A decision-support framework for evaluating AI-enabled ESG strategies in the context of sustainable manufacturing systems

Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).

AI-Powered Predictive Analytics and Sustainable Supply Chain Optimization are closely related.

“AI-Powered Predictive Analytics and Sustainable Supply Chain Optimization are closely related”

• AssumptionRests on an assumption: The seven selected AI-enabled ESG strategies are the relevant and comprehensive set for sustainable manufacturing.
• AssumptionRests on an assumption: Expert judgments used in the fuzzy TOPSIS analysis are reliable and can be meaningfully aggregated.

May combine two claims.

The paper identifies Renewable Energy Integration, AI-Powered Predictive Analytics, and Sustainable Supply Chain Optimization as critical ESG-focused alternatives.

“Further, it identifies critical ESG-focused alternatives: Renewable Energy Integration, AI-Powered Predictive Analytics, and Sustainable Supply Chain Optimization.”

Classified as probably not a substantial claim. May combine two claims.

The rankings obtained from the Fuzzy TOPSIS framework indicate the most effective alternatives and clarify priorities for action measures.

“The rankings, obtained as a result of application of the Fuzzy TOPSIS framework, indicate the most effective alternatives which provide more clarification around priorities for action measures.”

Probably not a substantial claim: may not pass S2 (checkable).

Results of the current research give a comprehensive overview of the assessment of ESG-driven strategies as a result of applying the Fuzzy TOPSIS method.

“Results of the current research give a comprehensive overview of the assessment of ESG-driven strategies as a result of applying the Fuzzy TOPSIS method.”

Probably not a substantial claim: may not pass S3 (load-bearing).

AI outcomes can have discriminatory bias resulting from biased data, model design faults, or societal inequalities in training data.

“This can result from biased data, faults in the design of the model used, or societal inequalities where inbuilt into training data sets.”

• cites [38] Díaz-Rodríguez, N. et al. Connecting the Dots in trustworthy artificial intelligence: from AI principles, ethics, and key requirements to responsible AI systems and regulation. Inform. Fusion. 99, 101896 (2023). sourceNot checked yet

  Not checked in the free run.

• cites [39] Akinrinola, O., Okoye, C. C., Ofodile, O. C. & Ugochukwu, C. E. Navigating and reviewing ethical dilemmas in AI development: strategies for transparency, fairness, and accountability. GSC Adv. Res. Reviews. 18 (3), 050–058 (2024). sourceNot checked yet

  Not checked in the free run.

• cites [40] Memarian, B. & Doleck, T. Fairness, accountability, transparency, and ethics (FATE) in artificial intelligence (AI), and higher education: A systematic review. Computers Education: Artif. Intelligence, 100152. (2023). sourceNot checked yet

  Not checked in the free run.

May combine two claims.

Using blockchain for capturing transactions and other activities means those transactions and activities will always be transparent, verifiable, and easily traced.

“Hence, using blockchain for capturing transactions and all other activities means that the transactions and other activities will always be transparent, verifiable, and easily traced.”

• cites [48] Almadadha, R. Blockchain technology in financial accounting: enhancing transparency, security, and ESG reporting. Blockchains 2 (3), 312–333 (2024). sourceNot checked yet

  Not checked in the free run.

• cites [49] Vezér, M. et al. An ESG lens on blockchain and public equities. In Morningstar Sustainalytics Report. (2022).Not checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S4 (based).

This research presents a scalable paradigm for evaluating ESG strategies across industries.

“This research presents a scalable paradigm for evaluating ESG strategies across industries”

• AssumptionRests on an assumption: The manufacturing sector case study is representative enough to support the framework's scalability to other industries.

May combine two claims.

Organizations can transform traditional waste management practices into more efficient, automated, data-driven processes that reduce waste, optimize resource recovery, and minimize environmental impacts by incorporating AI and IoT.

“Organizations can transform traditional practices of waste management into more efficient, automated, and data-driven processes that not only reduce waste but also optimize resource recovery and minimize environmental impacts by incorporating AI and IoT technologies.”

• cites [46] Gupta, P. K., Shree, V., Hiremath, L. & Rajendran, S. The use of modern technology in smart waste management and recycling: artificial intelligence and machine learning. Recent Adv. Comput. Intell., 173–188. (2019). sourceNot checked yet

  Not checked in the free run.

• cites [47] Fang, B., Yu, J., Chen, Z., Osman, A. I., Farghali, M., Ihara, I., Yap, P. S. (2023).Artificial intelligence for waste management in smart cities: a review. Environ. Chem. Lett. 21 (4), 1959–1989. sourceNot checked yet

  Not checked in the free run.

May combine two claims.

The integration of AI enables waste management systems to analyze IoT data for better planning, including predicting waste generation and performing resource planning.

“The integration of AI enables this information to be analyzed for better planning by optimizing the work of waste management systems in predicting waste generation and performing resource planning.”

• cites [46] Gupta, P. K., Shree, V., Hiremath, L. & Rajendran, S. The use of modern technology in smart waste management and recycling: artificial intelligence and machine learning. Recent Adv. Comput. Intell., 173–188. (2019). sourceNot checked yet

  Not checked in the free run.

• cites [47] Fang, B., Yu, J., Chen, Z., Osman, A. I., Farghali, M., Ihara, I., Yap, P. S. (2023).Artificial intelligence for waste management in smart cities: a review. Environ. Chem. Lett. 21 (4), 1959–1989. sourceNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S3 (load-bearing).

Bias is a key ethical issue for AI.

“Bias is a key ethical issue for AI - sometimes, the outcome of a computation can have an unacceptably discriminatory bias.”

• cites [38] Díaz-Rodríguez, N. et al. Connecting the Dots in trustworthy artificial intelligence: from AI principles, ethics, and key requirements to responsible AI systems and regulation. Inform. Fusion. 99, 101896 (2023). sourceNot checked yet

  Not checked in the free run.

• cites [39] Akinrinola, O., Okoye, C. C., Ofodile, O. C. & Ugochukwu, C. E. Navigating and reviewing ethical dilemmas in AI development: strategies for transparency, fairness, and accountability. GSC Adv. Res. Reviews. 18 (3), 050–058 (2024). sourceNot checked yet

  Not checked in the free run.

• cites [40] Memarian, B. & Doleck, T. Fairness, accountability, transparency, and ethics (FATE) in artificial intelligence (AI), and higher education: A systematic review. Computers Education: Artif. Intelligence, 100152. (2023). sourceNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S2 (checkable), S4 (based).

Fuzzy TOPSIS enhances the analysis process through accurate ranking and better alignment with the sustainability goals in organizational practices by considering objective as well as subjective criteria of alternatives.

“Fuzzy TOPSIS enhances the analysis process through accurate ranking and better alignment with the sustainability goals in organizational practices by considering objective as well as subjective criteria of alternatives.”

• AssumptionRests on an assumption: Expert judgments used in the fuzzy TOPSIS analysis are reliable and can be meaningfully aggregated.

May combine two claims.

In environmental sustainability, blockchain can be applied to trace origins of raw materials, follow up the environmental impact of processes, and check the legitimacy of sustainability claims.

