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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.

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

AbstractIn the wake of the growing threats to humanity from climate change, we analyzed the information and communications technology (ICT)/education—environmental nexus from three distinct blocs including BRICS, MINT, and the G7 economies between 1990 and 2020. Two models were examined to reach the study's objectives. The first model evaluates whether education and ICT are essential for environmental sustainability via potential reduction in carbon emission. On the other hand, the second model fills an existing gap in extant studies by examining the prospect of education and ICT in influencing citizens on the importance of transition to renewable energy usage. Driscoll and Kraay estimator was employed as a panacea tool for cross‐sectional dependence and slope homogeneity while the fixed effect approach provides sufficient robustness checks on the findings. While some outcomes vary per bloc, others are relatively similar across the three blocs. Education level in school enrollment perspectives shows a negative significant pollution reduction effect across the three blocs, while only the G7 bloc performed better from human capital perspectives. The combined sample bloc shows that ICT also significantly reduces carbon emission, however, an individual bloc analysis refutes this stance for the MINT bloc. Additionally, while renewable energy cushions emissions in all the blocs, rapid urbanization, shows a positive CO2 emission impact except in the G7 bloc. Last, ICT and education significantly boost renewable energy usage only in the G7. Hence, governments and stakeholders in the blocs should gravitate toward greater investments in quality education and greener ICT infrastructures for a sustainable environment.

Abstract Urban transformation is vital to global sustainable development as humans increasingly come to dwell in cities. Within cities, the mobility sector promises the highest potential of carbon emission reduction. The disruptive business innovation brought about by the advent of app-based smart-sharing systems is emancipating collaborative consumption of mobility at larger and deeper scales, ranging from car-pooling, expanded electric vehicle (EV) use to bike-sharing. Synchronizing the existing yet under-realized low-carbon transport modes in cities, such as public transport, with emerging and diversifying app-based sharing mobility business models, offers huge potential to transform urban mobility toward sustainability. Yet, the rapid business expansion and innovation of the sharing mobility companies have profoundly challenged existing socio-economic relationships, knowledge systems and physical infrastructures in cities. This study explores the synergy between the social-ecological innovation in the sharing economy and the sustainable development of urban systems, using empirical data from three business cases in the emerging sharing mobility sector – in modes of ride-sharing, EV-sharing and bike-sharing - of Shanghai, China. It indicates that there is a strong co-evolution mechanism between the transformation towards more sustainable city at the macro-level and the business ecosystem innovation towards greener and smarter transport at the meso-level. We argue that the two level transformations, triggered by the disruptive innovation of the sharing economy and led by urban transformation towards sustainability, mutually influence each other and re-enforce sustainable values and practices in the fast changing urban context and business innovations in Shanghai.

There are some risks in renewable energy investments, such as legal, technology and financial issues. Hence, appropriate actions should be taken to minimize these risks. However, each of the measures to be taken for the management of risks creates new costs for businesses. Therefore, to ensure the financial sustainability of the projects, measures for the most important risks should be taken at the first stage. Because of this situation, it is vital to make a priority analysis for the risks faced by renewable energy projects so that it can be possible to increase the effectiveness of risk management. Accordingly, in this study, it is aimed to examine the risks and rewards regarding sustainable investment decisions for renewable energy projects. In this scope, a novel model is generated by integrating different techniques. In this process, the evaluations of six different experts are taken into consideration. First, missing evaluations for the sustainable investment decisions in renewable energy are estimated with neuro decision-making and collaborative filtering. Secondly, the weights of the risk factors of sustainable energy investments are computed with neuro quantum spherical fuzzy (QNSLF) DEMATEL with golden cut. Thirdly, reward alternatives for sustainable decision making are analyzed with Neuro QNSLF TOPSIS with golden cut. This study contributes to literature by determining the most important risks for renewable energy projects by an original decision-making model. Another important novelty of this study is that a new technique called neuro decision-making has also been developed. In this technique, the facial expressions of the experts who evaluate are taken into consideration. It is defined that technological changes have the greatest significance. Additionally, governmental incentives are found as the most essential reward to improve sustainable investments in renewable energy. Thus, for the effective management of these risks, appropriate actions should be taken. It is also obvious that the competitiveness of companies that fall behind this new technology will decrease. In this context, these enterprises need to take the necessary measures to manage the technology risk to be sustainable in the long term.

