Tetiana Shevchenko, Lauren Durivault, Meisam Ranjbari, Zahra Shams Esfandabadi, Bernard Yannou, Robert Heidsieck, Michael Saidani, Ghada Bouillass
As the circular economy transition gains traction in the healthcare sector, extending the lifespan of complex medical devices has become a key priority, given their high cost and the challenges associated with their technological sophistication. To effectively advance this transition, there is a growing need for a robust tool capable of assessing and enhancing the circularity of complex medical devices. In response, this study introduces the Medical Device Circularity Assessment Tool (MDCATool), a novel tool for identifying circular redesign strategies to improve the circularity performance of complex medical devices. The MDCATool draws on the hybrid “Closing‐Slowing Future‐Past” quadrant model, enriched with a hierarchy of product life extension strategies and associated environmental performance attributes. To demonstrate its applicability, the developed tool is employed to identify redesign strategies for an X‐ray tube, an integral component of a mammography system from a medical technology original equipment manufacturer. In addition, this study presents a circularity data checklist to support automated diagnostics and redesign decisions, forming the basis of a digital application for medical device assessment. The tool, designed for industry practitioners, supports evidence‐based decisions and innovation in circular medical device design, helping to improve the circularity performance of complex medical devices, including magnetic resonance imaging scanners, computed tomography systems, and X‐ray and ultrasound devices.
{"title":"Circular Economy in the Healthcare Industry: Developing a Circularity Assessment Tool for Complex Medical Devices","authors":"Tetiana Shevchenko, Lauren Durivault, Meisam Ranjbari, Zahra Shams Esfandabadi, Bernard Yannou, Robert Heidsieck, Michael Saidani, Ghada Bouillass","doi":"10.1002/bse.70647","DOIUrl":"https://doi.org/10.1002/bse.70647","url":null,"abstract":"As the circular economy transition gains traction in the healthcare sector, extending the lifespan of complex medical devices has become a key priority, given their high cost and the challenges associated with their technological sophistication. To effectively advance this transition, there is a growing need for a robust tool capable of assessing and enhancing the circularity of complex medical devices. In response, this study introduces the Medical Device Circularity Assessment Tool (MDCATool), a novel tool for identifying circular redesign strategies to improve the circularity performance of complex medical devices. The MDCATool draws on the hybrid “Closing‐Slowing Future‐Past” quadrant model, enriched with a hierarchy of product life extension strategies and associated environmental performance attributes. To demonstrate its applicability, the developed tool is employed to identify redesign strategies for an X‐ray tube, an integral component of a mammography system from a medical technology original equipment manufacturer. In addition, this study presents a circularity data checklist to support automated diagnostics and redesign decisions, forming the basis of a digital application for medical device assessment. The tool, designed for industry practitioners, supports evidence‐based decisions and innovation in circular medical device design, helping to improve the circularity performance of complex medical devices, including magnetic resonance imaging scanners, computed tomography systems, and X‐ray and ultrasound devices.","PeriodicalId":9518,"journal":{"name":"Business Strategy and The Environment","volume":"7 1","pages":""},"PeriodicalIF":13.4,"publicationDate":"2026-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147495242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study examines how sustainability drivers—technological innovation, sustainable supply chain management (SSCM) and corporate social responsibility (CSR)—influence environmental and organisational performance in Vietnam's logistics industry. Drawing on the natural resource–based view, institutional theory and stakeholder theory, survey data from 322 logistics professionals in Vietnam were analysed using structural equation modelling (SEM) in Stata. The results show that technological innovation has a strong positive effect on environmental performance ( β = 0.788, p < 0.001), while SSCM positively influences organisational performance ( β = 0.116, p < 0.05). CSR significantly enhances environmental performance ( β = 0.215, p < 0.01) but demonstrates a negative association with organisational performance ( β = −0.117, p < 0.05), indicating short‐term cost pressures associated with CSR investments. In contrast, environmental performance does not exert a significant effect on organisational performance, and technological innovation does not significantly predict SSCM adoption, reflecting coordination gaps, institutional constraints and uneven sustainability integration within Vietnam's logistics sector. The findings suggest that logistics firms should strategically integrate digital technologies with sustainability‐oriented supply chain practices rather than adopting technological innovations in isolation. For policymakers, the results highlight the need for stronger regulatory enforcement, targeted incentives for green technologies and mechanisms that promote interfirm collaboration to translate sustainability investments into long‐term competitive advantages in emerging logistics markets such as Vietnam.
