Achieving the United Nations sustainable development goals – innovation diffusion and business model innovations

IF 2.3 Q3 REGIONAL & URBAN PLANNING Foresight Pub Date : 2024-08-29 DOI:10.1108/fs-11-2023-0233
Jarunee Wonglimpiyarat
{"title":"Achieving the United Nations sustainable development goals – innovation diffusion and business model innovations","authors":"Jarunee Wonglimpiyarat","doi":"10.1108/fs-11-2023-0233","DOIUrl":null,"url":null,"abstract":"<h3>Purpose</h3>\n<p>The study aims to analyse the race towards green development and United Nations sustainable development goals (SDGs) in the cases of Huawei and Shell. Both companies are the leaders in their respective industries. Huawei is an example case study representing the information and communications technology (ICT) industry whereas Shell is an example case study representing the oil and gas industry. The research analyses of the races in achieving UN SDGs were undertaken based on the innovation diffusion framework with the use of machine learning algorithms trained to extract data on sustainability activities and initiatives.</p><!--/ Abstract__block -->\n<h3>Design/methodology/approach</h3>\n<p>The research analyses the two case studies of Huawei and Shell. The research was undertaken through the steps of training machine learning algorithms, industry benchmarking and evaluating the performance of the race. The analyses regarding the activities and initiatives of Huawei and Shell in contributing towards SDGs are based on the data in the past 10 years (Years 2010–2019) using machine learning to extract data on sustainability activities and initiatives. In the case of Huawei, 313 sustainability reports were fed to the unsupervised machine learning algorithms revealing 15,101 sustainability actions and initiatives related to UN SDGs in the ICT industry. In the case of Shell, 2,015 sustainability reports were fed to the unsupervised machine learning algorithms revealing 47,365 sustainability actions and initiatives related to UN SDGs in the oil and gas industry.</p><!--/ Abstract__block -->\n<h3>Findings</h3>\n<p>The analyses of findings revealed that Huawei and Shell performed very well in progressing towards the UN SDGs. Huawei had strong performance in the ICT industry with regard to SDGs No. 3, 4, 7, 8, 11, 12 and 16 while Shell had strong performance in the oil and gas industry with regard to SDGs No. 3, 4, 6, 7, 8, 12 and 16. Both companies had placed a focus on achieving SDG 12 responsible consumption and production, SDG 7 affordable and clean energy and SDG 4 quality education. The synthesised business model innovations of Huawei and Shell had shown their environmental, social and governance strategies – Huawei’s 2030 vision for green development and Shell’s 2050 vision for net zero emissions.</p><!--/ Abstract__block -->\n<h3>Practical implications</h3>\n<p>The five pillars of people, planet, prosperity, peace and partnership according to the UN 2030 agenda for sustainable development have shown the way a company operates to promote sustainable eco-systems. The extent to which both Huawei and Shell link corporate strategies to the UN SDGs has reflected their implementation progress. Furthermore, the business model innovations of Huawei and Shell provides a useful framework which can be applied to encourage other companies/organisations in various industries to undertake ESG activities in practice.</p><!--/ Abstract__block -->\n<h3>Originality/value</h3>\n<p>The main contribution of this research is the application of machine learning algorithms and the innovation diffusion model in analysing the SDGs performance. The study applies the innovation diffusion framework to explore strategic actions and initiatives of Huawei and Shell in transitioning towards sustainability. The use of machine learning algorithms has identified their sustainability approach in achieving the UN SDGs.</p><!--/ Abstract__block -->","PeriodicalId":51620,"journal":{"name":"Foresight","volume":null,"pages":null},"PeriodicalIF":2.3000,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Foresight","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1108/fs-11-2023-0233","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"REGIONAL & URBAN PLANNING","Score":null,"Total":0}
引用次数: 0

Abstract

Purpose

The study aims to analyse the race towards green development and United Nations sustainable development goals (SDGs) in the cases of Huawei and Shell. Both companies are the leaders in their respective industries. Huawei is an example case study representing the information and communications technology (ICT) industry whereas Shell is an example case study representing the oil and gas industry. The research analyses of the races in achieving UN SDGs were undertaken based on the innovation diffusion framework with the use of machine learning algorithms trained to extract data on sustainability activities and initiatives.

Design/methodology/approach

The research analyses the two case studies of Huawei and Shell. The research was undertaken through the steps of training machine learning algorithms, industry benchmarking and evaluating the performance of the race. The analyses regarding the activities and initiatives of Huawei and Shell in contributing towards SDGs are based on the data in the past 10 years (Years 2010–2019) using machine learning to extract data on sustainability activities and initiatives. In the case of Huawei, 313 sustainability reports were fed to the unsupervised machine learning algorithms revealing 15,101 sustainability actions and initiatives related to UN SDGs in the ICT industry. In the case of Shell, 2,015 sustainability reports were fed to the unsupervised machine learning algorithms revealing 47,365 sustainability actions and initiatives related to UN SDGs in the oil and gas industry.

