A systematic bibliometric review of clean energy transition: Implications for low-carbon development

Research landscape of energy transition and green finance: A bibliometric analysis

This study utilizes bibliometric analysis to examine historical and present research patterns in the area of energy transition and green finance and to forecast potential future domains. Using the bibliometric method, 328 scholarly articles from the Web of Science database were evaluated. This paper identifies influential publications, maps the research landscape, and forecasts emerging tendencies through co-citation and co-word analyses. Co-citation analysis found three main clusters, while co-word analysis revealed four main clusters. Despite the growing significance of research on energy transition and green finance research, further in-depth investigation is necessary to offer a thorough depiction of the research domain. This research represents a pioneering endeavour in the utilization of bibliometric analysis to investigate the interrelationship between two items. It offers valuable insights into the rapidly expanding field of energy transition and green finance, effectively highlighting its contours and indicating potential future developments.

Bibliometric Analysis of Research on Traditional Chinese Exercise and Osteoarthritis

Background

Osteoarthritis (OA) is a common disease in geriatric rehabilitation medicine caused by the progressive destruction of articular cartilage. Traditional Chinese exercise (TCE) is an important component of traditional sports in China and aims to stretch the musculoskeletal tract and relieve joint pain. Bibliometrics can help researchers find suitable partners and understand the research hotspots and trends in a certain field. However, there is still a lack of bibliometric analysis in the field of TCE and OA.

Methods

All the literature was obtained from the Web of Science Core Collection database. The last search was performed on July 28, 2023. The bibliometric indicators, such as publications, citations, and H-index, were recorded. Bibliometrix and CiteSpace were used for visualization analysis. In addition, randomized controlled trials were included to summarize the exercise prescription of TCE for OA.

Results

A total of 170 articles were included. The field of OA with TCE had great development potential and was in the rising period. The countries, institutions, and authors with the most publications were the United States, Tufts Medical Center, and Harvey WF, respectively. The most popular journal was Osteoarthritis and Cartilage. The recent burst keywords in this field were mainly "hip", "pilot", and "risk". Tai Chi was the most studied TCE with the most detailed content of exercise prescription, followed by Baduanjin and Wuqinxi.

Conclusion

Our study provides a basis for researchers in this field to choose appropriate partner and academic journals. Moreover, pain, muscle strength, and quality of life management of elderly OA patients are research hotspots in this field. The intervention of hip OA risk through TCE is expected to become a research direction for emerging teams. The TCE prescription we summarized can better provide researchers with more treatment details.

Fault diagnosis of photovoltaic systems using artificial intelligence: A bibliometric approach

Conventional fault detection methods in photovoltaic systems face limitations when dealing with emerging monitoring systems that produce vast amounts of high-dimensional data across various domains. Accordingly, great interest appears within the international scientific community for the application of artificial intelligence methods, which are seen as a highly promising solution for effectively managing large datasets for detecting faults. In this review, more than 620 papers published since 2010 on artificial intelligence methods for detecting faults in photovoltaic systems are analyzed. To extract major research trends, in particular to detect most promising algorithms and approaches overcoming excessive time calculations, a conventional bibliographic survey would have been extremely difficult to complete. That is why this study proposes to carry out a review with an innovative approach based on a statistical method named Bibliometric and a Expert qualitative content analysis. This methodology consists of three stages. First, a collection of data from databases is carried out with all precautions to achieve a large, robust, high-quality database. Second, multiple bibliometric indicators are chosen based on the objectives to be achieved and analyzed to assess their real impact, such as the quantity and nature of publications, collaborative connections among organizations, researchers, and countries or most cited articles. Finally, the Expert qualitative content analysis carried out by experts identifies the current and emerging research topics that have the greatest impact on fault detection in photovoltaic systems using artificial intelligence.

Identification of Emerging Industrial Biotechnology Chassis Vibrio natriegens as a Novel High Salt-Tolerant and Feedstock Flexibility Electroactive Microorganism for Microbial Fuel Cell

The development of MFC using electroactive industrial microorganisms has seen a surge of interest because of the co-generation for bioproduct and electricity production. Vibrio natriegens as a promising next-generation industrial microorganism chassis and its application for microbial fuel cells (MFC) was first studied. Mediated electron transfer was found in V. natriegens MFC (VMFC), but V. natriegens cannot secrete sufficient electron mediators to transfer electrons to the anode. All seven electron mediators supplemented are capable of improving the electronic transfer efficiency of VMFC. The media and carbon sources switching study reveals that VMFCs have excellent bioelectricity generation performance with feedstock flexibility and high salt-tolerance. Among them, 1% glycerol as the sole carbon source produced the highest power density of 111.9 ± 6.7 mW/cm². The insight of the endogenous electronic mediators found that phenazine-1-carboxamide, phenazine-1-carboxylic acid, and 1-hydroxyphenazine are synthesized by V. natriegens via the shikimate pathway and the phenazine synthesis and modification pathways. This work provides the first proof for emerging industrial biotechnology chassis V. natriegens as a novel high salt-tolerant and feedstock flexibility electroactive microorganism for MFC, and giving insight into the endogenous electron mediator biosynthesis of VMFC, paving the way for the application of V. natriegens in MFC and even microbial electrofermentation (EF).