“In environmental sustainability, for instance, it can be applied to trace origins of raw materials; subsequently, follow-up the environmental impact of processes adopted by any given plant, and finally, check for legitimacy in claims regarding sustainability.”

• cites [48] Almadadha, R. Blockchain technology in financial accounting: enhancing transparency, security, and ESG reporting. Blockchains 2 (3), 312–333 (2024). sourceNot checked yet

  Not checked in the free run.

• cites [49] Vezér, M. et al. An ESG lens on blockchain and public equities. In Morningstar Sustainalytics Report. (2022).Not checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing). May combine two claims.

AI and other advanced technologies enable organizations to rethink traditional processes, reduce environmental and social impacts, and address global sustainability problems.

“Here, organizations are enabled to rethink traditional processes as well as reduce their environmental and social impacts and introduce breathtaking solutions to address the problems of global sustainability directly.”

• cites [30] Matinaro, V. & Liu, Y. Towards increased innovativeness and sustainability through organizational culture: A case study of a Finnish construction business. J. Clean. Prod. 142, 3184–3193 (2017). sourceNot checked yet

  Not checked in the free run.

• cites [31] Carayannis, E. G., Sindakis, S. & Walter, C. Business model innovation as lever of organizational sustainability. J. Technol. Transf. 40, 85–104 (2015). sourceNot checked yet

  Not checked in the free run.

May combine two claims.

AI-driven energy management systems use machine learning algorithms to forecast renewable energy availability and reallocate energy distribution to ensure constant supply and reduce non-renewable energy use.

“Such systems use machine learning algorithms to forecast when the sun or wind will be available, and energy distribution is then appropriately re-allocated to ensure a constant supply and diminish the use of non-renewable energy sources43–45.”

• cites [43] Sims, R., Mercado, P. E. & Krewitt, W. Integration of renewable energy into present and future energy systems. (2011). sourceNot checked yet

  Not checked in the free run.

• cites [44] Weitemeyer, S., Kleinhans, D., Vogt, T. & Agert, C. Integration of renewable energy sources in future power systems: the role of storage. Renew. Energy. 75, 14–20 (2015). sourceNot checked yet

  Not checked in the free run.

• cites [45] Zakeri, B., Syri, S. & Rinne, S. Higher renewable energy integration into the existing energy system of Finland–Is there any maximum limit? Energy 92, 244–259 (2015). sourceNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

Transparent decision-making encourages stakeholder participation and a feeling of ownership.

“It encourages participation by stakeholders and a feeling of ownership as well34–36.”

• cites [34] Efunniyi, C. P. et al. Strengthening corporate governance and financial compliance: enhancing accountability and transparency. Finance Acc. Res. J. 6 (8), 1597–1616 (2024). sourceNot checked yet

  Not checked in the free run.

• cites [35] Hale, T. N. Transparency, accountability, and global governance. Global governance, 73–94. (2008). sourceNot checked yet

  Not checked in the free run.

• cites [36] Fung, B. The demand and need for transparency and disclosure in corporate governance. Univers. J. Manage. 2 (2), 72–80 (2014). sourceNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S4 (based). May combine two claims.

Most work focused on one dimension of ESG or an industry-specific approach, under conventional MCDM techniques without totally utilizing AI to refine and optimize decisions.

“Most work focused on one dimension of ESG or at worst, an industry-specific approach, under conventional MCDM techniques without totally utilizing AI to refine and optimize decisions.”

May combine two claims.

IoT devices in waste bins, recycling containers, and along waste collection routes can track waste accumulation levels, monitor waste types, and generate real-time data on waste collection patterns.

“These IoT devices may be designed to track the level of waste accumulation, monitor the types of waste collected, and generate real-time data regarding waste collection patterns.”

• cites [46] Gupta, P. K., Shree, V., Hiremath, L. & Rajendran, S. The use of modern technology in smart waste management and recycling: artificial intelligence and machine learning. Recent Adv. Comput. Intell., 173–188. (2019). sourceNot checked yet

  Not checked in the free run.

• cites [47] Fang, B., Yu, J., Chen, Z., Osman, A. I., Farghali, M., Ihara, I., Yap, P. S. (2023).Artificial intelligence for waste management in smart cities: a review. Environ. Chem. Lett. 21 (4), 1959–1989. sourceNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S3 (load-bearing).

AI can be used in education to facilitate more student-centered and adapted learning experiences, especially in regions deprived of adequate quality education.

“In this sense, AI will be used in education to facilitate the development of more student-centered and adapted learning experiences, especially for regions where students are deprived of adequate quality education.”

• cites [50] Grant, P. & Basye, D. Personalized Learning: A Guide for Engaging Students with Technology (International Society for Technology in Education, 2014).Not checked yet

  Not checked in the free run.

• cites [51] Reynolds, R. A. Achieving Equity for All Students: Culturally Relevant Project-based Learning and the Academic Achievement Gap (Doctoral dissertation, Northeastern University Boston). (2024).Not checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S2 (checkable), S4 (based).

More reliable and robust results are achieved by the suggested framework that deals with linguistic variables and imprecision in expert judgments using fuzzy set theory.

“More reliable and robust results are achieved by the suggested framework that deals with linguistic variables and imprecision in expert judgments using fuzzy set theory.”

• AssumptionRests on an assumption: Expert judgments used in the fuzzy TOPSIS analysis are reliable and can be meaningfully aggregated.

Probably not a substantial claim: may not pass S4 (based).

The current study suggests a thorough framework for ranking AI-enabled ESG strategies in manufacturing using Fuzzy TOPSIS that takes into consideration the ambiguity of human judgements and guarantees consistency in decision-making.

“In order to solve this, the current study suggests a thorough framework for ranking AI-enabled ESG strategies in manufacturing using Fuzzy TOPSIS, which takes into consideration the ambiguity of human judgements and guarantees consistency in decision-making.”

• AssumptionRests on an assumption: The seven selected AI-enabled ESG strategies are the relevant and comprehensive set for sustainable manufacturing.
• AssumptionRests on an assumption: Expert judgments used in the fuzzy TOPSIS analysis are reliable and can be meaningfully aggregated.

Classified as probably not a substantial claim. May combine two claims.

Data intelligence and operational efficiency have strategic value in improving sustainability practices.

“emphasizing the strategic value of data intelligence as well as operational efficiency in improving sustainability practices.”

• AssumptionRests on an assumption: The seven selected AI-enabled ESG strategies are the relevant and comprehensive set for sustainable manufacturing.
• AssumptionRests on an assumption: Expert judgments used in the fuzzy TOPSIS analysis are reliable and can be meaningfully aggregated.

Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).

Renewable energy integration is in line with the industry's pivotal role in advancing energy transition and climate action.

“which is in line with the industry’s pivotal role in advancing energy transition and climate action.”

• AssumptionRests on an assumption: The seven selected AI-enabled ESG strategies are the relevant and comprehensive set for sustainable manufacturing.
• AssumptionRests on an assumption: Expert judgments used in the fuzzy TOPSIS analysis are reliable and can be meaningfully aggregated.

Probably not a substantial claim: may not pass S2 (checkable). May combine two claims.

The findings underscore the positive impacts AI may have on ESG decision-making processes and on developing a culture of sustainability and accountability within organizations.

