Few researches have inspected the task of green finance in reducing CO2 emissions, while earlier studies have inspected the influence of economic development on carbon emissions. A green finance development index is built using four indicators to fill in this knowledge gap: green credit, green insurance, green securities, and green investing. Using data spanning the years 2005–2019, a panel quantile regression is applied to investigate the links between green finance, renewable energy, and CO2 emissions. Increases in renewable energy use and advances in the green finance development index have contributed to a reduction in CO2 emissions from BRICS countries. CO2 emissions on the other hand slowed the growth of renewable energy use, slowed the flow of investment to green projects, and ultimately hampered the development of green finance. There was also a clear policy-driven influence on renewable energy spending in the countries of the BRICS region. Green finance policies, on the other hand, have consistently failed to have a long-term impact. Therefore, rising the consumption of renewable energy and creating a carbon trading market are all part of this study’s recommendations for green finance policy improvement. 

/pdf/does-green-finance-mitigate-the-effects-of-climate-3os6sm5y.pdf

Does green finance mitigate the effects of climate variability: role of renewable energy investment and infrastructure

Assessing the impact of renewable energy investment, green technology innovation, and industrialization on sustainable development: A case study of China

Does green finance and renewable energy promote tourism for sustainable development: Empirical evidence from China

Small- and medium-sized enterprises (SMEs) in China have been hit hard by the coronavirus (COVID-19) outbreak, which has jeopardized their going out of business altogether. As a result, this research will shed light on the long-term impacts of COVID-19 lockdown on small businesses worldwide. The information was gathered through a survey questionnaire that 313 people completed. Analyzing the model was accomplished through the use of SEM in this investigation. Management and staff at SMEs worldwide provided the study’s data sources. Research shows that COVID-19 has a significantly bad influence on profitability, operational, economic, and access to finance. In the study’s findings, outside funding aids have played an important role in SMEs’ skill to persist and succeed through technological novelty than in their real output. SME businesses, administrations, and policymakers need to understand the implications of this study’s results. 

/pdf/the-impact-of-covid-19-on-small-and-medium-sized-enterprises-25sr95j5.pdf

The impact of COVID-19 on small- and medium-sized enterprises (SMEs): empirical evidence for green economic implications

The number of applications being developed that require access to knowledge about the real world has increased rapidly over the past two decades. Domain ontologies, which formalize the terms being used in a discipline, have become essential for research in areas such as Machine Learning, the Internet of Things, Robotics, and Natural Language Processing, because they enable separate systems to exchange information. The quality of these domain ontologies, however, must be ensured for meaningful communication. Assessing the quality of domain ontologies for their suitability to potential applications remains difficult, even though a variety of frameworks and metrics have been developed for doing so. This article reviews domain ontology assessment efforts to highlight the work that has been carried out and to clarify the important issues that remain. These assessment efforts are classified into five distinct evaluation approaches and the state of the art of each described. Challenges associated with domain ontology assessment are outlined and recommendations are made for future research and applications.

Evaluating Domain Ontologies: Clarification, Classification, and Challenges

Industry 4.0 technology and circular economy practices: business management strategies for environmental sustainability

Biosynthesis and characterization of bacterial cellulose membranes presenting relevant characteristics for air/gas filtration

The semantic web is a technology to save data in a machine-readable format that facilitates machines to intelligently match that data with related data based on meanings. Whilst this approach is being adopted and implemented by some large organisations there is a need for an effective semantic search engine to maximise the full potential of that semantic web. A major difficulty is that the search experience is dependent on a number of elements including a user-friendly interface, a strong query language processor, a result optimiser, result ranking and the use of appropriate data structures to store data. Apart from the technical aspects related to implementation, a strategy to prioritise these elements is needed to optimize and enhance the search experience over the semantic web. The purpose of this work is to investigate some relevant issues on querying the semantic web in a context of semantic search engines, and propose a framework that facilitates an effective search over the semantic web.

New Framework for Semantic Search Engine

Search tools are essential for an information system. Being syntax based, the existing search engines have a number of limitations in particular their difficulty in returning relevant results. Typically these limitations are partly mitigated through use of various customised techniques in order to provide the best possible user experience. Unlike conventional search engines, a semantic web search engine attaches meanings to key words. One of the biggest challenges for such a semantic search engine is to maintain the standard of user experience while serving its purpose of finding relevant data with meanings. A semantic search tool needs to both link meanings to search keywords and be able to efficiently retrieve information. The purpose of this work is to investigate the techniques that can be used for efficient information retrieval and effective user experience within the context of a semantic search engine. We propose two algorithms for query optimisation and ontology crawler.

User experience and efficiency for semantic search engine

Researchers in the semantic web community have been designing tools and architectures to integrate heterogeneous data originated from distributed sources in the last decade. Technologies, such as RDF, have enabled new data spaces and concept descriptors to define an increasing complex and heterogeneous web of data. To these growing data resource spaces it is necessary to bridge between the different knowledge domains and provide a homogeneous and user-friendly interface. This paper discusses the limitations of some of the existing tools and solutions. The authors also propose a new architecture to integrate, using a mediator and centralised global schema, semi structured heterogeneous sources with large scale of structured data sources, particularly Linked Open Data cloud, and provide a search interface for non-expert users to searching and to accessing to them.

Data integration approach for semi-structured and structured data (Linked Data)

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