本研究考察了可持续发展驱动因素——技术创新、可持续供应链管理（SSCM）和企业社会责任（CSR）——如何影响越南物流业的环境和组织绩效。利用基于自然资源的观点、制度理论和利益相关者理论，利用Stata的结构方程模型（SEM）分析了来自越南322名物流专业人士的调查数据。研究结果表明，技术创新对环境绩效有显著的正向影响（β = 0.788, p < 0.001），而SSCM对组织绩效有显著的正向影响（β = 0.116, p < 0.05）。企业社会责任显著提高了环境绩效（β = 0.215, p < 0.01），但与组织绩效呈负相关（β = - 0.117, p < 0.05），表明与企业社会责任投资相关的短期成本压力。相比之下，环境绩效对组织绩效没有显著影响，技术创新也不能显著预测SSCM的采用，这反映了越南物流部门的协调差距、制度约束和不平衡的可持续性整合。研究结果表明，物流公司应战略性地将数字技术与以可持续发展为导向的供应链实践相结合，而不是孤立地采用技术创新。对于政策制定者来说，研究结果强调需要加强监管执法，有针对性地激励绿色技术，并建立促进企业间合作的机制，将可持续发展投资转化为越南等新兴物流市场的长期竞争优势。
{"title":"Integrating Innovation and Environmental Strategy: Sustainability Drivers and Organisational Performance in Vietnam's Logistics Sector","authors":"Erkan Duzgun, Erhan Atay","doi":"10.1002/bse.70776","DOIUrl":"https://doi.org/10.1002/bse.70776","url":null,"abstract":"This study examines how sustainability drivers—technological innovation, sustainable supply chain management (SSCM) and corporate social responsibility (CSR)—influence environmental and organisational performance in Vietnam's logistics industry. Drawing on the natural resource–based view, institutional theory and stakeholder theory, survey data from 322 logistics professionals in Vietnam were analysed using structural equation modelling (SEM) in Stata. The results show that technological innovation has a strong positive effect on environmental performance ( <jats:italic>β</jats:italic> = 0.788, <jats:italic>p</jats:italic> < 0.001), while SSCM positively influences organisational performance ( <jats:italic>β</jats:italic> = 0.116, <jats:italic>p</jats:italic> < 0.05). CSR significantly enhances environmental performance ( <jats:italic>β</jats:italic> = 0.215, <jats:italic>p</jats:italic> < 0.01) but demonstrates a negative association with organisational performance ( <jats:italic>β</jats:italic> = −0.117, <jats:italic>p</jats:italic> < 0.05), indicating short‐term cost pressures associated with CSR investments. In contrast, environmental performance does not exert a significant effect on organisational performance, and technological innovation does not significantly predict SSCM adoption, reflecting coordination gaps, institutional constraints and uneven sustainability integration within Vietnam's logistics sector. The findings suggest that logistics firms should strategically integrate digital technologies with sustainability‐oriented supply chain practices rather than adopting technological innovations in isolation. For policymakers, the results highlight the need for stronger regulatory enforcement, targeted incentives for green technologies and mechanisms that promote interfirm collaboration to translate sustainability investments into long‐term competitive advantages in emerging logistics markets such as Vietnam.","PeriodicalId":9518,"journal":{"name":"Business Strategy and The Environment","volume":"16 1","pages":""},"PeriodicalIF":13.4,"publicationDate":"2026-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147495241","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The purpose of this study is to investigate the role of the green supply chain as a mediator in the relationship between a green marketing strategy and a firm's environmental performance. Using sustainability theory as our theoretical framework, we developed a model and tested it based on survey data from 170 managers in Israeli companies operating in international markets. Our findings reveal that a green marketing strategy has no direct effect on a firm's environmental performance. However, a green supply chain fully mediates the relationship between a green marketing strategy and a firm's environmental performance. These findings align with sustainability theory by showing that implementing a green marketing strategy alone does not necessarily guarantee improved environmental performance. Maximizing the impact of a green marketing strategy requires its operational implementation across the supply chain to drive environmental performance. Our results have both theoretical and managerial implications. First, from the perspective of sustainability theory, our study combines two interdisciplinary organizational approaches (marketing and the supply chain) that together enable a company to function more effectively. Second, logistics and green supply chain strategies provide faster responses to customers' changing demands and thereby improve a firm's environmental performance. Practically, managers need to implement innovative technologies to adapt their organization to the changing business environment while considering environmental conservation and responding to changes in customers' needs.