“The findings thus underscore the positive impacts AI may have not only on ESG decision-making processes but also on developing a culture of sustainability and accountability within organizations.”

• AssumptionRests on an assumption: The seven selected AI-enabled ESG strategies are the relevant and comprehensive set for sustainable manufacturing.
• AssumptionRests on an assumption: Expert judgments used in the fuzzy TOPSIS analysis are reliable and can be meaningfully aggregated.

May combine two claims.

Transparency from blockchain breeds confidence among stakeholders and supports right compliance by giving organisations an opportunity to report their sustainability efforts accurately.

“Transparency not only breeds confidence among stakeholders but also supports the right compliance by giving organisations an opportunity to report their sustainability efforts accurately48,49.”

• cites [48] Almadadha, R. Blockchain technology in financial accounting: enhancing transparency, security, and ESG reporting. Blockchains 2 (3), 312–333 (2024). sourceNot checked yet

  Not checked in the free run.

• cites [49] Vezér, M. et al. An ESG lens on blockchain and public equities. In Morningstar Sustainalytics Report. (2022).Not checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

The decentralized, distributed ledger system of blockchain stores data in an immutable manner and shares data safely and securely.

“The decentralized, distributed ledger system of blockchain stores data in an immutable manner and shares data safely and securely.”

• cites [48] Almadadha, R. Blockchain technology in financial accounting: enhancing transparency, security, and ESG reporting. Blockchains 2 (3), 312–333 (2024). sourceNot checked yet

  Not checked in the free run.

• cites [49] Vezér, M. et al. An ESG lens on blockchain and public equities. In Morningstar Sustainalytics Report. (2022).Not checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S4 (based).

This study bridges the gap by applying a fuzzy TOPSIS approach for ESG performance evaluation that is more robust, consistent and reliable.

“This study bridges the gap by applying a fuzzy TOPSIS approach for ESG performance evaluation, which considers the uncertainties of expert judgments and avoids the risk of making decisions through a more robust, consistent and reliable framework.”

• AssumptionRests on an assumption: The seven selected AI-enabled ESG strategies are the relevant and comprehensive set for sustainable manufacturing.
• AssumptionRests on an assumption: Expert judgments used in the fuzzy TOPSIS analysis are reliable and can be meaningfully aggregated.

Probably not a substantial claim: may not pass S3 (load-bearing).

Organizations can show compliance towards sustainability standards such as carbon emissions cut, natural resource conservation, and renewable power resources utilisation because blockchain records every step of the product life cycle from sourcing through manufacturing to distribution.

“It is also possible that the organisations show compliance towards sustainability standards such as carbon emissions cut, natural resource conservation, and renewable power resources utilisation because blockchain technology records every step of the life cycle of the product from sourcing through manufacturing to distribution.”

• cites [48] Almadadha, R. Blockchain technology in financial accounting: enhancing transparency, security, and ESG reporting. Blockchains 2 (3), 312–333 (2024). sourceNot checked yet

  Not checked in the free run.

• cites [49] Vezér, M. et al. An ESG lens on blockchain and public equities. In Morningstar Sustainalytics Report. (2022).Not checked yet

  Not checked in the free run.

May combine two claims.

Generative AI product design can reduce raw material usage, lower production costs, and reduce environmental impacts.

“This can lead to a strong reduction in the usage of raw materials, lowering the costs of production, and reducing environmental impacts due to the creation of new products55,56.”

• cites [55] Rane, N. L., Kaya, O. & Rane, J. Advancing industry 4.0, 5.0, and society 5.0 through generative artificial intelligence like ChatGPT. Artificial Intelligence, Machine Learning, and Deep Learning for Sustainable Industry, 5, 2. (2024). sourceNot checked yet

  Not checked in the free run.

• cites [56] Alzoubi, Y. I. & Mishra, A. Green artificial intelligence initiatives: potentials and challenges. J. Clean. Prod. 143090. (2024). sourceNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S4 (based). May combine two claims.

AI can predict waste generation patterns and suggest reuse or sustainable alternatives within a circular economy, reducing total environmental impact.

“This keeps the circle small and resource “looping” so resources are reused and recycled instead of thrown away, which makes the AI system predict the waste generation patterns and suggest strategies of reuse or shifting towards sustainable alternatives within a circular economy. This is because it will generally decrease the environmental impact supply chain activities have in total.”

Probably not a substantial claim: may not pass S2 (checkable), S4 (based). May combine two claims.

Generative AI holds potential to drive sustainable innovation by enabling businesses to design green products, optimize operations, and reduce resource consumption.

“Generative AI holds a lot of potential in driving sustainable innovation through the ability of businesses to design green products, optimize various operations, and reduce consumption of resources.”

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

AI chatbots, virtual tutors, and automated assessment systems can provide learners with instant feedback, personalized guidance, and continuous support without direct face-to-face instruction.

“By using AI chatbots, virtual tutors, and automated assessment systems, learners will receive instant feedback, personalized guidance, and continuous support even when they are not getting direct face-to-face instruction.”

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

AI-powered platforms can facilitate real-time data exchange between suppliers, manufacturers, and distributors to collaborate on common sustainability goals.

“Real-time data exchange between suppliers, manufacturers, and distributors through AI-powered platforms can work together on common sustainability goals.”

Probably not a substantial claim: may not pass S4 (based). May combine two claims.

AI-driven supply chain management contributes to cost reduction, increased transparency, and ethical resource use, driving a shift toward more sustainable and resilient supply chains.

“Thus, AI-driven supply chain management contributes to cost reduction and increased transparency and, in fact, leads to ethical resource use, which will further drive a shift toward more sustainable and resilient supply chains that can benefit businesses and the entire society.”

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

Blockchain and AI analytics can establish transparent supply chains with traceability from raw material origin to every stage.

“Through blockchain and AI analytics, companies can establish clear supply chains that are transparent with traceability from the origin of raw materials down to every stage.”

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

Generative AI can optimize energy use in buildings by generating designs that consider natural lighting and heating/cooling systems to reduce reliance on artificial energy.

“For example, AI can optimize energy use in buildings by generating designs while considering factors like natural lighting and heating and cooling systems to reduce reliance on artificial forms of energy.”

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

AI can design programs targeted at the specific needs of marginalized groups such as women, refugees, and people with disabilities for education, healthcare, and employment opportunities.

“AI is instrumental for the design of programs targeted at the specific needs of marginalized sections of society, such as women, refugees, and those with disabilities, towards education, health care, and employment opportunities that could previously have remained inaccessible.”

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

AI-driven diagnostic tools can improve diagnosis by health practitioners where medical experience is deficient.

“AI-driven diagnostic tools can also improve the proper diagnosis to be made by health practitioners when there is a deficiency of medical experience in some populations.”

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.

AI can optimize waste management within supply chains by identifying opportunities to reduce packaging waste, optimize recycling, and minimize material waste.

“Another function that AI can perform is optimization of waste management within supply chains. Analyzing production and logistics data, AI can spot opportunities such as reducing packaging waste, optimizing recycling processes, and minimizing material waste at all levels within the supply chain.”

