Impact of innovation in renewable energy generation, transmission, or distribution-related technologies on carbon dioxide emission in the USA

You, Chengde; Khattak, Shoukat Iqbal; Ahmad, Manzoor

doi:10.1007/s11356-021-17938-w

Cited by 28 publications

(5 citation statements)

References 137 publications

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“…Khattak & Ahmad 49 and You et al 50 also established the same outcome in OECD nations and the USA, respectively. You et al, 51 established TECIN decreases CO 2 emissions in the USA. Khattak & Ahmad 52 investigated the BRICS economies to explore the impact of TECIN on carbon emissions.…”

Section: Literature Reviewmentioning

confidence: 99%

Investigating the asymmetric effects of renewable energy-carbon neutrality nexus: Can technological innovation, trade openness, and transport services deliver the target for Germany?

Ibrahim

Adebayo

Awosusi

et al. 2022

Energy & Environment

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The present millennium records the most devastating effects of global warming than what was evident in the pre-industrial era. The pervasive effects of global warming have raised concerns about the sustainability of the present and future generations. Hence, researchers are working assiduously towards proffering the most effective method of halting the surging emissions from global, regional, and national angles. This study contributes to the extant literature by providing the first empirical verification of how renewable energy, technological innovations, trade openness, and transport services impact carbon emissions in Germany. The empirical evidence is anchored on annual time series data from 1990 to 2020 estimated using the novel nonlinear ARDL to explore the positive and negative shocks associated with the short and long-run nexus. The wavelet coherence estimator is equally for ascertaining the multiple time scales in the time series and extends the knowledge frontier to the literature. The following findings are eminent from the overall analyses. First, long-run cointegration is supported among the variables of interest Second, only the adverse effects of renewable energy and technology are substantial enough to mitigate carbon emissions. The trade-induced emissions are established with the positive impacts of trade openness on carbon emissions. Transport services contribute significantly to carbon emissions in the long-run and short-run periods. However, mitigating effects of small magnitude are apparent in the long-run due to the perceived transition to green transportation. Policy implications supporting the transition to 100% renewable energy and green transportation are significant steps towards achieving Germany's carbon neutrality target.

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Section: Literature Reviewmentioning

confidence: 99%

Investigating the asymmetric effects of renewable energy-carbon neutrality nexus: Can technological innovation, trade openness, and transport services deliver the target for Germany?

Ibrahim

Adebayo

Awosusi

et al. 2022

Energy & Environment

View full text Add to dashboard Cite

show abstract

“…Most electrical power distribution systems rely on fossil-fuel generators, which are detrimental to the environment due to the high air pollutants induced by this technique. According to [160], almost 40% of the CO 2 emissions are due to power generation. Furthermore, producing a large amount of power in one site increases the delivery cost, particularly for distant consumers where long transmission cables are required.…”

Section: E Smart Grid 20 1) Motivationmentioning

confidence: 99%

Split Federated Learning for 6G Enabled-Networks: Requirements, Challenges, and Future Directions

Hafi,

Brik,

Frangoudis

et al. 2024

IEEE Access

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Sixth-generation (6G) networks anticipate intelligently supporting a wide range of smart services and innovative applications. Such a context urges a heavy usage of Machine Learning (ML) techniques, particularly Deep Learning (DL), to foster innovation and ease the deployment of intelligent network functions/operations, which are able to fulfill the various requirements of the envisioned 6G services. The revolution of 6G networks is driven by massive data availability, moving from centralized and big data towards small and distributed data. This trend has motivated the adoption of distributed and collaborative ML/DL techniques. Specifically, collaborative ML/DL consists of deploying a set of distributed agents that collaboratively train learning models without sharing their data, thus improving data privacy and reducing the time/communication overhead. This work provides a comprehensive study on how collaborative learning can be effectively deployed over 6G wireless networks. In particular, our study focuses on Split Federated Learning (SFL), a technique that recently emerged promising better performance compared with existing collaborative learning approaches. We first provide an overview of three emerging collaborative learning paradigms, including federated learning, split learning, and split federated learning, as well as of 6G networks along with their main vision and timeline of key developments. We then highlight the need for split federated learning towards the upcoming 6G networks in every aspect, including 6G technologies (e.g., intelligent physical layer, intelligent edge computing, zero-touch network management, intelligent resource management) and 6G use cases (e.g., smart grid 2.0, Industry 5.0, connected and autonomous systems). Furthermore, we review existing datasets along with frameworks that can help in implementing SFL for 6G networks. We finally identify key technical challenges, open issues, and future research directions related to SFL-enabled 6G networks.INDEX TERMS 6G networks, wireless communication, federated deep learning, split deep learning, split federated learning.

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“…Yang and Song (2023) defined that since renewable energy types are affected by weather conditions, differences in production capacity may occur. To minimize this problem, flexible services are needed (Chien et al, 2023;You et al, 2023). This variability in renewable energies can cause fluctuations in energy production.…”

Section: Literature Reviewmentioning

confidence: 99%

A Novel Fuzzy Model for Knowledge-Driven Process Optimization in Renewable Energy Projects

Huang,

Yüksel,

Dinçer

2024

J Knowl Econ

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This study is aimed at identifying key indicators to increase knowledge-based process optimization for renewable energy projects. Within this context, a novel fuzzy decision-making model is introduced that has two different stages. The first stage is related to the weighting of the knowledge-based determinants of process optimization in investment decisions by using quantum picture fuzzy rough sets (QPFR)-based multi-step wise weight assessment ratio analysis (M-SWARA). On the other side, the second stage consists of ranking the investment alternatives for process optimization in renewable energy projects via the QPFR-based technique for order preference by similarity (TOPSIS) methodology. The main contribution of this study is that a priority analysis is conducted for information-based factors affecting the performance of renewable energy projects. This situation provides an opportunity for the investments to implement appropriate strategies to increase the optimization of these investments. It is concluded that quality is the most essential indicator with respect to the process optimization of these projects. It can be possible to increase the efficiency of these projects by using better quality products. Innovation has an important role in ensuring the use of quality products in environmental sustainability. Owing to new technologies, it is easier to use more effective and innovative products. This condition also contributes to increasing the efficiency of the energy production process. Furthermore, the findings also denote that the most appropriate energy innovation alternative is the variety of clean energy sources. By focusing on different clean energy alternatives, the risk of interruptions in energy generation can be minimized. In other words, the negative impact of climatic conditions on energy production can be lowered significantly with the help of this situation.

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Impact of innovation in renewable energy generation, transmission, or distribution-related technologies on carbon dioxide emission in the USA

Cited by 28 publications

References 137 publications

Investigating the asymmetric effects of renewable energy-carbon neutrality nexus: Can technological innovation, trade openness, and transport services deliver the target for Germany?

Investigating the asymmetric effects of renewable energy-carbon neutrality nexus: Can technological innovation, trade openness, and transport services deliver the target for Germany?

Split Federated Learning for 6G Enabled-Networks: Requirements, Challenges, and Future Directions

A Novel Fuzzy Model for Knowledge-Driven Process Optimization in Renewable Energy Projects

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