Clean Household Energy Consumption and Residents' Well-Being: Empirical Analysis and Mechanism Test

Improving well-being is a critical problem for worldwide social progress. Research on well-being effects of clean household energy consumption is of great significance for the realization of United Nation's Sustainable Development Goals (SDGs). Due to the multifaceted role of clean household energy in enhancing well-being as a sustainable development goal, it has attracted extensive academic attention and research but still needs to be further refined through new perspectives. This paper uses data from the 2018 China Health and Retirement Longitudinal Study to conduct an empirical analysis of clean household energy consumption and residents' well-being using an ordered probit model, the instrumental variables method, a conditional mixed process (CMP) method, and a mechanism analysis model. The results show that (1) household clean energy consumption contributes to residents' well-being, and the results remained significant after selecting "Do you have an electric Bicycle?" as an instrumental variable to address the endogeneity question. (2) According to heterogeneity research, women's wellbeing is impacted by clean energy consumption in the household more than men's. Only in rural locations can clean household energy consumption significantly boost well-being; urban and suburban areas are unaffected. (3) In the mechanism analysis, health condition and depression played a mediating role on the impact of clean household energy consumption on well-being, and social contact played a moderating role on the impact of clean household energy consumption on well-being. The findings and policy recommendations in this paper are suggestive of how we can improve the well-being of residents in low- and middle-income countries and provide reference values for research in related fields around the world.

Local Governance in Just Energy Transition: Towards a Community-Centric Framework

Knowledge of energy transition (ET) is evolving in developing countries. Yet, it is unclear how the transition should be managed in a way that ensures justice for local stakeholders. We synthesise the extant theoretical ideas and practices of the local governance (LG) related to ET, which are vital in ensuring justice in energy policy at the local level. The paper advances this development by a systematic integrative literature review (N = 569) from the Web of Science (WoS) and highly cited grey literature linked to participation, LG, and ET. The bibliometric analysis indicates that, while the literature on energy justice is growing, limited attention has been paid to LG in just energy transition (N = 36). The analysis further indicates that more than half of the scientific literature is produced by five countries from the global North alone. In-depth scrutiny of highly cited studies and grey literature in LG and ET underlines the lack of a generalised framework of local ET governance, especially in the global South. We address this gap and propose a framework that exhibits a community-centric LG, which is essential for just energy transition.

Green Credit Policy and Corporate Stock Price Crash Risk: Evidence From China

Using the promulgation of Green Credit Guidelines in China as the research setting, this paper exploits a quasi-natural experiment to examine the impact of green credit policy on the stock price crash risk of heavy-polluting firms. The results show that green credit policy significantly increases the risk of stock price crash of heavy-polluting firms. Such impact is transmitted through increased financial constraints and reduced information transparency. In addition, we find that the impact of green credit policy on the stock price crash risk is more pronounced in firms with weak external governance and a small size. Our findings provide policy implications for mitigating corporate risks and promoting corporate sustainability.

Policy Announcement, Investor Attention, and Stock Volatility: Evidence From the New Energy Vehicle Industry

New energy vehicle (NEV) policies have greatly promoted the growth of the NEV industry in China, while also attracting a lot of investor attention. Using Chinese NEV concept stocks and related industrial policies, including purchase tax incentives (PTI) and promotion and application (P&A) policies, issued from 2011 to 2020 as the research setting, this paper adopts a panel data model to examine the impact of policy announcement on the volatility of NEV concept stocks, as well as the mediating role of investor attention in transmitting the impact. We find that NEV P&A policies have a significant and positive impact on NEV concept stock volatility, while PTI policies do not have a significant impact. Moreover, investor attention plays a partial mediating role in transmitting the impact of P&A policies on NEV stock market by increasing the stock volatility risk. Furthermore, there is heterogeneous effect of equity ownership in the relationship between policy announcement and investor attention on the volatility of NEV concept stocks; non-state-owned firms are more sensitive to the NEV P&A policies than state-owned firms. By analyzing the relationship between policy announcement and concept stock volatility, this paper enriches the research on NEV concept stocks and provides policy implications for the NEV industry.