Probably not a substantial claim: may not pass S3 (load-bearing).

AI can design adaptive learning systems that self-adjust lesson difficulty according to individual performance.

“Artificial intelligence could, for example, be used to design adaptive learning systems that self-adjust the difficulty of lessons according to individual performance, so everybody faces appropriate challenges and struggles appropriately.”

• cites [50] Grant, P. & Basye, D. Personalized Learning: A Guide for Engaging Students with Technology (International Society for Technology in Education, 2014).Not checked yet

  Not checked in the free run.

• cites [51] Reynolds, R. A. Achieving Equity for All Students: Culturally Relevant Project-based Learning and the Academic Achievement Gap (Doctoral dissertation, Northeastern University Boston). (2024).Not checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.

Generative AI can facilitate exploitation of renewable energy sources by streamlining energy consumption and helping optimally exploit renewable energy.

“AI can further be used to facilitate the exploitation of renewable energy sources by streamlining the consumption of energy in all directions within a firm and helping to optimally exploit renewable energy.”

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

AI-based employment tools can match employees with job openings by matching applicant profiles and employer expectations.

“AI-based employment tools can match the right employees with the proper job openings by matching applicant profiles and employer expectations to propose suitable job placements and matching.”

May combine two claims.

Generative AI is a strong development tool in green innovations, allowing businesses to design sustainable products and optimize processes concerning operative consumption levels.

“Generative AI is coming out as a strong development tool in green innovations, allowing businesses to design sustainable products and optimize processes concerning operative consumption levels.”

• cites [55] Rane, N. L., Kaya, O. & Rane, J. Advancing industry 4.0, 5.0, and society 5.0 through generative artificial intelligence like ChatGPT. Artificial Intelligence, Machine Learning, and Deep Learning for Sustainable Industry, 5, 2. (2024). sourceNot checked yet

  Not checked in the free run.

• cites [56] Alzoubi, Y. I. & Mishra, A. Green artificial intelligence initiatives: potentials and challenges. J. Clean. Prod. 143090. (2024). sourceNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.

AI can make supply chains more transparent and visible, enabling businesses to track goods, inventory levels, and environmental effects throughout the supply chain.

“It can also make supply chains more transparent and more visible, which enables businesses to have insights into the movement of goods and track inventory levels while assessing environmental effects throughout the entire supply chain.”

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

AI in healthcare can develop solutions suited to the specific needs of underprivileged populations.

“AI in healthcare can revolutionize community health services through developing solutions that suit the specific needs of underprivileged populations.”

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

AI-enhanced workforce and community development can enhance social inclusion and empowerment.

“Another critical feature of AI-enhanced workforce and community development is that it provides a capacity to enhance social inclusion and empowerment.”

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.

AI can analyze public health records for trend identification, health outcome prediction, and optimizing resource allocation for efficient and fair healthcare services.

“Use of AI can also be implemented in studying the massive public health records for trend identification, health outcome prediction, and optimizing resource allocation so as to efficiently and fairly provide healthcare services.”

Probably not a substantial claim: may not pass S4 (based).

AI can reduce energy usage and waste in production by using machine learning to predict and readjust production schedules to optimize energy and raw material usage.

“Thirdly, AI can be used in the production process through the reduction of energy usage and waste. Organizations could explore machine learning algorithms that may be used in predicting and readjusting production schedules to optimize energy raw material usage in resource usage.”

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

Generative AI can design electronics with easily removable parts, allowing consumers to upgrade or repair devices instead of replacing them.

“For instance, generative AI can design electronics with easy removable parts, thereby allowing consumers to upgrade or repair devices instead of replacing them totally.”

Probably not a substantial claim: may not pass S3 (load-bearing).

Generative AI can support circular economy models by designing products for disassembly, recycling, and reuse, such as modular components that are replaceable or repurposable.

“Another predominant role of generative AI would be to sustain circular economy models, focused on reuse, recycling, and reduction. AI algorithms can participate in the development of products while considering their last life: The capabilities to disassemble them after they are used and recycle them can become important. Such designs might make use of modular components that are either replaceable”

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

AI can be leveraged to develop portals that involve larger numbers of currently unserved people directly in policy-making and program development.

“AI can be leveraged for developing portals that can involve increasingly larger numbers of those who are currently unserved more directly in policy-making and program development work issues that affect their lives.”

Probably not a substantial claim: may not pass S4 (based). May combine two claims.

AI can analyze sensor and equipment data in real time to identify inefficient energy consumption and recommend adjustments to reduce waste and energy costs.

“AI may analyze sensor and equipment data to know in real-time the level of consumption of energy, know areas that are inefficient, and recommend adjustments to reduce waste and energy costs.”

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.

Improved job matching by AI can ease unemployment rates and support local economic development and long-term meaningful careers.

“With improved job matching, AI can ease unemployment rates, hence supporting local economic development and the ability to lay out long-term meaningful careers.”

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

AI-powered platforms can process individual learning styles, identify strengths and weaknesses, and provide context-specific content to help students progress at their own pace.

“AI-powered platforms may process individual ways of learning, note areas of strength and weakness, and provide context-specific content to help a student move forward at a pace he or she finds convenient for the self.”

• cites [50] Grant, P. & Basye, D. Personalized Learning: A Guide for Engaging Students with Technology (International Society for Technology in Education, 2014).Not checked yet

  Not checked in the free run.

• cites [51] Reynolds, R. A. Achieving Equity for All Students: Culturally Relevant Project-based Learning and the Academic Achievement Gap (Doctoral dissertation, Northeastern University Boston). (2024).Not checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

AI can predict manufacturer demand to avoid overproduction and associated material and energy waste.

“AI can better predict the demand of manufacturers and avoid overproducing and associated material and energy waste.”

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

AI-powered solutions can optimize transport routes to reduce fuel consumption and carbon footprints by analyzing traffic, weather, and logistics data.

“AI-powered solutions can optimize the route so that less fuel is consumed and load is planned with minimal carbon footprints. AI can read through the history of traffic patterns, weather conditions, and logistic data to present the best transport routes in terms of road time and fuel use.”

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

AI can create virtual classrooms or online learning programs that reach remote or marginalized areas with limited access to traditional educational institutions.

“In addition to this, AI can be useful in the creation of virtual classrooms or online learning programs that reach out to remote or marginalized areas where access to traditional educational institutions is limited.”

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.

AI analytics can educate local leaders on local needs, design targeted interventions, and measure their efforts.

“Analytics run through AI can educate these leaders on the local needs, designing targeted interventions, and the measurement of their efforts.”

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.

Generative AI can design more efficient factory layouts, optimize equipment usage, and predict maintenance needs to minimize downtime and resource waste.

“An example of the use of generative AI involves the design of more efficient factory layouts, optimizing equipment usage, and predicting maintenance needs so that machines operate at their peak while minimizing downtime and waste of resources.”

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

Generative AI can design products made solely of recyclable materials to minimize environmental impact of production and disposal.

“Similarly, generative AI can be used to come up with products that are solely made of recyclable materials in order to minimize the environmental impact of their production and disposal.”

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

Generative AI can improve operational efficiency by analyzing production data to identify gaps in energy, water, and raw material usage and suggest adjustments to reduce waste.

