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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.

The aim of this study is to define strategies to increase green nuclear energy investments. Within this context, a model is suggested that includes two different stages. Firstly, the consensus-based fuzzy preferences are determined for these investments. For this purpose, four different TRIZ-based strategies are identified, which have an influence on the effectiveness of these projects. Furthermore, in the second stage, the significance weights of these strategies are calculated by considering the spherical fuzzy DEMATEL approach. The findings indicate that dynamicity is the most appropriate TRIZ strategy for the effectiveness of green nuclear energy investments. In addition, the prior action is another significant strategy that should also be considered in these projects. It is obvious that sufficient technological infrastructure is very important for the performance of green nuclear power plants and there is a rapid development of this technology. Because of this situation, these nuclear power plants should be designed effectively so that they can be adoptable to any changes in the technology. In this scope, qualified personnel should be employed, and communication between departments should be effective and of high quality. With the help of these issues, technological changes can be adopted more easily.

Abstract: Second-generation biofuels, starting from lignocellulosic biomass, are considered as a renewable alternative for fossil fuels with lower environmental impact and potentially higher supply and energy security. The economic and environmental performance of second-generation bioethanol production from corn stover in the European Union (EU) is studied, starting in Belgium as base case. A comparative environmental techno-economic assessment has been conducted, with process simulations in Aspen Plus and corn stover availability data in thirteen EU countries to calculate minimum ethanol selling prices (MESP) and Greenhouse gas emissions (GHGe). In this analysis, the emphasis is on the comparison of different pretreatment technologies, namely (i) dilute acid, (ii) alkaline, (iii) steam explosion and (iv) liquid hot water. Dilute acid showed the best economic and environmental performance for the base case scenario. Within the EU, Hungary and Romania presented the lowest MESP for the steam explosion model at 0.39 and 0.43 EUR/L respectively. Poland showed the lowest GHGe, at 0.46 kg CO2eq/L for the alkaline model, mainly due to the avoided product allocation on electricity and its high carbon intensity in the electricity generation sector. The second lowest GHGe were obtained in France for the dilute acid model and are attributed to its low agricultural emissions intensity. This study identifies a location-dependence of the economic and environmental performance of pretreatment technologies, which can be extrapolated from the EU to other large regions around the world and should be taken into consideration by decision-makers.

Green energy technologies (GETs) are environmentally friendly in nature, making a promising contribution to attaining net-zero carbon goals. Although the Pakistani government has begun using GETs to minimize the adverse effects of carbon emissions, consumers' adoption rate is quite low. There are few studies examining consumers' desire to adopt GETs in the country. This study attempts to fill this research gap and also contributes by adding three novel factors to the theory of planned behavior (i.e., green energy technology awareness, openness to experience, and green energy technology discomfort) to comprehensively analyze the impact of various factors influencing consumers' desire to adopt GETs. For this purpose, the study establishes a systematic research framework. Data were collected from (n = 330) households in the five major cities (Peshawar, Abbottabad, Mardan, Mingora, and Swabi) of Khyber Pakhtunkhwa Province via an inclusive questionnaire survey. The formulated hypotheses are evaluated and scrutinized using structural equation modeling. The results reveal that environmental concern (β = 0.245), green energy technology awareness (β = 0.362), openness to experience (β = 0.256), and green energy technology benefits (β = 0.225) positively affect consumers' desire to adopt GETs. On the other hand, green energy technology costs (β = 0.325) and green energy technology discomfort (β = 0.395) have a negative effect on consumers' adoption of GETs. The research findings emphasize the importance of increasing recognition of GETs, reforming policy frameworks, and providing budget-friendly and user-friendly technologies. Research limitations and future research perspectives are also addressed.

This review highlights the importance of controlling the crystallization dynamics for the deposition of high-quality photovoltaic perovskite layers on larger-area coatings.

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.

Wind energy projects provide clean energy so that they should be increased to reach the sustainable development goals of the countries. However, current decision-making process should be improved for the effectiveness of these projects. Thus, critical factors should be considered to understand the significant indicators of the performance of the wind energy projects. This article aims to determine the factors that should be considered when deciding on wind energy investments. In this context, 9 different criteria belonging to 3 dimensions (project, firm, market) are determined based on literature review. Later, an analysis is carried out by using hesitant interval-valued intuitionistic fuzzy (IVIF) Decision Making Trial and Evaluation Laboratory (DEMATEL) to identify the most important factors. Furthermore, 4 different investment strategies in Boston Consultancy Group (BCG) matrix have been determined as alternatives. To determine which of these strategies is suitable for wind energy investments, the hesitant IVIF multi-objective optimization on the basis of ratio analysis (MOORA) method has been considered. Additionally, a comparative evaluation is also performed by using technique for order preference by similarity to ideal solution (TOPSIS) methodology. Similarly, sensitivity analysis is also made by considering 9 different cases. The analysis results of different methodologies are quite similar which shows the coherency and reliability of the findings. It is concluded that firm-based factors play the most significant role. It is also identified that technical development, financial performance and organizational effectiveness are the most significant criteria to make investment decision on wind energy projects. Furthermore, due to the market growth potential, it is recommended that wind energy investors increase their investments and strengthen their position in the market.

Climate change and rising global temperatures pose significant challenges for natural resource management. While developed economies have made progress in addressing these issues, emerging economies are still striving to achieve carbon neutrality, sustainable resource use, and environmental sustainability. This research aims to identify the factors driving carbon emissions in emerging economies over the past three decades. The study establishes a long-run relationship among the factors under investigation by employing various panel diagnostic methods. Non-parametric approaches are used to account for the non-symmetric distribution of panel data. The findings reveal that natural resource components have asymmetric impacts on carbon emissions, with oil rents reducing emissions and mineral rents increasing them. Economic growth and agricultural value added are identified as significant contributors to carbon emissions in the region. On the other hand, renewable energy consumption plays a crucial role in achieving carbon neutrality targets. Gross capital formation exhibits a mixed influence on carbon emissions, being positive and significant in lower quantiles and significantly negative in upper quantiles. These estimates are robust and align with existing literature. The study recommends sustainable resource abstraction and utilization, renewable energy production and consumption improvements, and enhanced capital formation. By providing empirical evidence and policy recommendations, this research contributes to understanding the relationship between these factors and their impact on carbon emissions, facilitating effective strategies for sustainable development and environmental preservation. Xinyang Normal University