Findings

The analyses of findings revealed that Huawei and Shell performed very well in progressing towards the UN SDGs. Huawei had strong performance in the ICT industry with regard to SDGs No. 3, 4, 7, 8, 11, 12 and 16 while Shell had strong performance in the oil and gas industry with regard to SDGs No. 3, 4, 6, 7, 8, 12 and 16. Both companies had placed a focus on achieving SDG 12 responsible consumption and production, SDG 7 affordable and clean energy and SDG 4 quality education. The synthesised business model innovations of Huawei and Shell had shown their environmental, social and governance strategies – Huawei’s 2030 vision for green development and Shell’s 2050 vision for net zero emissions.

Practical implications

The five pillars of people, planet, prosperity, peace and partnership according to the UN 2030 agenda for sustainable development have shown the way a company operates to promote sustainable eco-systems. The extent to which both Huawei and Shell link corporate strategies to the UN SDGs has reflected their implementation progress. Furthermore, the business model innovations of Huawei and Shell provides a useful framework which can be applied to encourage other companies/organisations in various industries to undertake ESG activities in practice.

Originality/value

The main contribution of this research is the application of machine learning algorithms and the innovation diffusion model in analysing the SDGs performance. The study applies the innovation diffusion framework to explore strategic actions and initiatives of Huawei and Shell in transitioning towards sustainability. The use of machine learning algorithms has identified their sustainability approach in achieving the UN SDGs.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
实现联合国可持续发展目标--创新传播和商业模式创新
本研究旨在分析华为和壳牌公司在绿色发展和联合国可持续发展目标(SDGs)方面的竞赛。这两家公司都是各自行业中的佼佼者。华为是代表信息和通信技术(ICT)行业的案例研究范例,而壳牌则是代表石油和天然气行业的案例研究范例。在创新扩散框架的基础上,利用经过训练的机器学习算法来提取可持续发展活动和倡议的数据,对实现联合国可持续发展目标的竞赛进行了研究分析。研究通过训练机器学习算法、行业基准和评估竞赛表现等步骤进行。关于华为和壳牌在促进可持续发展目标方面的活动和举措的分析是基于过去 10 年(2010-2019 年)的数据,使用机器学习提取可持续发展活动和举措的数据。就华为而言,313 份可持续发展报告被输入到无监督机器学习算法中,揭示了 ICT 行业中与联合国可持续发展目标相关的 15101 项可持续发展行动和倡议。在壳牌公司的案例中,无监督机器学习算法收集了 2,015 份可持续发展报告,揭示了石油和天然气行业中与联合国可持续发展目标相关的 47,365 项可持续发展行动和倡议。华为在 ICT 行业的可持续发展目标 3、4、7、8、11、12 和 16 方面表现突出,而壳牌在石油和天然气行业的可持续发展目标 3、4、6、7、8、12 和 16 方面表现突出。两家公司都把重点放在实现可持续发展目标 12 "负责任的消费和生产"、可持续发展目标 7 "负担得起的清洁能源 "和可持续发展目标 4 "优质教育 "上。华为和壳牌的商业模式创新综合展示了其环境、社会和治理战略--华为的 2030 年绿色发展愿景和壳牌的 2050 年净零排放愿景。华为和壳牌将企业战略与联合国可持续发展目标联系起来的程度反映了其实施进展。此外,华为和壳牌的商业模式创新提供了一个有用的框架,可用于鼓励各行各业的其他公司/组织在实践中开展环境、社会和治理活动。本研究运用创新扩散框架来探讨华为和壳牌在向可持续发展转型过程中的战略行动和举措。机器学习算法的使用确定了它们在实现联合国可持续发展目标方面的可持续发展方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Foresight
Foresight REGIONAL & URBAN PLANNING-
CiteScore
5.10
自引率
5.00%
发文量
45
期刊介绍: ■Social, political and economic science ■Sustainable development ■Horizon scanning ■Scientific and Technological Change and its implications for society and policy ■Management of Uncertainty, Complexity and Risk ■Foresight methodology, tools and techniques
期刊最新文献
Technology foresight in Indonesia: developing scenarios to determine electrical vehicle research priority for future innovation Exploring the relationship between small and medium-sized enterprises innovation and organizational performance: a prospective study on the industrial sector in Ecuador Examining sustainable consumption patterns through green purchase behavior and digital media engagement: a case of Pakistan’s postmillennials Achieving the United Nations sustainable development goals – innovation diffusion and business model innovations Who wants cryptocurrency? The effects of expectations and conjectures
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1