“Besides product design, generative AI could lead to improved efficiency in operations, such as helping optimize manufacturing processes by conserving the usage of resources. Analyzing production data, AI algorithms may identify gaps in areas concerning energy, water, and raw materials usage to suggest possible adjustments that would help streamline the operation and reduce waste.”

Classified as probably not a substantial claim.

AI predictive capability helps manage sustainability-related risks by enabling real-time operational adjustments to reduce adverse environmental or social impacts.

“This predictive capability becomes highly useful in managing sustainability-related risks, as it enables the business to make real-time adjustments in operations in terms of reducing adverse environmental or social impacts.”

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.

Use of data in AI community solutions ensures resources are saved, programs suit local realities, and individuals are owners of the process.

“The use of data in that way ensures resources are saved, community programs are put in place to suit local realities, and individuals are carried along in being owners of the process in solutions undertaken for their benefit.”

Probably not a substantial claim: may not pass S3 (load-bearing).

AI-powered solutions can assess an individual's existing skill-set, career vision, and local in-demand skills to provide specific training and certification programs linked to employment opportunities.

“AI-powered solutions can assess an individual’s existing skill-set, career vision, and in-demand skills in their locality to provide specific training and certification programs that link up with particular employment opportunities.”

• cites [52] Tavakoli, M. et al. An AI-based open recommender system for personalized labor market driven education. Adv. Eng. Inform. 52, 101508 (2022). sourceNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

AI can make reporting and documentation easier for companies to monitor sustainability performance and submit accurate reports to regulators and stakeholders.

“Secondly, AI can make reporting and documentation easier for companies to monitor their sustainability performance and submit accurate reports to regulators and other stakeholders.”

Probably not a substantial claim: may not pass S4 (based).

AI can evaluate the environmental impact of sourcing to help organizations choose suppliers that support sustainability objectives.

“Another area where AI can benefit any business is the evaluation of the environmental impact from sourcing. Organizations will know whom they should utilize as suppliers, who supports their sustainability objectives, and where best to source.”

Probably not a substantial claim: may not pass S4 (based).

Generative AI can design sustainable products and processes that ensure compliance with environmental certification requirements and international sustainability standards.

“This is also supported by generative AI by designing sustainable products and processes that ensure businesses comply with any regulatory requirements for environmental certifications. These can be evaluated as per international sustainability standards with AI suggesting improvements through energy efficiency or carbon emissions reduction to secure certification.”

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.

Generative AI can support sustainable packaging solutions by optimizing packaging material and structure to reduce packaging amount, eliminate waste, and use environmentally friendly materials.

“Further, it supports the development of sustainable packaging solutions. Generative AI helps reduce the amount of packaging used, eliminate unnecessary waste, and use environmentally friendly materials through optimal designs of packaging materials and structures.”

May combine two claims.

Embedding AI in supply chain management helps reach sustainability targets by minimizing operational costs and enhancing organizational performance.

“The embedding of AI in supply chain management aids in the reaching of sustainability targets because it minimizes operational costliness and enhances organizational performance53,54.”

• cites [53] Singh, P. K. Digital transformation in supply chain management: artificial intelligence (AI) and machine learning (ML) as catalysts for value creation. Int. J. Supply Chain Manage. 12 (6), 57–63 (2023). sourceNot checked yet

  Not checked in the free run.

• cites [54] Riad, M., Naimi, M. & Okar, C. Enhancing supply chain resilience through artificial intelligence: developing a comprehensive conceptual framework for AI implementation and supply chain optimization. Logistics 8 (4), 111 (2024). sourceNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S4 (based).

AI-led tools can help ensure supply chain activities comply with evolving environmental and social regulations and standards.

“Finally, other related sustainable practice is compliance with evolving regulations and standards on sustainability, through AI-led tools for support. With the tight regulations implemented by the world’s governments and organizations on the environmental and social aspects within businesses, AI can help in ensuring that the activities conducted in each supply chain are within compliance.”

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

Personalized AI educational tools can bridge the gap for students in underserved communities by making one-size-fits-all teaching inefficient, improving learning and equitable education.

“These personalized educational tools can bridge the gap for students in underserved communities by rendering one-size-fits-all teaching methods inefficient, so students are able to learn better and experience a more equitable education50,51.”

• cites [50] Grant, P. & Basye, D. Personalized Learning: A Guide for Engaging Students with Technology (International Society for Technology in Education, 2014).Not checked yet

  Not checked in the free run.

• cites [51] Reynolds, R. A. Achieving Equity for All Students: Culturally Relevant Project-based Learning and the Academic Achievement Gap (Doctoral dissertation, Northeastern University Boston). (2024).Not checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S4 (based). May combine two claims.

AI can analyze supplier data and track raw materials to verify labor rights and environmental regulation compliance.

“AI can be used in analyzing supplier data and tracking raw materials. This way, labor rights and environmental regulations compliance can be verified.”

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.

AI can ensure suppliers maintain high performance and meet sustainability criteria by providing live feedback on supplier progress.

“AI can help ensure that suppliers maintain a high level of performance and do meet the basic criterion that is necessary for sustainability and provide live feedback regarding the progress of a supplier.”

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.

AI can alert companies to new regulatory requirements and help them adjust processes to meet current standards.

“In case of continuous updates in regulatory changes, AI will alert companies to new requirements and help them adjust their processes to meet such current standards.”

Probably not a substantial claim: may not pass S3 (load-bearing).

AI can analyze labor market trends to identify in-demand skills and recommend them to individuals, positioning them in more sustainable employment.

“For example, AI can analyze labor market trends to identify a list of in-demand skills and then recommend it to an individual based on those recommendations, thereby positioning the individual in a more sustainable employment position.”

• cites [52] Tavakoli, M. et al. An AI-based open recommender system for personalized labor market driven education. Adv. Eng. Inform. 52, 101508 (2022). sourceNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

Generative AI can design sustainable materials such as biodegradable plastics or alternative fibers by simulating molecular forms and testing environmental resilience.

“Generative AI can also aid in the designing of sustainable materials, such as biodegradable plastics or alternative fibers, through the simulation of the form of molecules and experimenting with their environmental resilience.”

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.

Generative AI can identify energy-efficient manufacturing methods to reduce waste energy and maximize output.

“Similarly, it is able to identify the energy-efficient ways of manufacturing through generative AI, where waste energy is reduced to maximum output.”

Classified as probably not a substantial claim.

Fuzzy TOPSIS is applied to assess multiple ESG strategies to address complexity and uncertainty in decision-making.

“In this paper, the Fuzzy Technique for Order Preference by Similarity to Ideal Solution (Fuzzy TOPSIS) is applied in order to assess multiple Environmental, Social, and Governance (ESG) strategies at present to address the complexity and uncertainty inherent in decision-making.”

Classified as probably not a substantial claim. May combine two claims.

Fuzzy TOPSIS integrates fuzzy logic into the classic TOPSIS framework, allowing consideration of vagueness and providing a robust evaluation process.

“Therefore, to overcome these aforementioned limitations, Fuzzy TOPSIS integrates fuzzy logic into the classic TOPSIS framework. This allows for the consideration of vagueness in assessments and, therefore, provides for a robust process of evaluation.”