{"title":"Green Marketing: The Relationships Between Marketing Strategy, Supply Chain, and Firms' Environmental Performance","authors":"Gavriel Dahan, Michal Levi‐Bliech","doi":"10.1002/bse.70804","DOIUrl":"https://doi.org/10.1002/bse.70804","url":null,"abstract":"The purpose of this study is to investigate the role of the green supply chain as a mediator in the relationship between a green marketing strategy and a firm's environmental performance. Using sustainability theory as our theoretical framework, we developed a model and tested it based on survey data from 170 managers in Israeli companies operating in international markets. Our findings reveal that a green marketing strategy has no direct effect on a firm's environmental performance. However, a green supply chain fully mediates the relationship between a green marketing strategy and a firm's environmental performance. These findings align with sustainability theory by showing that implementing a green marketing strategy alone does not necessarily guarantee improved environmental performance. Maximizing the impact of a green marketing strategy requires its operational implementation across the supply chain to drive environmental performance. Our results have both theoretical and managerial implications. First, from the perspective of sustainability theory, our study combines two interdisciplinary organizational approaches (marketing and the supply chain) that together enable a company to function more effectively. Second, logistics and green supply chain strategies provide faster responses to customers' changing demands and thereby improve a firm's environmental performance. Practically, managers need to implement innovative technologies to adapt their organization to the changing business environment while considering environmental conservation and responding to changes in customers' needs.","PeriodicalId":9518,"journal":{"name":"Business Strategy and The Environment","volume":"33 1","pages":""},"PeriodicalIF":13.4,"publicationDate":"2026-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147495243","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Globally, solar energy initiatives are increasingly recognised not only as a means of decarbonisation but also as vehicles for promoting the circular economy (CE) principles throughout infrastructure life cycles. The expansion of solar installations and the associated material intensity require a transition to circularity to alleviate resource depletion, waste production and lifecycle emissions. Circularity redefines solar infrastructure as a sustainable material repository, enhancing asset value via predictive maintenance, component recovery and closed‐loop supply chains. Circular economy business models (CEBM) implement these practices by transitioning from single‐asset sales to service‐oriented, performance‐based frameworks. Thus, in this study, we sought to identify critical factors to improve a circular economy business model (CEBM) for solar projects. A structured quantitative survey was employed to gather data from 426 solar project practitioners across various worldwide locations, offering a cross‐geographical perspective on circularity in solar projects. The analysis encompassed multiple procedures, including model creation, measurement model estimation and structural model estimation. Structural equation modelling (SEM) was employed to evaluate the study's model. First, our findings empirically enhance the theory in the following three respects: by redefining circularity as a capability for configuring business models; by clarifying that sustainability outcomes are inherently integrated within energy organisations rather than merely added; and by conceptualising solar projects as transient yet transformative platforms that instantiate long‐term circular value logics. Secondly, we have empirically validated a ce MB that surpasses CE principles by focusing on organisational elements and project‐level implementation techniques, demonstrating how energy firms can incorporate CE value‐creation principles into the design and execution of solar projects.
