Version 1
: Received: 15 July 2024 / Approved: 15 July 2024 / Online: 15 July 2024 (12:13:55 CEST)
How to cite:
Lee, J.-H.; Kang, H.-Y.; Hwang, Y. W. Development of an Auxiliary Indicator for Improving the Rationality and Reliability of the National‐Level Carbon Productivity Indicator. Preprints2024, 2024071169. https://doi.org/10.20944/preprints202407.1169.v1
Lee, J.-H.; Kang, H.-Y.; Hwang, Y. W. Development of an Auxiliary Indicator for Improving the Rationality and Reliability of the National‐Level Carbon Productivity Indicator. Preprints 2024, 2024071169. https://doi.org/10.20944/preprints202407.1169.v1
Lee, J.-H.; Kang, H.-Y.; Hwang, Y. W. Development of an Auxiliary Indicator for Improving the Rationality and Reliability of the National‐Level Carbon Productivity Indicator. Preprints2024, 2024071169. https://doi.org/10.20944/preprints202407.1169.v1
APA Style
Lee, J. H., Kang, H. Y., & Hwang, Y. W. (2024). Development of an Auxiliary Indicator for Improving the Rationality and Reliability of the National‐Level Carbon Productivity Indicator. Preprints. https://doi.org/10.20944/preprints202407.1169.v1
Chicago/Turabian Style
Lee, J., Hong-Yoon Kang and Yong Woo Hwang. 2024 "Development of an Auxiliary Indicator for Improving the Rationality and Reliability of the National‐Level Carbon Productivity Indicator" Preprints. https://doi.org/10.20944/preprints202407.1169.v1
Abstract
Global attention to climate change has surged since the advent of the Paris Agreement, intensifying the importance of measuring and managing carbon productivity indicators on a national level. Nevertheless, concerns persist regarding the reliability of such measurements because of inherent discrepancies in implementing and operating national-level carbon productivity indicators, coupled with their inherent uncertainty. This study proposes a multiple regression model to address these issues aimed at refining national-level carbon productivity indicators metrics, accounting for factors such as the gross domestic product and total greenhouse gas emissions by sectors. The objective was to offer insights into enhancing and effectively utilizing current indicators, enabling a more nuanced interpretation of the variation in the carbon productivity indicators across diverse industrial landscapes. This study showed that adjustments of the carbon productivity metrics reflect disparities in emissions across industrial structures, with countries characterized by high emissions from non-service industries showing improving trends. In addition, this paper proposes an auxiliary indicator estimating method for carbon productivity, emphasizing its utility in interpreting productivity indicators within the context of varying industrial compositions across OECD countries. This study underscores the inadequacies of the current national productivity estimating method, pinpointing areas requiring refinement. Specifically, the method for estimating the auxiliary indicator for carbon productivity guarantees enhanced rationality when integrated with current methodologies. Moreover, by elucidating the nuances of industrial structures, this study advocates for more sophisticated approaches to interpreting and managing the productivity indicators tailored to each unique economic landscape of each country. Nevertheless, the limitations stemming from data availability underscore the need for further research, particularly in refining the national-level carbon resource productivity indicators analyses and exploring the thematic productivity variations in greater depth. By addressing these gaps, future studies will contribute to a more comprehensive understanding of national-level carbon resource productivity indicators dynamics and reveal targeted strategies for sustainable development.
Keywords
carbon productivity; Gross Domestic Product (GDP); Total Greenhouse gas Emission (TGE); the auxiliary indicator; environmental statistics research; multiple regression analysis
Subject
Social Sciences, Geography, Planning and Development
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
