Global Carbon Finance Trends: A Bibliometric Investigation

Zenia Hukoyi^*
*Correspondence: Zenia Hukoyi, Department of Economics, Northeast Forestry University, Hexing Road, Harbin, China, Email:

Introduction

This paper used bibliometric analysis to examine 4408 academic works on carbon finance from 1992 to 2021 using the Web of Science core database. This work shows that the quantity of distributions on carbon finance-related research has expanded the Diary of Cleaner Creation is the most useful diary. China has the most significant number of publications, whereas the United States collaborates with other nations the most extensively. Chinese authors make up the highest proportion of the top most prolific authors the Chinese Academy of Sciences is the independent institution with the most output and influence. Through keyword analysis, we are able to summarize the primary research directions in carbon finance: carbon sequestration, economic development, carbon capture, modelling carbon price forecasting. Research connected with the effect of energy utilization, sustainable power, and urbanization on fossil fuel by-products might turn into a problem area for future examination in carbon finance. This paper is able to provide some references for future research through an in-depth analysis of the development of carbon finance over the past 30 years and future trends, which is very important for promoting carbon emission reduction.

Description

Longer feasible to rely on labour-intensive sectors to propel economic development due to the ageing of the population. The economy of several nations has concurrently transitioned from fast growth to high-quality development, but they are now in the predicament of "getting old before getting rich." These nations started utilising industrial robots in the manufacturing process in order to suit their own national development needs. These nations can now sustain economic development without having to hire a lot of people. Costs have gone down, productivity has gone up, and the chance of human mistake has decreased as a result. Robots can operate continuously and can be utilised in harmful working environments that may be too risky for people. The usage of has had an influence on manufacturer-modern time, ozone harming substance discharges from human exercises have fundamentally expanded adding to environmental change, a worldwide issue. Extreme weather, severe droughts, and forest fires are just a few of the ecological effects that climate change is having on all parts of the world. Because it is necessary to fund initiatives related to green energy and renewable energy, carbon finance has emerged as an essential component of the development of the green economy there is currently no standard definition of carbon finance Instead, the idea can be broadly divided into two categories A narrow definition and a broad definition. Carbon finance is a collective term for the various cash flows obtained from selling project-based greenhouse gas emission reductions or trading carbon permits In a more narrow sense, carbon finance is a collective term for the various financial instruments and approaches that address climate issues through market-based instruments in a carbonconstrained society [1,2].

Climate change will gradually and adversely affect environmental degradation and socioeconomic systems that are dependent on the environment, with the potential to cause significant population displacement hence, addressing environmental change and progressing to a low-carbon economy are fundamental for all states to consider. Various state run administrations have embraced environment arrangements to resolve this issue. Both the Kyoto Protocol in the United Nations Framework Convention on Climate is significant the novel idea of carbon finance was born out of these two conventions. The parties to the approved the Paris Agreement in, which outlined strategies for combating climate change after At the Hangzhou Summit in China and the United States ratified the Paris Agreement, ushering in a new era in carbon emission control additionally, more and more nations are participating in carbon-neutral climate change mitigation measures. The concluded negotiations on the Paris Agreement's implementation rules, laying the groundwork for its full and active implementation. Since then, updated climate commitments have been submitted by over one hundred nations The development of a low-carbon economy is absolutely necessary to address climate change and reduce greenhouse gas emissions quickly [3,4].

Research already conducted focuses on how industrial robots affect productivity and replace human resources. Industrial robots and economic growth are closely related to one another Industrial robots can promote economic development by influencing productivity return on invested capital and enhancing total factor productivity They also contribute to quickening economic growth. Industrial robots speed up structural upgrades in China's service industry and encourage improvements and rationalisation of industrial structures. In addition, academics see industrial robots as a sign of technical advancement, saying that they boost productivity primarily through enhancing scale efficiency and technology efficiency Industrial robots contribute significantly to sustainable development by lowering energy use and encouraging the development of green technologies [5].

Conclusion

Carbon price forecasting, which includes the carbon price prediction, energy prices, and other keywords, is the sixth cluster. Fossil fuel by products are one of the central point’s adding to natural contamination and environmental change, and the productive activity of the fossil fuel by products exchanging market successfully advances fossil fuel by product decrease. The management of carbon trading markets, the formulation of related policies, and the choices made by investors all depend on precise forecasting of carbon prices. In this group, analysts centre around working on the exactness and strength of carbon cost gauging. In addition, they have proposed a variety of carbon price forecasting model approaches, including the deep neural network model a hybrid carbon price forecasting model based on quadratic decomposition and improved extreme learning machine a hybrid carbon price forecasting model based long-term shortterm memory network a quadratic decomposition carbon price forecasting model based on a kernel limit learning machine.

Acknowledgement

None.

Conflict of Interest

None.

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