{"title":"The Adoption of a Sustainable Circular Economy Business Model for Solar Energy Projects: Linking Circularity Practices and Value Creation Principles","authors":"Suleiman Mohammad Al Sabah, Edward G. Ochieng","doi":"10.1002/bse.70743","DOIUrl":"https://doi.org/10.1002/bse.70743","url":null,"abstract":"Globally, solar energy initiatives are increasingly recognised not only as a means of decarbonisation but also as vehicles for promoting the circular economy (CE) principles throughout infrastructure life cycles. The expansion of solar installations and the associated material intensity require a transition to circularity to alleviate resource depletion, waste production and lifecycle emissions. Circularity redefines solar infrastructure as a sustainable material repository, enhancing asset value via predictive maintenance, component recovery and closed‐loop supply chains. Circular economy business models (CEBM) implement these practices by transitioning from single‐asset sales to service‐oriented, performance‐based frameworks. Thus, in this study, we sought to identify critical factors to improve a circular economy business model (CEBM) for solar projects. A structured quantitative survey was employed to gather data from 426 solar project practitioners across various worldwide locations, offering a cross‐geographical perspective on circularity in solar projects. The analysis encompassed multiple procedures, including model creation, measurement model estimation and structural model estimation. Structural equation modelling (SEM) was employed to evaluate the study's model. First, our findings empirically enhance the theory in the following three respects: by redefining circularity as a capability for configuring business models; by clarifying that sustainability outcomes are inherently integrated within energy organisations rather than merely added; and by conceptualising solar projects as transient yet transformative platforms that instantiate long‐term circular value logics. Secondly, we have empirically validated a <jats:sc>ce</jats:sc> MB that surpasses CE principles by focusing on organisational elements and project‐level implementation techniques, demonstrating how energy firms can incorporate CE value‐creation principles into the design and execution of solar projects.","PeriodicalId":9518,"journal":{"name":"Business Strategy and The Environment","volume":"31 1","pages":""},"PeriodicalIF":13.4,"publicationDate":"2026-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147495262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mohammad Ali Beheshtinia, Ali Ghorbani, Masood Fathi, Morteza Ghobakhloo, Behzad Foroughi
Industry 5.0 has emerged to address the limitations of Industry 4.0 by emphasizing inclusive sustainability through environmentalism, human‐centricity, and resilience. However, its development faces barriers across technological, organizational, and social dimensions. This study proposes a structured decision‐making framework to identify critical barriers, design targeted strategies, and prioritize them for implementation. Strategies are evaluated using seven criteria: impact, cost, timeframe, feasibility, risk, complexity, and acceptance. A hybrid fuzzy multi‐criteria decision‐making method, informed by systematic literature review and expert consultation, ensures reliable and balanced prioritization. The analysis identified 54 barriers and 35 strategies, highlighting the highest importance of “public–private partnership programs in driving digital transformation,” “resilience‐focused business continuity,” and “localized and regional digital infrastructure development,” respectively. Considering all criteria, the strategies “clarity on long‐term return on investment,” “resilience‐focused business continuity programs,” and “ethical AI adoption programs for smaller enterprises” received the highest implementation priority, respectively. This study makes three key contributions to the literature on Industry 5.0. First, it provides a comprehensive and structured summary of Industry 5.0 barriers, identifying 54 barriers across seven distinct dimensions. Second, it develops a systematic QFD‐based framework that directly links these barriers to strategies for overcoming them and facilitating the development of Industry 5.0. Third, it introduces a novel MCDM method called F‐WASKOR. The study provides a phased roadmap for advancing Industry 5.0 transformation and realizing inclusive sustainability goals.
