Exploring Explainable Range of In-situ Portable CO2 Sensor Signatures for Carbon Stock Estimated in Forestry Carbon Project

Hwang, Young Hwan; Lee, Jung Joo; Park, Seong-Il; Um, Jung‐Sup

doi:10.18494/sam.2019.2522

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…Besides, accuracy, transparency, and uncertainty of inventory CO 2 emission data vary among countries owing to the differences in proficiency and level of development of statistics [5]. Thus, documented inventory CO 2 emissions sometimes show large discrepancy with the actual CO 2 directly emitted to the atmosphere [6,7].…”

Section: Introductionmentioning

confidence: 99%

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

Hwang

Schlüter

2020

IJERPH

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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.

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Section: Introductionmentioning

confidence: 99%

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

Hwang

Schlüter

2020

IJERPH

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“…It is in the Dalseo administrative district of the metropolitan city Daegu, the third most populous city in South Korea. Compared with other Korean cities, Daegu has low rainfall and abundant solar radiation, (20)(21)(22) making it suitable for solar power generation. The experimental target, Daegu Educational Training Institute (DETI), is in the Gamsam-dong residential area.…”

Section: Study Areamentioning

confidence: 99%

Comparative Evaluation of Tracking Capability of Spatial Patterns on Defective Urban Solar Panels between Unmanned Aerial Vehicle Video Stream and Photomosaics

Hwang¹,

Um²,

Kim³

2022

Sensors and Materials

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Unmanned aerial vehicle (UAV) autopilot flight requires a specific altitude when inspecting defective urban solar panels to avoid obstacles such as high-rise buildings, trees, and telegraph poles. Therefore, autopilot-based thermal imaging has severe data redundancy because the nonsolar panel area occupies more than 99% of the ground target. We aim to explore the tracking capability of a UAV video stream for defective urban solar panels by comparing spatial and clustering patterns with autopilot-based photomosaics. The spatial patterns of distributions and clusters in defective solar panels have high similarity (80-100%) to those of autopilot-based photomosaics. The results of this study can serve as a valuable reference for video-stream-based thermal deficiency inspections of defective solar panels in urban areas.

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

Hwang

et al. 2020

Energies

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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.

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Exploring Explainable Range of In-situ Portable CO2 Sensor Signatures for Carbon Stock Estimated in Forestry Carbon Project

Cited by 3 publications

References 14 publications

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

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

Comparative Evaluation of Tracking Capability of Spatial Patterns on Defective Urban Solar Panels between Unmanned Aerial Vehicle Video Stream and Photomosaics

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

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