• AssumptionRests on an assumption: Traditional decision-making methods generally fail to capture subjective judgments or imprecise data typically present in ESG criteria evaluations.

Probably not a substantial claim: may not pass S3 (load-bearing).

AI-based learning systems can enable real-time workplace learning, helping individuals adapt to changing economic conditions.

“AI-based learning systems can also, in real time, give room to a person to learn new skills at their place of work, therefore being ready to shift and adapt amid ever-changing economic conditions52.”

• cites [52] Tavakoli, M. et al. An AI-based open recommender system for personalized labor market driven education. Adv. Eng. Inform. 52, 101508 (2022). sourceNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

AI can optimize healthcare access by developing telemedicine platforms that remotely connect exposed populations with healthcare professionals.

“AI can also be used to optimize healthcare access by developing telemedicine platforms that remotely connect exposed populations with healthcare professionals.”

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

AI enables community-driven solutions where local leaders and organizations use data to identify and solve specific community problems.

“AI also enables solutions being driven by the community where local leaders and organizations can leverage data to identify and solve specific community problems.”

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

AI can trace raw material sourcing conditions to ensure balanced compensation to workers and environmental maintenance.

“For example, AI can trace out the conditions in which the raw material is sourced so that a balanced compensation to the workers is given and the environment is maintained.”

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.

AI-driven systems can analyze individual health data to propose personalized treatment programs and track progress for early identification of health risks.

“AI-driven systems would be able to analyze all data regarding individual health so that personalized treatment programs could be proposed and progress tracked for early identification of potential risks towards health.”

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

AI can predict the onset of chronic conditions such as diabetes or hypertension by analyzing health indicators and lifestyles, allowing intervention before expensive medical problems.

“For example, by using AI, the onset of chronic conditions such as diabetes or hypertension can be predicted when such health indicators combined with lifestyles are analyzed, hence allowing health care providers to intervene before expensive medical problems are introduced.”

Probably not a substantial claim: may not pass S4 (based).

AI systems can analyze supplier data on cost, delivery time, quality, and sustainability performance to recommend the best suppliers for each product or service.

“AI systems can analyze the significant supplier data concerning cost, delivery time, quality, and sustainability performance, thereby recommending the best suppliers for each product or service.”

Probably not a substantial claim: may not pass S3 (load-bearing).

Fuzzy TOPSIS has become one of the most prominent methods for solving MCDM problems.

“It has become one of the most prominent methods for solving MCDM problems59,60.”

• cites [59] Zyoud, S. H. & Fuchs-Hanusch, D. A bibliometric-based survey on AHP and TOPSIS techniques. Expert Syst. Appl. 78, 158–181 (2017). sourceNot checked yet

  Not checked in the free run.

• cites [60] Aydoğan, H. & Ozkir, V. A fermatean fuzzy MCDM method for selection and ranking problems: case studies. Expert Syst. Appl. 237, 121628 (2024). sourceNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

AI predictive approaches can reduce health inequalities among underserved populations by enabling early diagnosis, individualized treatment, and continuous monitoring in resource-limited settings.

“Such predictive approaches can even eliminate health inequalities among the underserved by achieving early diagnosis, individualized treatment, and continuous monitoring of health that would otherwise not be achieved in resource-limited settings.”

May combine two claims.

Generative AI demand forecasting reduces overproduction, saving resources and avoiding environmental costs of excess inventory including storage, transportation, and disposal.

“This not only saves resources but also ensures that the firm remains free of environmental costs of excess inventory, including storage, transportation, and disposal57,58.”

• cites [57] Mahat, D. et al. AI-driven optimization of supply chain and logistics in mechanical engineering. In 2023 10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON) 10, (1611–1616). IEEE. (2023) sourceNot checked yet

  Not checked in the free run.

• cites [58] Lodhi, S. K., Gill, A. Y. & Hussain, H. K. Green innovations: artificial intelligence and sustainable materials in production. BULLET: Jurnal Multidisiplin Ilmu. 3 (4), 492–507 (2024).Not checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.

AI-based predictive analytics can predict supply chain disruptions such as delays, shortages, or bottlenecks and enable remedial measures ahead of time.

“Using AI-based predictive analytics, organizations can predict disruptions in the supply chain - be it delays, shortages, or bottlenecks - and take remedial measures ahead of their time.”

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

AI algorithms can optimize freight scheduling by combining shipments into fewer trips, decreasing transportation emissions.

“Another form of application of AI algorithms is in the optimization of freight scheduling; shipments could be combined to fewer trips which thereby decrease the emission level related to transportation.”

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

The study is static and sector-specific.

“Furthermore, the study is static and sector-specific.”

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

Under challenging environmental circumstances, hybrid MCDM-blockchain models support operational resilience, strategic alignment, and risk reduction.

“Under challenging environmental circumstances, these models support operational resilience, strategic alignment and risk reduction71–74.”

• cites [71] Dehshiri, S. J. H. & Amiri, M. Evaluation of blockchain implementation solutions in the sustainable supply chain: A novel hybrid decision approach based on Z-numbers. Expert Syst. Appl. 235, 121123 (2024). sourceNot checked yet

  Not checked in the free run.

• cites [72] Dehshiri, S. J. H., Amiri, M., Mostafaeipour, A. & Le, T. Integrating blockchain and strategic alliance in renewable energy supply chain toward sustainability: A comparative decision framework under uncertainty. Energy 304, 132136 (2024). sourceNot checked yet

  Not checked in the free run.

• cites [73] Varriale, V., Cammarano, A., Michelino, F. & Caputo, M. Critical analysis of the impact of artificial intelligence integration with cutting-edge technologies for production systems. J. Intell. Manuf. 36 (1), 61–93 (2025). sourceNot checked yet

  Not checked in the free run.

• cites [74] Dehshiri, S. J. H., Amiri, M. & Bamakan, S. M. H. Evaluating the blockchain technology strategies for reducing renewable energy development risks using a novel integrated decision framework. Energy 289, 129987 (2024). sourceNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S3 (load-bearing).

Hybrid strategies combining MCDM under uncertainty with blockchain technology have proven beneficial in resolving implementation issues in sustainable supply chains and renewable energy.

“Particularly, hybrid strategies that combine MCDM under uncertainty with blockchain technology have proven to be very beneficial in resolving implementation issues in sustainable supply chains and renewable energy.”

• cites [71] Dehshiri, S. J. H. & Amiri, M. Evaluation of blockchain implementation solutions in the sustainable supply chain: A novel hybrid decision approach based on Z-numbers. Expert Syst. Appl. 235, 121123 (2024). sourceNot checked yet

  Not checked in the free run.

• cites [72] Dehshiri, S. J. H., Amiri, M., Mostafaeipour, A. & Le, T. Integrating blockchain and strategic alliance in renewable energy supply chain toward sustainability: A comparative decision framework under uncertainty. Energy 304, 132136 (2024). sourceNot checked yet

  Not checked in the free run.

• cites [73] Varriale, V., Cammarano, A., Michelino, F. & Caputo, M. Critical analysis of the impact of artificial intelligence integration with cutting-edge technologies for production systems. J. Intell. Manuf. 36 (1), 61–93 (2025). sourceNot checked yet

  Not checked in the free run.