{"title":"Strategies for Realizing Industry 5.0 Inclusive Sustainability Goals for Sustainable Development: A Structured Framework for Overcoming Barriers and Guiding Transformation","authors":"Mohammad Ali Beheshtinia, Ali Ghorbani, Masood Fathi, Morteza Ghobakhloo, Behzad Foroughi","doi":"10.1002/bse.70755","DOIUrl":"https://doi.org/10.1002/bse.70755","url":null,"abstract":"Industry 5.0 has emerged to address the limitations of Industry 4.0 by emphasizing inclusive sustainability through environmentalism, human‐centricity, and resilience. However, its development faces barriers across technological, organizational, and social dimensions. This study proposes a structured decision‐making framework to identify critical barriers, design targeted strategies, and prioritize them for implementation. Strategies are evaluated using seven criteria: impact, cost, timeframe, feasibility, risk, complexity, and acceptance. A hybrid fuzzy multi‐criteria decision‐making method, informed by systematic literature review and expert consultation, ensures reliable and balanced prioritization. The analysis identified 54 barriers and 35 strategies, highlighting the highest importance of “public–private partnership programs in driving digital transformation,” “resilience‐focused business continuity,” and “localized and regional digital infrastructure development,” respectively. Considering all criteria, the strategies “clarity on long‐term return on investment,” “resilience‐focused business continuity programs,” and “ethical AI adoption programs for smaller enterprises” received the highest implementation priority, respectively. This study makes three key contributions to the literature on Industry 5.0. First, it provides a comprehensive and structured summary of Industry 5.0 barriers, identifying 54 barriers across seven distinct dimensions. Second, it develops a systematic QFD‐based framework that directly links these barriers to strategies for overcoming them and facilitating the development of Industry 5.0. Third, it introduces a novel MCDM method called F‐WASKOR. The study provides a phased roadmap for advancing Industry 5.0 transformation and realizing inclusive sustainability goals.","PeriodicalId":9518,"journal":{"name":"Business Strategy and The Environment","volume":"2 1","pages":""},"PeriodicalIF":13.4,"publicationDate":"2026-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147495263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Bouteska, Le Thanh Ha, Jihène Ghouli, Naif Alsagr
Leveraging the innovative 𝑅2 decomposed linkage method, our study delves into the complex and reciprocal relationship between monthly energy fluctuations and climate policy uncertainty (CPU) indicators. Spanning from January 2017 to December 2023, this analysis highlights both immediate and delayed interactions between these factors. We find that clean and solar energy markets predominantly shaped climate uncertainty from Q3 2018 to late 2020, particularly during the early stages of the climate uncertainty‐solar energy connection. In contrast, from Q4 2022 onward, climate uncertainty emerged as the dominant force influencing clean/solar energy. Additionally, climate uncertainty exerted significant control over crude oil prices throughout 2019 and 2021, before an inversion of this relationship emerged in the latter part of 2022. For natural gas, climate uncertainty maintained dominance from 2021 through Q1 2023, with a notable reversal from Q2 2023 onward.
{"title":"Shifting Currents: Unraveling the Dynamic Dance Between Climate Policy Uncertainty and Energy Market Volatility","authors":"A. Bouteska, Le Thanh Ha, Jihène Ghouli, Naif Alsagr","doi":"10.1002/bse.70735","DOIUrl":"https://doi.org/10.1002/bse.70735","url":null,"abstract":"Leveraging the innovative 𝑅2 decomposed linkage method, our study delves into the complex and reciprocal relationship between monthly energy fluctuations and climate policy uncertainty (CPU) indicators. Spanning from January 2017 to December 2023, this analysis highlights both immediate and delayed interactions between these factors. We find that clean and solar energy markets predominantly shaped climate uncertainty from Q3 2018 to late 2020, particularly during the early stages of the climate uncertainty‐solar energy connection. In contrast, from Q4 2022 onward, climate uncertainty emerged as the dominant force influencing clean/solar energy. Additionally, climate uncertainty exerted significant control over crude oil prices throughout 2019 and 2021, before an inversion of this relationship emerged in the latter part of 2022. For natural gas, climate uncertainty maintained dominance from 2021 through Q1 2023, with a notable reversal from Q2 2023 onward.","PeriodicalId":9518,"journal":{"name":"Business Strategy and The Environment","volume":"92 1","pages":""},"PeriodicalIF":13.4,"publicationDate":"2026-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147495245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Muskan Sahu, Waleed M. Alahdal, Mohammed M. Elgammal
This study investigates how artificial intelligence (AI) can support sustainable development strategies in OECD countries, particularly in achieving the United Nations Sustainable Development Goals (SDGs). The study contributes to the literature by investigating the role of AI as a transformative enabler of sustainable business strategies across varying institutional quality conditions. Employing quantile regression and two‐stage least squares (2SLS) techniques, this study examines the environmental and strategic implications of three AI dimensions: research and development, market advantage, and infrastructure. The findings reveal that all three dimensions of AI have a positive influence on SDG outcomes. However, the moderating role of institutional quality proved complex and dimension‐specific, sometimes enhancing and at other times constraining AI's effectiveness. These findings underscore the importance for OECD countries of adopting governance‐sensitive, innovation‐driven strategies to leverage AI in support of environmental sustainability. The study offers policy‐relevant recommendations for aligning AI deployment with sustainable development objectives.
