{"title":"驾驭碳效率的不确定性:跨收入群体的全球评估","authors":"Ziyao Li , Sangmok Kang","doi":"10.1016/j.ecoinf.2024.102837","DOIUrl":null,"url":null,"abstract":"<div><div>This study evaluates the carbon efficiency of 163 countries between 1992 and 2019, focusing on the relationship between economic growth and emission reductions. By using a novel approach that integrates Stochastic Metafrontier Analysis with Bayesian inference, the study robustly analyzes data variability and uncertainty. The results highlight significant differences in carbon efficiency across income groups. High-income countries (G1) show a technology gap uncertainty of 0.118, while low-income countries (G4) have a slightly higher uncertainty at 0.133, indicating challenges in technology transfer for both groups. Middle-income countries (G2), with the lowest uncertainty at 0.045, demonstrate a strong capacity to adopt advanced technologies and improve carbon efficiency. The study also identifies critical factors influencing carbon efficiency uncertainty, such as urbanization, forest area, and foreign direct investment. Urbanization affects these groups differently: it raises uncertainty in G4 by 0.0107 but reduces it in G1 by −0.0069, reflecting varying stages of urban development. These findings suggest the need for targeted policies to improve technology transfer, optimize urbanization, and enhance sustainable resource use, thereby facilitating a more effective shift to a low-carbon economy and reducing carbon efficiency uncertainties.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"83 ","pages":"Article 102837"},"PeriodicalIF":5.8000,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Navigating uncertainty in carbon efficiency: A global assessment across income groups\",\"authors\":\"Ziyao Li , Sangmok Kang\",\"doi\":\"10.1016/j.ecoinf.2024.102837\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>This study evaluates the carbon efficiency of 163 countries between 1992 and 2019, focusing on the relationship between economic growth and emission reductions. By using a novel approach that integrates Stochastic Metafrontier Analysis with Bayesian inference, the study robustly analyzes data variability and uncertainty. The results highlight significant differences in carbon efficiency across income groups. High-income countries (G1) show a technology gap uncertainty of 0.118, while low-income countries (G4) have a slightly higher uncertainty at 0.133, indicating challenges in technology transfer for both groups. Middle-income countries (G2), with the lowest uncertainty at 0.045, demonstrate a strong capacity to adopt advanced technologies and improve carbon efficiency. The study also identifies critical factors influencing carbon efficiency uncertainty, such as urbanization, forest area, and foreign direct investment. Urbanization affects these groups differently: it raises uncertainty in G4 by 0.0107 but reduces it in G1 by −0.0069, reflecting varying stages of urban development. These findings suggest the need for targeted policies to improve technology transfer, optimize urbanization, and enhance sustainable resource use, thereby facilitating a more effective shift to a low-carbon economy and reducing carbon efficiency uncertainties.</div></div>\",\"PeriodicalId\":51024,\"journal\":{\"name\":\"Ecological Informatics\",\"volume\":\"83 \",\"pages\":\"Article 102837\"},\"PeriodicalIF\":5.8000,\"publicationDate\":\"2024-10-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Ecological Informatics\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1574954124003790\",\"RegionNum\":2,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ECOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ecological Informatics","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1574954124003790","RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ECOLOGY","Score":null,"Total":0}
Navigating uncertainty in carbon efficiency: A global assessment across income groups
This study evaluates the carbon efficiency of 163 countries between 1992 and 2019, focusing on the relationship between economic growth and emission reductions. By using a novel approach that integrates Stochastic Metafrontier Analysis with Bayesian inference, the study robustly analyzes data variability and uncertainty. The results highlight significant differences in carbon efficiency across income groups. High-income countries (G1) show a technology gap uncertainty of 0.118, while low-income countries (G4) have a slightly higher uncertainty at 0.133, indicating challenges in technology transfer for both groups. Middle-income countries (G2), with the lowest uncertainty at 0.045, demonstrate a strong capacity to adopt advanced technologies and improve carbon efficiency. The study also identifies critical factors influencing carbon efficiency uncertainty, such as urbanization, forest area, and foreign direct investment. Urbanization affects these groups differently: it raises uncertainty in G4 by 0.0107 but reduces it in G1 by −0.0069, reflecting varying stages of urban development. These findings suggest the need for targeted policies to improve technology transfer, optimize urbanization, and enhance sustainable resource use, thereby facilitating a more effective shift to a low-carbon economy and reducing carbon efficiency uncertainties.
期刊介绍:
The journal Ecological Informatics is devoted to the publication of high quality, peer-reviewed articles on all aspects of computational ecology, data science and biogeography. The scope of the journal takes into account the data-intensive nature of ecology, the growing capacity of information technology to access, harness and leverage complex data as well as the critical need for informing sustainable management in view of global environmental and climate change.
The nature of the journal is interdisciplinary at the crossover between ecology and informatics. It focuses on novel concepts and techniques for image- and genome-based monitoring and interpretation, sensor- and multimedia-based data acquisition, internet-based data archiving and sharing, data assimilation, modelling and prediction of ecological data.