AbstractEnvironmental degradation is a major challenge facing the world. Our view is that a country's productive structure, reflected through its knowledge content and technical capabilities (economic complexity), is strongly correlated with its environmental performance. To empirically confirm this view, the link between economic complexity and environmental performance in member countries of the Organization for Economic Co‐operation and Development (OECD) was examined within a modified version of the Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT) model incorporating two alternative measures of economic complexity. The model was estimated using the fixed effects extension proposed by Driscoll and Kraay (DK‐FE) and Generalized Method of Moments (GMM) estimation techniques. Granger causality testing in frequency domain was also employed to examine country‐specific relationships. The sample period extended from 2007 to 2016. The study findings provided reliable empirical justification for our position. The coefficients for economic complexity in the long‐run estimations revealed that economic complexity positively impacted on environmental performance in the OECD countries. Granger causality outcomes also indicated economic complexity as a meaningful predictor of environmental performance in most of the OECD countries.

Accurate building energy prediction is vital to develop optimal control strategies to enhance building energy efficiency and energy flexibility. In recent years, the data-driven approach based on machine learning algorithms has been widely adopted for building energy prediction due to the availability of massive data in building automation systems (BASs), which automatically collect and store real-time building operational data. For new buildings and most existing buildings without installing advanced BASs, there is a lack of sufficient data to train data-driven predictive models. Transfer learning is a promising method to develop accurate and reliable data-driven building energy prediction models with limited training data by taking advantage of the rich data/knowledge obtained from other buildings. Few studies focused on the influences of source building datasets, pre-training data volume, and training data volume on the performance of the transfer learning method. The present study aims to develop a transfer learning-based ANN model for one-hour ahead building energy prediction to fill this research gap. Around 400 non-residential buildings’ data from the open-source Building Genome Project are used to test the proposed method. Extensive analysis demonstrates that transfer learning can effectively improve the accuracy of BPNN-based building energy models for information-poor buildings with very limited training data. The most influential building features which influence the effectiveness of transfer learning are found to be building usage and industry. The research outcomes can provide guidance for implementation of transfer learning, especially in selecting appropriate source buildings and datasets for developing accurate building energy prediction models.

Continuing economic progress with less environmental damage and achieving a sustainable environment require switching from fossil fuels to green energy. However, alleviating environmental damage of growth has become a major challenge for BRICS where economic progress amidst rising urbanization pollutes the environment. In this context, the fight against climate change and actions towards environmental sustainability are greatly affected by rising economic policy uncertainty. Hence, this study assesses the role of green energy, urbanization, and economic growth in CO2 emissions in the presence of economic policy uncertainty in BRICS (excluding South Africa) from 1997 to 2020. The study used the cross-sectionally augmented auto-regressive distributive lag technique for revealing the short- and long-run effects of the analyzed variables on environmental quality. The empirical evidence suggested that the environmental Kuznets curve exists according to the recent framework of Narayan and Narayan Energy Policy 38:661-666, (2010) because even though economic growth increases CO2 emissions, its long-run effect is less than the short-run effect. Economic policy uncertainty boosts CO2 not only in the short-run but also in the long-run, evidencing that a sustainable environment requires decreasing the levels of policy uncertainty. For BRICS, switching towards green energy is a vital option to decrease environmental deterioration owing to the negative connection between green energy and CO2. The findings indicated that rapid urbanization is among the causes of high CO2. Furthermore, economic policy uncertainty influences both green energy and economic growth levels. Finally, policies are recommended to mitigate environmental deterioration.