{"title":"Governing Artificial Intelligence and Digital Economy for Sustainability: Does Institutional Quality Help or Hinder SDGs Progress Across OECD Countries?","authors":"Muskan Sahu, Waleed M. Alahdal, Mohammed M. Elgammal","doi":"10.1002/bse.70784","DOIUrl":"https://doi.org/10.1002/bse.70784","url":null,"abstract":"This study investigates how artificial intelligence (AI) can support sustainable development strategies in OECD countries, particularly in achieving the United Nations Sustainable Development Goals (SDGs). The study contributes to the literature by investigating the role of AI as a transformative enabler of sustainable business strategies across varying institutional quality conditions. Employing quantile regression and two‐stage least squares (2SLS) techniques, this study examines the environmental and strategic implications of three AI dimensions: research and development, market advantage, and infrastructure. The findings reveal that all three dimensions of AI have a positive influence on SDG outcomes. However, the moderating role of institutional quality proved complex and dimension‐specific, sometimes enhancing and at other times constraining AI's effectiveness. These findings underscore the importance for OECD countries of adopting governance‐sensitive, innovation‐driven strategies to leverage AI in support of environmental sustainability. The study offers policy‐relevant recommendations for aligning AI deployment with sustainable development objectives.","PeriodicalId":9518,"journal":{"name":"Business Strategy and The Environment","volume":"33 1","pages":""},"PeriodicalIF":13.4,"publicationDate":"2026-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147495268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Md Zubair Ahmad, Jewel Rana, Mohammad Fakhrul Islam, Md Nazmul Islam Jihad, Md. Rashed, Md. Kamal Uddin
The United States (U.S.) faces challenges in achieving its ambitious net‐zero carbon emissions target by 2050, with current emissions having fallen by less than 1% in 2024. Despite an investment of $500 billion in low‐carbon resources while holding the second‐largest green technology patent portfolio globally, it is further imperative to investigate ongoing innovations for suboptimal resource allocation and policy misalignment between investment strategies and environmental effectiveness. In this study, we examine the comparative impacts of artificial intelligence (AI) innovation, research and development (R&D) investment, government intervention, natural resource rents, and renewable energy consumption on U.S. environmental sustainability (ECOI) spanning 1990–2022. We bridge the gap in prior literature with respect to understanding which pathways of innovation lead to the highest carbon efficiency returns per dollar invested, moving beyond aggregate investment analysis toward identifying the optimal policy sequencing and resource allocation strategies. We implemented a comprehensive time series econometric framework, including autoregressive distributed lag bounds testing, the vector error correction model, and Granger causality analysis on 33 years of national‐level data. Our findings suggest that R&D investment results in the greatest improvement in long‐term carbon intensity, followed by AI patents and renewable energy usage. Government intervention has significant negative long‐term effects despite positive short‐term impacts, which may indicate potential crowding‐out effects. Natural resource dependency has positive long‐term benefits with negative short‐term impacts, suggesting opportunities for strategic extraction. The error correction mechanism implies a moderate adjustment speed toward equilibrium, whereas impulse response functions (IRFs) reveal that AI innovations establish rapid environmental benefits peaking in the second period. These results provide crucial evidence for federal climate investment prioritization by suggesting that taking funds away from direct government spending and putting them into AI‐integrated R&D initiatives could maximize carbon reduction outcomes and accelerate progress toward net‐zero targets.
