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Abstract The aim of this study is to evaluate the incremental innovation performance of nuclear energy projects. Within this context, a novel model is generated which consists of two different stages, and large nuclear reactors are taken into consideration. Firstly, the Pythagorean fuzzy DEMATEL is used to weight the phases of technology S-Curve for nuclear energy projects. Moreover, the second stage includes the ranking two-generation technology S-curve with integer patterns for nuclear energy projects. In this framework, the best combinations are selected for innovation life cycle pattern with the integer code series. The findings demonstrate that the nuclear energy companies need to consider the two-generation technology S-Curve because continuous technological developments are occurring for nuclear power generation. It is also determined that aging in the first generation is the most significant period of two-generation technology S-Curve for nuclear energy projects. In this process, critical decisions should be made regarding future technological investments. In addition, the growth phase in the second generation is also important for the effectiveness of the nuclear energy technology. Conducting effective evaluations in these processes will contribute to increasing the efficiency of companies.

Abstract The aim of this study is to evaluate the incremental innovation performance of nuclear energy projects. Within this context, a novel model is generated which consists of two different stages, and large nuclear reactors are taken into consideration. Firstly, the Pythagorean fuzzy DEMATEL is used to weight the phases of technology S-Curve for nuclear energy projects. Moreover, the second stage includes the ranking two-generation technology S-curve with integer patterns for nuclear energy projects. In this framework, the best combinations are selected for innovation life cycle pattern with the integer code series. The findings demonstrate that the nuclear energy companies need to consider the two-generation technology S-Curve because continuous technological developments are occurring for nuclear power generation. It is also determined that aging in the first generation is the most significant period of two-generation technology S-Curve for nuclear energy projects. In this process, critical decisions should be made regarding future technological investments. In addition, the growth phase in the second generation is also important for the effectiveness of the nuclear energy technology. Conducting effective evaluations in these processes will contribute to increasing the efficiency of companies.

Abstract This study aims to analyze the innovation strategies for the green supply chain management with QFD (quality function deployment) multidimensionally. The novelty of the study is to define the criteria of green supply chain for each stage of QFD and propose a hybrid model by IVIF (interval-valued intuitionistic fuzzy) DEMATEL (decision making trial and evaluation laboratory) and IVIF MOORA (Multi-Objective Optimization by Ratio Analysis) respectively. The results demonstrate that understanding the customer expectations with customer relation management is the most important innovation strategy for the green supply chain management in energy industry with the consecutive stages of QFD whereas benchmarking the competitive market environment has relatively the last seat in the ranking. Hence, it is recommended that energy companies should have an effective customer relationship management. In this context, these companies should make a detailed analysis to learn what their customers directly expect from them. With the help of this issue, these companies should generate their product and services based on these expectations. Additionally, it is also stated that new service and product development is also essential for energy companies to improve their innovativeness. For this purpose, a research and development department should be created, and the qualified people should be employed. Additionally, different opinions should be collected from various parties, such as customers, employees, and suppliers. Since customers who are satisfied will prefer these companies, the energy companies can catch the opportunity to increase their market share.

Abstract This study aims to analyze the innovation strategies for the green supply chain management with QFD (quality function deployment) multidimensionally. The novelty of the study is to define the criteria of green supply chain for each stage of QFD and propose a hybrid model by IVIF (interval-valued intuitionistic fuzzy) DEMATEL (decision making trial and evaluation laboratory) and IVIF MOORA (Multi-Objective Optimization by Ratio Analysis) respectively. The results demonstrate that understanding the customer expectations with customer relation management is the most important innovation strategy for the green supply chain management in energy industry with the consecutive stages of QFD whereas benchmarking the competitive market environment has relatively the last seat in the ranking. Hence, it is recommended that energy companies should have an effective customer relationship management. In this context, these companies should make a detailed analysis to learn what their customers directly expect from them. With the help of this issue, these companies should generate their product and services based on these expectations. Additionally, it is also stated that new service and product development is also essential for energy companies to improve their innovativeness. For this purpose, a research and development department should be created, and the qualified people should be employed. Additionally, different opinions should be collected from various parties, such as customers, employees, and suppliers. Since customers who are satisfied will prefer these companies, the energy companies can catch the opportunity to increase their market share.

