010-2019). Regional results support the critical roles of thermal efficiency improvement (accounting for 20% of the decrease in CO2 emissions) and fossil fuel mix (61%) in preventing CO2 emission increases in the developed regions (e.g., the United States and Europe). The decrease of fossil fuel share gradually demonstrates its importance in carrying the positive effects on curbing emissions in the most of regions, including the developing economics (i.e. China and India) after 2010 (accounting for 46% of the decrease in CO2 emissions). Our results highlight the contributions of different driving forces to emissions have significantly changed over the past 30 years, and this comprehensive analysis indicates that the structure optimization and transformations of power plants is paramount importance to curb or further reduce CO2 emissions from the power sector in the future.

scholarly journals Acquiring and Geo-Visualizing Aviation Carbon Footprint among Urban Agglomerations in China

2019 ◽  
Vol 11 (17) ◽  
pp. 4515 ◽  
Author(s):  
Chuntao Wu ◽  
Maozhu Liao ◽  
Chengliang Liu
Keyword(s):  
Co2 Emissions ◽  
Driving Forces ◽  
Current Knowledge ◽  
Value Added ◽  
Chord Diagram ◽  
Carbon Footprints ◽  
Urban Agglomerations ◽  
Factor Va ◽  
Annual Total ◽  
Kaya Identity

This paper had two main purposes. One was to estimate annual total aviation CO2 emissions from/among all key urban agglomerations (UAs) in China and its changes patterns from 2007 to 2014. The second one was to visualize the aviation carbon footprints among the UAs by using a chord diagram plot. This study also used Kaya identity to decompose the contribution of potential driving forces behind the aviation CO2 emissions using Kaya identity. Especially, it decomposed factor CO2/gross domestic product (GDP), which is wildly used in Kaya identity analysis, into factor CO2/value-added (VA) and factor VA/GDP. Here, VA represents the tourism value added of the corresponding flights. The main results were: (1) The UAs developed a much bigger and stronger carbon network among themselves. (2) There was also an expanding of the flows to less densely populated or less developed UAs. However, the regional disparity increased significantly. (3) Compared with the driving factor of population, the GDP per capita impacted the emission amount more significantly. Our contribution had two folds. First, it advances current knowledge by fulfilling the research gap between transport emissions and UA relationship. Second, it provides a new approach to visualizing the aviation carbon footprints as well as the relationships among UAs.

scholarly journals Analyzing the Driving Forces behind CO2 Emissions in Energy-Resource-Poor and Fossil-Fuel-Centered Economies: Case Studies from Taiwan, Japan, and South Korea

Energies ◽  
2021 ◽  
Vol 14 (17) ◽  
pp. 5351
Author(s):  
Yun-Hsun Huang ◽  
Jung-Hua Wu ◽  
Hao-Syuan Huang
Keyword(s):  
Energy Efficiency ◽  
South Korea ◽  
Co2 Emissions ◽  
Fossil Fuel ◽  
Driving Forces ◽  
Energy Resource ◽  
Input Output ◽  
Technological Advances ◽  
Resource Poor ◽  
Factor Substitution

Based on the strong similarities between energy-resource-poor and fossil-fuel-centered economies (e.g., Taiwan, Japan, and South Korea) in terms of economy, culture, and energy usage characteristics, they should be analyzed collectively. This study adopted two-tier input-output structural decomposition analysis to identify the driving forces behind CO2 emissions to these countries to the formulation of effective environmental policy. Data from the World Input-Output Database was used to decompose relative changes in CO2 emissions into a range of technological advances, factor substitution, and final demand effects. Technological advances in energy (direct) contributed to a 77% reduction in Taiwan and a 34% reduction in South Korea. This is a clear indication that improving energy efficiency via technological advances should be a priority. In Japan in particular, there was a 22% reduction in CO2 emissions attributable to technological advances in materials; hence, it is recommended that Taiwan and South Korea work to extensively develop eco-industrial parks to create industry clusters to promote resource/energy efficiency and reductions in CO2 emissions. Decomposition results based on factor substitution revealed that a variety of strategies will be required, such as switching to fuels that are less carbon intensive, promoting the adoption of renewable energies, and implementing clean-coal technologies.

scholarly journals Correlating elements content in mosses collected in 2015 across Germany with spatially associated characteristics of sampling sites and their surroundings

2019 ◽  
Vol 31 (1) ◽  
Author(s):  
Stefan Nickel ◽  
Winfried Schröder
Keyword(s):  
Land Use ◽  
Population Density ◽  
Atmospheric Deposition ◽  
Vegetation Structure ◽  
Urban Areas ◽  
Explanatory Power ◽  
Spatial Density ◽  
Area Index ◽  
Explanatory Variables ◽  
Tree Layer

Abstract Background The aim of the study was a statistical evaluation of the statistical relevance of potentially explanatory variables (atmospheric deposition, meteorology, geology, soil, topography, sampling, vegetation structure, land-use density, population density, potential emission sources) correlated with the content of 12 heavy metals and nitrogen in mosses collected from 400 sites across Germany in 2015. Beyond correlation analysis, regression analysis was performed using two methods: random forest regression and multiple linear regression in connection with commonality analysis. Results The strongest predictor for the content of Cd, Cu, Ni, Pb, Zn and N in mosses was the sampled species. In 2015, the atmospheric deposition showed a lower predictive power compared to earlier campaigns. The mean precipitation (2013–2015) is a significant factor influencing the content of Cd, Pb and Zn in moss samples. Altitude (Cu, Hg and Ni) and slope (Cd) are the strongest topographical predictors. With regard to 14 vegetation structure measures studied, the distance to adjacent tree stands is the strongest predictor (Cd, Cu, Hg, Zn, N), followed by the tree layer height (Cd, Hg, Pb, N), the leaf area index (Cd, N, Zn), and finally the coverage of the tree layer (Ni, Cd, Hg). For forests, the spatial density in radii 100–300 km predominates as significant predictors for Cu, Hg, Ni and N. For the urban areas, there are element-specific different radii between 25 and 300 km (Cd, Cu, Ni, Pb, N) and for agricultural areas usually radii between 50 and 300 km, in which the respective land use is correlated with the element contents. The population density in the 50 and 100 km radius is a variable with high explanatory power for all elements except Hg and N. Conclusions For Europe-wide analyses, the population density and the proportion of different land-use classes up to 300 km around the moss sampling sites are recommended.

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