{"title":"Innovation Pathways to Carbon Efficiency: Disentangling the Effects of AI, R&D, and Clean Energy Blessings on U.S. Environmental Sustainability","authors":"Md Zubair Ahmad, Jewel Rana, Mohammad Fakhrul Islam, Md Nazmul Islam Jihad, Md. Rashed, Md. Kamal Uddin","doi":"10.1002/bse.70748","DOIUrl":"https://doi.org/10.1002/bse.70748","url":null,"abstract":"The United States (U.S.) faces challenges in achieving its ambitious net‐zero carbon emissions target by 2050, with current emissions having fallen by less than 1% in 2024. Despite an investment of $500 billion in low‐carbon resources while holding the second‐largest green technology patent portfolio globally, it is further imperative to investigate ongoing innovations for suboptimal resource allocation and policy misalignment between investment strategies and environmental effectiveness. In this study, we examine the comparative impacts of artificial intelligence (AI) innovation, research and development (R&D) investment, government intervention, natural resource rents, and renewable energy consumption on U.S. environmental sustainability (ECOI) spanning 1990–2022. We bridge the gap in prior literature with respect to understanding which pathways of innovation lead to the highest carbon efficiency returns per dollar invested, moving beyond aggregate investment analysis toward identifying the optimal policy sequencing and resource allocation strategies. We implemented a comprehensive time series econometric framework, including autoregressive distributed lag bounds testing, the vector error correction model, and Granger causality analysis on 33 years of national‐level data. Our findings suggest that R&D investment results in the greatest improvement in long‐term carbon intensity, followed by AI patents and renewable energy usage. Government intervention has significant negative long‐term effects despite positive short‐term impacts, which may indicate potential crowding‐out effects. Natural resource dependency has positive long‐term benefits with negative short‐term impacts, suggesting opportunities for strategic extraction. The error correction mechanism implies a moderate adjustment speed toward equilibrium, whereas impulse response functions (IRFs) reveal that AI innovations establish rapid environmental benefits peaking in the second period. These results provide crucial evidence for federal climate investment prioritization by suggesting that taking funds away from direct government spending and putting them into AI‐integrated R&D initiatives could maximize carbon reduction outcomes and accelerate progress toward net‐zero targets.","PeriodicalId":9518,"journal":{"name":"Business Strategy and The Environment","volume":"315 1","pages":""},"PeriodicalIF":13.4,"publicationDate":"2026-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147495266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study systematizes the literature on eco‐innovation and economic complexity, aiming to understand how the sophistication of productive structures shapes countries' capacity to develop environmentally responsible innovations, and how eco‐innovation may, in turn, influence productive sophistication. Accordingly, the study (1) maps predominant conceptual and methodological approaches; (2) synthesizes empirical evidence on the effects of productive complexity on eco‐innovation, including potential bidirectional dynamics; (3) examines the regulatory, policy, and incentive frameworks conditioning this relationship; (4) identifies the technological, institutional, and human capabilities underpinning eco‐innovation and productive upgrading; (5) investigates the role of international integration and cross‐country cooperation; and (6) assesses the environmental outcomes associated with complexity trajectories and green innovation. Methodologically, the analysis combines a bibliometric analysis with a systematic literature review encompassing 79 peer‐reviewed articles published between 2014 and 2024. The results indicate consistent evidence that economic complexity positively influences eco‐innovation, while the reverse mechanism—eco‐innovation fostering productive sophistication—remains underexplored, with only one study explicitly addressing bidirectionality. The literature also shows that eco‐innovation contributes to reducing CO 2 emissions, although findings on the environmental effects of economic complexity are mixed. Institutional quality, productive capabilities, and global linkages emerge as central drivers of both higher complexity and improved environmental performance. Although patents are widely used as proxies for eco‐innovation, few studies treat them as the primary unit of analysis, and comparative work across countries or between green and conventional innovation remains limited. Overall, the study identifies substantial conceptual and empirical gaps and underscores the need for more integrated and interdisciplinary research to advance understanding of the interplay between eco‐innovation, productive sophistication, and sustainability.