To ensure business continuity in the post-COVID-19 era, decision-makers should reconfigure their traditional supply chain (SC) networks, assisted by the development and implementation of cutting-edge technologies. Recently, Industry 5.0 (I5.0) has gained increasing attention as a paradigm offering salient features for the creation of resilient and inclusive operations by ensuring long-standing SC sustainability. However, there has been insufficient analysis of the challenges of implementing I5.0 in SCs. Therefore, this study aims to understand the challenges of implementing I5.0 when managing the impact of SC disruptions due to the COVID-19 pandemic in an emerging economy. Both qualitative and quantitative methods were used for this research. First, the challenges to I5.0 implementation were identified through a literature review and experts’ feedback. Those challenges were examined and prioritised using the Best-Worst Method (BWM). Second, the contextual relationships amongst these challenges were analysed using interpretive structural modelling (ISM) with cross-impact matrix multiplication applied to classification (MICMAC) analysis. Findings showed that to adopt I5.0 initiatives successfully in order to manage the post-COVID-19 impact on SC sustainability, the active involvement of senior managers is required in the execution process. Findings also showed that financial support and funding (e.g., tax reduction, short loans, etc.) from investors and the government play a pivotal role in enabling sustainability in SCs. Finally, the challenges were classified using MICMAC analysis to provide valuable insights for managing future disruptions. This study is expected to help managers and decision-makers successfully overcome the challenges of implementing I5.0 in SCs and thus improve SC sustainability.

To ensure business continuity in the post-COVID-19 era, decision-makers should reconfigure their traditional supply chain (SC) networks, assisted by the development and implementation of cutting-edge technologies. Recently, Industry 5.0 (I5.0) has gained increasing attention as a paradigm offering salient features for the creation of resilient and inclusive operations by ensuring long-standing SC sustainability. However, there has been insufficient analysis of the challenges of implementing I5.0 in SCs. Therefore, this study aims to understand the challenges of implementing I5.0 when managing the impact of SC disruptions due to the COVID-19 pandemic in an emerging economy. Both qualitative and quantitative methods were used for this research. First, the challenges to I5.0 implementation were identified through a literature review and experts’ feedback. Those challenges were examined and prioritised using the Best-Worst Method (BWM). Second, the contextual relationships amongst these challenges were analysed using interpretive structural modelling (ISM) with cross-impact matrix multiplication applied to classification (MICMAC) analysis. Findings showed that to adopt I5.0 initiatives successfully in order to manage the post-COVID-19 impact on SC sustainability, the active involvement of senior managers is required in the execution process. Findings also showed that financial support and funding (e.g., tax reduction, short loans, etc.) from investors and the government play a pivotal role in enabling sustainability in SCs. Finally, the challenges were classified using MICMAC analysis to provide valuable insights for managing future disruptions. This study is expected to help managers and decision-makers successfully overcome the challenges of implementing I5.0 in SCs and thus improve SC sustainability.

Additional efforts will be needed by European countries to improve the energy efficiency, as with current trends the 20% objective will be missed. Small and medium-sized enterprises (SMEs) manufacturing sector is a promising field, as SMEs are less energy-efficient than larger enterprises. Several studies investigated the barriers to the diffusion of technologies and practices for industrial energy efficiency, but little attention has been paid to understand the factors affecting the perception of such barriers by SMEs. In this multiple case-study, we have investigated 20 Primary Metal manufacturing SMEs in Northern Italy. Economic and information barriers are perceived as the major issues. Interestingly, firm's size, innovativeness of the market in which enterprises operate, as well as product and process innovation are factors affecting barriers to energy efficiency. Differences have been observed within SMEs, especially for information and competence-related barriers. In particular, a more innovative external context in which enterprises operate and a greater production process complexity seem to reduce barriers. Moreover, more product innovative enterprises seem to have a lower perception of behavioral and technology-related barriers. The results of this exploratory investigation provide useful suggestions for policy design and further research on industrial energy efficiency.