• cites [74] Dehshiri, S. J. H., Amiri, M. & Bamakan, S. M. H. Evaluating the blockchain technology strategies for reducing renewable energy development risks using a novel integrated decision framework. Energy 289, 129987 (2024). sourceNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S2 (checkable).

The methodology used in this research works well in providing a platform to mold the complexities and subjectivities of ESG assessments.

“The methodology used here works well in providing an ample platform to mold the complexities and subjectivities of ESG assessments.”

May combine two claims.

AI-Powered Predictive Analytics and Sustainable Supply Chain Optimization ranked among their peers, underscoring the importance of embracing cutting-edge technologies for efficiency, resource conservation, and responsible practice.

“Other options, including AI-Powered Predictive Analytics and Sustainable Supply Chain Optimization ranked among their peers, underscore the importance of embracing cutting-edge technologies as the primary steps in improving efficiency, conserving more resources, and promoting responsible practice.”

Probably not a substantial claim: may not pass S2 (checkable). May combine two claims.

This paper complements existing literature by proposing a specific application within the ESG field, thereby closing the gap between theoretical development and practical implementation.

“The work of this paper complements existing literature by proposing a specific application within the ESG field, thereby closing the gap that exists between some theoretical development and real practical implementation.”

Probably not a substantial claim: may not pass S3 (load-bearing).

Recent research has proposed solutions to AI-enabled ESG implementation problems, including hybrid data governance models, explainable AI models, stakeholder training, and staged digital transformation plans.

“Recent research have proposed solutions to these problems, including the adoption of hybrid data governance models, explainable AI models, stakeholder training and staged digital transformation plans68–70.”

• cites [68] Nookala, G. Adaptive data governance frameworks for data-driven digital transformations. J. Comput. Innovation, 4(1). (2024).Not checked yet

  Not checked in the free run.

• cites [69] Brunetti, F. et al. Digital transformation challenges: strategies emerging from a multi-stakeholder approach. TQM J. 32 (4), 697–724 (2020). sourceNot checked yet

  Not checked in the free run.

• cites [70] Papagiannidis, E., Enholm, I. M., Dremel, C., Mikalef, P. & Krogstie, J. Toward AI governance: identifying best practices and potential barriers and outcomes. Inform. Syst. Front. 25 (1), 123–141 (2023). sourceNot checked yet

  Not checked in the free run.

• AssumptionRests on an assumption: AI-enabled ESG initiatives face implementation issues in actual production settings, including integration of diverse data sources, data availability and dependability, reluctance to accept new technologies, and high upfront expenditures.

Probably not a substantial claim: may not pass S3 (load-bearing).

Lower-ranked alternatives Blockchain for Transparent Governance (A4) and AI-Enhanced Workforce and Community Development (A5) present niche yet useful contributions to particular dimensions of ESG.

“Lower-ranked alternatives, for instance Blockchain for Transparent Governance (A4) and AI-Enhanced Workforce and Community Development (A5), present niche yet useful contributions to particular dimensions of ESG”

Probably not a substantial claim: may not pass S3 (load-bearing).

The top ranking of Renewable Energy Integration (A2) resonates with global sustainability priorities calling for organizations to shift toward renewable energy systems to reduce carbon footprints and mitigate climate change.

“This result resonates with global sustainability priorities – an increasing call for organizations to shift toward renewable energy systems in order to reduce carbon footprints and mitigate the impact of climate change.”

May combine two claims.

Renewable Energy Integration is the most impactful alternative, highlighting energy transition to cleaner sources as a major answer to environmental challenges.

“Alternative scenarios for the field of Renewable Energy Integration disclose an alternative that is the most impactful, highlighting energy transition to cleaner sources as one of the major answers toward environmental challenges.”

May combine two claims.

The third ranking of Sustainable Supply Chain Optimization (A6) emphasizes emission reduction and ethical operation in supply chains, which stakeholders strictly check.

“Ranking in third place is Sustainable Supply Chain Optimization (A6), emphasizing this even more crucial aspect of emission reduction, the operation of ethics in the supply chains, nowadays strictly checked by stakeholders.”

May combine two claims.

Generative AI for Eco-Friendly Innovation (A7) and Smart Waste Management Systems (A3) have moderate impact on ESG goals.

“suggesting their moderate impact on ESG goals”

Probably not a substantial claim: may not pass S2 (checkable). May combine two claims.

These findings are critical in guiding decisions aligned with ESG objectives and reinforce the importance of integrating AI-driven solutions within sustainable practices.

“Such findings are, therefore critical in guiding towards such decisions which align with environmental, social, and governance objectives, and which, in a sense, reinforce the importance of integrating AI-driven solutions within sustainable practices.”

Probably not a substantial claim: may not pass S2 (checkable), S4 (based). May combine two claims.

Incorporating fuzzy MCDM into the evaluation process provides a strong basis for slow but successful integration of ESG in sustainable manufacturing systems, permitting structured decision-making even in unpredictable situations.

“A strong basis for the slow but successful integration of ESG in sustainable manufacturing systems is provided by the incorporation of fuzzy MCDM into the evaluation process, which permits structured decision-making even in unpredictable situations.”

• AssumptionRests on an assumption: ESG considerations are inherently complex and their multifaceted interconnection does not fit traditional evaluation frameworks, creating a gap in effective decision-making tools.

Classified as probably not a substantial claim.

This paper provides a holistic framework for ESG performance ranking and prioritization through Fuzzy TOPSIS to account for complexities and uncertainty in sustainability decision-making.

“This paper provides a holistic framework for ESG performance ranking and prioritization through the application of the Fuzzy TOPSIS method to account for inherent complexities and uncertainty in sustainability decision-making.”

May combine two claims.

Blockchain for Transparent Governance (A4) and AI-Enhanced Workforce and Community Development (A5) have relatively lesser, albeit significant, roles in the prioritized alternatives.

“indicating their relatively lesser, albeit significant, roles in the prioritized alternatives”

Probably not a substantial claim: may not pass S2 (checkable).

The outcome of this study reasserts the potential of integrating fuzzy logic and AI-driven approaches to positively drive sustainable development.

“The outcome of this study is the reassertion of the potential of integrating fuzzy logic and AI-driven approaches to positively drive sustainable development, ensuring that organizations will contribute effectively towards a resilient and equitable future.”

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

The lower-ranked options are less relevant but still offer inspiration in niche ESG aspects such as governance transparency and community development.

“The lower-ranked options are less relevant, but not without their own inspiration in niche aspects of ESG, like governance transparency and community development, which form significant facets of effective sustainability.”

Probably not a substantial claim: may not pass S4 (based).

Fuzzy TOPSIS compensates for uncertainty and imprecision in subjective judgments, especially for ESG evaluation, which is often dominated by qualitative judgments.

“This would compensate all kinds of uncertainty and imprecision found in subjective judgments, especially for the evaluation of ESG, since it is often dominated by qualitative judgments.”

Classified as probably not a substantial claim.

Applying fuzzy logic to TOPSIS makes it possible to systematically rank and evaluate alternatives.