{"title":"Eco‐Innovation, Economic Complexity, and Sustainability: A Bibliometric and Systematic Literature Review","authors":"Gregory Matheus Pereira de Moraes, Diogo Ferraz","doi":"10.1002/bse.70756","DOIUrl":"https://doi.org/10.1002/bse.70756","url":null,"abstract":"This study systematizes the literature on eco‐innovation and economic complexity, aiming to understand how the sophistication of productive structures shapes countries' capacity to develop environmentally responsible innovations, and how eco‐innovation may, in turn, influence productive sophistication. Accordingly, the study (1) maps predominant conceptual and methodological approaches; (2) synthesizes empirical evidence on the effects of productive complexity on eco‐innovation, including potential bidirectional dynamics; (3) examines the regulatory, policy, and incentive frameworks conditioning this relationship; (4) identifies the technological, institutional, and human capabilities underpinning eco‐innovation and productive upgrading; (5) investigates the role of international integration and cross‐country cooperation; and (6) assesses the environmental outcomes associated with complexity trajectories and green innovation. Methodologically, the analysis combines a bibliometric analysis with a systematic literature review encompassing 79 peer‐reviewed articles published between 2014 and 2024. The results indicate consistent evidence that economic complexity positively influences eco‐innovation, while the reverse mechanism—eco‐innovation fostering productive sophistication—remains underexplored, with only one study explicitly addressing bidirectionality. The literature also shows that eco‐innovation contributes to reducing CO <jats:sub>2</jats:sub> emissions, although findings on the environmental effects of economic complexity are mixed. Institutional quality, productive capabilities, and global linkages emerge as central drivers of both higher complexity and improved environmental performance. Although patents are widely used as proxies for eco‐innovation, few studies treat them as the primary unit of analysis, and comparative work across countries or between green and conventional innovation remains limited. Overall, the study identifies substantial conceptual and empirical gaps and underscores the need for more integrated and interdisciplinary research to advance understanding of the interplay between eco‐innovation, productive sophistication, and sustainability.","PeriodicalId":9518,"journal":{"name":"Business Strategy and The Environment","volume":"32 1","pages":""},"PeriodicalIF":13.4,"publicationDate":"2026-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147495265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As global warming intensifies, climate risks' impact on firm value has become a critical concern for academia and investors. This systematic literature review analyzes 50 event studies in this research field, classifying them by climate risk type. The analysis indicates that physical risk events predominantly result in negative abnormal stock returns for affected firms, whereas transition risk events tend to penalize the stock prices of carbon‐intensive firms and reward those of low‐carbon ones. The review's findings emphasize the role of investor overreaction, driven by salience bias, as a significant factor beyond traditional market efficiency considerations. Four propositions are derived, and eight research opportunities to address gaps in the understanding of stock market reactions to climate risk events are identified. This review encourages investors and firms alike to enhance climate risk management and advances research by synthesizing the current field through a theory‐guided organizing framework that considers a range of factors influencing investor responses to salient climate risks.
{"title":"Stock Market Reactions to Climate Risk Events: A Systematic Literature Review and Research Agenda","authors":"Mario Schuster, Rainer Lueg","doi":"10.1002/bse.70773","DOIUrl":"https://doi.org/10.1002/bse.70773","url":null,"abstract":"As global warming intensifies, climate risks' impact on firm value has become a critical concern for academia and investors. This systematic literature review analyzes 50 event studies in this research field, classifying them by climate risk type. The analysis indicates that physical risk events predominantly result in negative abnormal stock returns for affected firms, whereas transition risk events tend to penalize the stock prices of carbon‐intensive firms and reward those of low‐carbon ones. The review's findings emphasize the role of investor overreaction, driven by salience bias, as a significant factor beyond traditional market efficiency considerations. Four propositions are derived, and eight research opportunities to address gaps in the understanding of stock market reactions to climate risk events are identified. This review encourages investors and firms alike to enhance climate risk management and advances research by synthesizing the current field through a theory‐guided organizing framework that considers a range of factors influencing investor responses to salient climate risks.","PeriodicalId":9518,"journal":{"name":"Business Strategy and The Environment","volume":"13 1","pages":""},"PeriodicalIF":13.4,"publicationDate":"2026-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147495267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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