Additional efforts will be needed by European countries to improve the energy efficiency, as with current trends the 20% objective will be missed. Small and medium-sized enterprises (SMEs) manufacturing sector is a promising field, as SMEs are less energy-efficient than larger enterprises. Several studies investigated the barriers to the diffusion of technologies and practices for industrial energy efficiency, but little attention has been paid to understand the factors affecting the perception of such barriers by SMEs. In this multiple case-study, we have investigated 20 Primary Metal manufacturing SMEs in Northern Italy. Economic and information barriers are perceived as the major issues. Interestingly, firm's size, innovativeness of the market in which enterprises operate, as well as product and process innovation are factors affecting barriers to energy efficiency. Differences have been observed within SMEs, especially for information and competence-related barriers. In particular, a more innovative external context in which enterprises operate and a greater production process complexity seem to reduce barriers. Moreover, more product innovative enterprises seem to have a lower perception of behavioral and technology-related barriers. The results of this exploratory investigation provide useful suggestions for policy design and further research on industrial energy efficiency.

Achieving environmental sustainability has become a global concern amidst increasing climate change threat. Using quarterly frequency data for the case of Russia from 1992 to 2018, the present study explores the interaction between disaggregated energy consumption (renewable energy and non-renewable energy), trade flow and economic growth on a broader measure for environmental degradation (ecological footprint). The choice of the variables draws strength from initiative of the United Nations Sustainable Development Goals (UN-SDG, 7, 8 11 and 13) for responsible energy consumption and clean energy consumption while mitigating climate change issues. The study applied the quantile-on-quantile regression (QQR) and nonparametric causality-in-quantiles to capture these associations. The outcomes from the QQR disclosed that in the majority of the quantiles, trade openness and renewable energy use contribute to environmental sustainability, while nonrenewable energy amplifies ecological footprint. Furthermore, growth in Russia escalates its ecological footprint. Moreover, in the majority of the quantiles, all the exogenous variables can predict ecological footprint. Given the outcomes of this study, it outlines the need for a paradigm shift for alternative and clean energy consumption in Russian energy mix amidst its economic growth trajectory while accounting for green-development approaches. Pathways to fully achieve the sustainability targets are carefully outlined in the concluding section.

Achieving environmental sustainability has become a global concern amidst increasing climate change threat. Using quarterly frequency data for the case of Russia from 1992 to 2018, the present study explores the interaction between disaggregated energy consumption (renewable energy and non-renewable energy), trade flow and economic growth on a broader measure for environmental degradation (ecological footprint). The choice of the variables draws strength from initiative of the United Nations Sustainable Development Goals (UN-SDG, 7, 8 11 and 13) for responsible energy consumption and clean energy consumption while mitigating climate change issues. The study applied the quantile-on-quantile regression (QQR) and nonparametric causality-in-quantiles to capture these associations. The outcomes from the QQR disclosed that in the majority of the quantiles, trade openness and renewable energy use contribute to environmental sustainability, while nonrenewable energy amplifies ecological footprint. Furthermore, growth in Russia escalates its ecological footprint. Moreover, in the majority of the quantiles, all the exogenous variables can predict ecological footprint. Given the outcomes of this study, it outlines the need for a paradigm shift for alternative and clean energy consumption in Russian energy mix amidst its economic growth trajectory while accounting for green-development approaches. Pathways to fully achieve the sustainability targets are carefully outlined in the concluding section.

Abstract Scenario-based studies agree that the technical potential for CO2 emissions reduction from the housing stock is large. This paper explores how a market might be developed for the refurbishment activities assumed in these scenarios, taking the existing market for repair, maintenance and improvement (RMI) as its starting point. Interviews with 16 small and medium-sized enterprises (SMEs) in the construction industry reveal the interdependence of products, practices and processes in housing renovation activities. Conservative practice as well as innovation can be understood as the outcome of multi-lateral influences on firms from other firms, clients, the material buildings and products in their working lives, and from regulations and regulators. Contractors' openness to innovation is contingent on an informal approach to risk assessment, taking account of cost, time efficiency, client demands, and installer confidence in the reliability of the resulting work. The implications of the research are discussed in relation to the need for new practices and processes on refurbishment projects, raising questions for future research on key questions of quality assurance, performance over time, the application of standards, and vocational training.