利用有限数据实现可持续性:预测二氧化碳排放量的新型预测分析方法

IF 6.9 3区 管理学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Christos K. Filelis-Papadopoulos, Samuel N. Kirshner, Philip O’Reilly
{"title":"利用有限数据实现可持续性:预测二氧化碳排放量的新型预测分析方法","authors":"Christos K. Filelis-Papadopoulos, Samuel N. Kirshner, Philip O’Reilly","doi":"10.1007/s10796-024-10516-8","DOIUrl":null,"url":null,"abstract":"<p>Unforeseen events (e.g., COVID-19, the Russia-Ukraine conflict) create significant challenges for accurately predicting CO2 emissions in the airline industry. These events severely disrupt air travel by grounding planes and creating unpredictable, ad hoc flight schedules. This leads to many missing data points and data quality issues in the emission datasets, hampering accurate prediction. To address this issue, we develop a predictive analytics method to forecast CO2 emissions using a unique dataset of monthly emissions from 29,707 aircraft. Our approach outperforms prominent machine learning techniques in both accuracy and computational time. This paper contributes to theoretical knowledge in three ways: 1) advancing predictive analytics theory, 2) illustrating the organisational benefits of using analytics for decision-making, and 3) contributing to the growing focus on aviation in information systems literature. From a practical standpoint, our industry partner adopted our forecasting approach under an evaluation licence into their client-facing CO2 emissions platform.</p>","PeriodicalId":13610,"journal":{"name":"Information Systems Frontiers","volume":"52 1","pages":""},"PeriodicalIF":6.9000,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Sustainability with Limited Data: A Novel Predictive Analytics Approach for Forecasting CO2 Emissions\",\"authors\":\"Christos K. Filelis-Papadopoulos, Samuel N. Kirshner, Philip O’Reilly\",\"doi\":\"10.1007/s10796-024-10516-8\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Unforeseen events (e.g., COVID-19, the Russia-Ukraine conflict) create significant challenges for accurately predicting CO2 emissions in the airline industry. These events severely disrupt air travel by grounding planes and creating unpredictable, ad hoc flight schedules. This leads to many missing data points and data quality issues in the emission datasets, hampering accurate prediction. To address this issue, we develop a predictive analytics method to forecast CO2 emissions using a unique dataset of monthly emissions from 29,707 aircraft. Our approach outperforms prominent machine learning techniques in both accuracy and computational time. This paper contributes to theoretical knowledge in three ways: 1) advancing predictive analytics theory, 2) illustrating the organisational benefits of using analytics for decision-making, and 3) contributing to the growing focus on aviation in information systems literature. From a practical standpoint, our industry partner adopted our forecasting approach under an evaluation licence into their client-facing CO2 emissions platform.</p>\",\"PeriodicalId\":13610,\"journal\":{\"name\":\"Information Systems Frontiers\",\"volume\":\"52 1\",\"pages\":\"\"},\"PeriodicalIF\":6.9000,\"publicationDate\":\"2024-07-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Information Systems Frontiers\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1007/s10796-024-10516-8\",\"RegionNum\":3,\"RegionCategory\":\"管理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information Systems Frontiers","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s10796-024-10516-8","RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0

摘要

不可预见的事件(如 COVID-19、俄乌冲突)给航空业二氧化碳排放量的准确预测带来了巨大挑战。这些事件使飞机停飞,并造成不可预测的临时航班时刻表,从而严重扰乱了航空旅行。这导致排放数据集中出现许多数据点缺失和数据质量问题,从而阻碍了准确预测。为解决这一问题,我们开发了一种预测分析方法,利用来自 29,707 架飞机的独特月度排放数据集预测二氧化碳排放量。我们的方法在准确性和计算时间上都优于著名的机器学习技术。本文在三个方面对理论知识做出了贡献:1)推动预测分析理论的发展;2)说明使用分析技术进行决策对组织的益处;3)为信息系统文献中日益增长的对航空业的关注做出贡献。从实践角度来看,我们的行业合作伙伴在评估许可下采用了我们的预测方法,并将其纳入面向客户的二氧化碳排放平台。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Sustainability with Limited Data: A Novel Predictive Analytics Approach for Forecasting CO2 Emissions

Sustainability with Limited Data: A Novel Predictive Analytics Approach for Forecasting CO2 Emissions

Unforeseen events (e.g., COVID-19, the Russia-Ukraine conflict) create significant challenges for accurately predicting CO2 emissions in the airline industry. These events severely disrupt air travel by grounding planes and creating unpredictable, ad hoc flight schedules. This leads to many missing data points and data quality issues in the emission datasets, hampering accurate prediction. To address this issue, we develop a predictive analytics method to forecast CO2 emissions using a unique dataset of monthly emissions from 29,707 aircraft. Our approach outperforms prominent machine learning techniques in both accuracy and computational time. This paper contributes to theoretical knowledge in three ways: 1) advancing predictive analytics theory, 2) illustrating the organisational benefits of using analytics for decision-making, and 3) contributing to the growing focus on aviation in information systems literature. From a practical standpoint, our industry partner adopted our forecasting approach under an evaluation licence into their client-facing CO2 emissions platform.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Information Systems Frontiers
Information Systems Frontiers 工程技术-计算机:理论方法
CiteScore
13.30
自引率
18.60%
发文量
127
审稿时长
9 months
期刊介绍: The interdisciplinary interfaces of Information Systems (IS) are fast emerging as defining areas of research and development in IS. These developments are largely due to the transformation of Information Technology (IT) towards networked worlds and its effects on global communications and economies. While these developments are shaping the way information is used in all forms of human enterprise, they are also setting the tone and pace of information systems of the future. The major advances in IT such as client/server systems, the Internet and the desktop/multimedia computing revolution, for example, have led to numerous important vistas of research and development with considerable practical impact and academic significance. While the industry seeks to develop high performance IS/IT solutions to a variety of contemporary information support needs, academia looks to extend the reach of IS technology into new application domains. Information Systems Frontiers (ISF) aims to provide a common forum of dissemination of frontline industrial developments of substantial academic value and pioneering academic research of significant practical impact.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信