“Applying fuzzy logic principles to the more conventional framework developed as TOPSIS makes it possible to systematically rank and evaluate alternatives.”

May combine two claims.

The findings underscore the diverse potential of AI-driven initiatives, with renewable energy integration and predictive analytics emerging as the most impactful alternatives in advancing ESG objectives.

“These findings underscore the diverse potential of AI-driven initiatives, with renewable energy integration and predictive analytics emerging as the most impactful alternatives in advancing ESG objectives.”

May combine two claims.

AI-Powered Predictive Analytics (A1) has potential in forecasting environmental risks and optimizing operations.

“highlighting its potential in forecasting environmental risks and optimizing operations”

Classified as probably not a substantial claim.

The study provides an identifiable and reproducible framework for the assessment of ESG.

“The significance of this study lies in the possibility of providing an identifiable and reproducible framework for the assessment of ESG.”

Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing). May combine two claims.

The approach used here strengthens the evaluation process and increases the importance of incorporating more advanced decision-making tools into sustainability-focused initiatives.

“The approach used here not only strengthens the evaluation process but also increases the importance of incorporating more advanced decision-making tools into sustainability-focused initiatives65–67.”

• cites [65] Dehshiri, S. J. H. & Amiri, M. Evaluating challenges of implementing industry 5.0 in the renewable energy supply chain toward sustainability: A comparative analysis using decision-making approaches. Energy Strategy Rev.. 59, 101761 (2025). sourceNot checked yet

  Not checked in the free run.

• cites [66] Masudin, I., Habibah, I. Z., Wardana, R. W., Restuputri, D. P. & Shariff, S. S. R. Enhancing supplier selection for sustainable Raw materials: A comprehensive analysis using analytical network process (ANP) and TOPSIS methods. Logistics 8 (3), 74 (2024). sourceNot checked yet

  Not checked in the free run.

• cites [67] Dehshiri, S. J. H., Mostafaeipour, A. & Amiri, M. Evaluating challenges of circular economy and internet of things in renewable energy supply chain through a hybrid decision-making framework. J. Environ. Manage. 370, 122785 (2024). sourceNot checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S3 (load-bearing).

Recent research supports the importance of combining cutting-edge decision-making frameworks with new technology to improve sustainability performance, consistent with the study's findings.

“Recent research supports the importance of combining cutting-edge decision-making frameworks with new technology to improve sustainability performance, which is consistent with the study’s findings.”

• cites [71] Dehshiri, S. J. H. & Amiri, M. Evaluation of blockchain implementation solutions in the sustainable supply chain: A novel hybrid decision approach based on Z-numbers. Expert Syst. Appl. 235, 121123 (2024). sourceNot checked yet

  Not checked in the free run.

• cites [72] Dehshiri, S. J. H., Amiri, M., Mostafaeipour, A. & Le, T. Integrating blockchain and strategic alliance in renewable energy supply chain toward sustainability: A comparative decision framework under uncertainty. Energy 304, 132136 (2024). sourceNot checked yet

  Not checked in the free run.

• cites [73] Varriale, V., Cammarano, A., Michelino, F. & Caputo, M. Critical analysis of the impact of artificial intelligence integration with cutting-edge technologies for production systems. J. Intell. Manuf. 36 (1), 61–93 (2025). sourceNot checked yet

  Not checked in the free run.

• cites [74] Dehshiri, S. J. H., Amiri, M. & Bamakan, S. M. H. Evaluating the blockchain technology strategies for reducing renewable energy development risks using a novel integrated decision framework. Energy 289, 129987 (2024). sourceNot checked yet

  Not checked in the free run.

• AssumptionRests on an assumption: AI-enabled ESG initiatives face implementation issues in actual production settings, including integration of diverse data sources, data availability and dependability, reluctance to accept new technologies, and high upfront expenditures.

Classified as probably not a substantial claim.

The second ranking of AI-Powered Predictive Analytics (A1) affirms an increasing need for AI technological resources in predicting environmental risk to optimize resource utilization and drive data-based sustainability strategies.

“The importance of A1 – AI-Powered Predictive Analytics, the second highest ranked, affirms an increasing need for reliance on AI technological resources in predicting environmental risk to optimize resource utilization, thereby driving data-based sustainability strategies.”

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

The model relies on expert inputs, which could be biased and might not represent viewpoints from different industries.

“The model relies on expert inputs, which could be biased by nature and might not represent viewpoints from different industries.”

Classified as probably not a substantial claim. May combine two claims.

The structured Fuzzy TOPSIS approach promotes systematic and transparent evaluation, ensuring selection of strategies that best match ESG principles and sustainable development goals.

“Thus, this structured approach promotes both systematic and transparent evaluation processes, ensuring selection according to selected strategies that best match with ESG principles and sustainable development goals.”

Probably not a substantial claim: may not pass S2 (checkable), S4 (based). May combine two claims.

The overall soundness of the Fuzzy TOPSIS approach ensures consistency in diverse decision-making scenarios, making it scalable to a wide range of organizational needs and contexts.

“The overall soundness of the Fuzzy TOPSIS approach ensures consistency in diverse scenarios of decision-making, which makes it scalable towards the wide range of organizational needs and contexts.”

Classified as probably not a substantial claim.

By quantifying qualitative evaluations and coordinating them with sustainability goals, the framework offers an organized method for assessing AI-enabled ESG solutions.

“Operationally, by quantifying qualitative evaluations and coordinating them with sustainability goals, the framework offers an organised method for assessing AI-enabled ESG solutions.”

Probably not a substantial claim: may not pass S4 (based).

Comparing the present study with previous works using AHP, VIKOR, or WASPAS reveals the specific capabilities of the fuzzy logic approach in dealing with linguistic appraisals and imprecise data.

“Comparing the present study with previous works employing other MCDM methodologies, such as AHP, VIKOR, or WASPAS, reveals the specific capabilities of the fuzzy logic approach in dealing with linguistic appraisals and with imprecise data.”

Probably not a substantial claim: may not pass S2 (checkable), S4 (based). May combine two claims.

The technique is flexible enough for real-world decision-making in sustainable manufacturing systems because its dual perspective guarantees conceptual soundness and practical applicability.

“The technique is flexible enough to be used in real-world decision-making in sustainable manufacturing systems because of this dual perspective, which guarantees both conceptual soundness and practical applicability.”

Probably not a substantial claim: may not pass S3 (load-bearing).

Sustainable Supply Chain Optimization (A6) emphasizes the importance of AI-driven logistics and ethical sourcing in supply chain management.

“emphasizing the importance of AI-driven logistics and ethical sourcing in supply chain management”

Probably not a substantial claim: may not pass S2 (checkable).

Adoption of AI-driven and fuzzy logic methodologies could help improve decision-making within ESG contexts.

“The wider implications of the study are in fact that adoption of AI-driven and fuzzy logic methodologies could help flourish decision-making within ESG contexts.”

Probably not a substantial claim: may not pass S4 (based).

Fuzzy TOPSIS is viable in domains where fuzziness and imprecision are predominant.

“It is a fairly viable method to be applied in domains where fuzziness and imprecision are predominant, so called as fuzzy environment.”