The influencing factors of carbon trading companies applying blockchain technology: evidence from eight carbon trading pilots in China

Carbon emission reduction is now a global issue, and the prediction of carbon trading market prices is an important means of reducing emissions. This paper innovatively proposes a second decomposition carbon price prediction model based on the nuclear extreme learning machine optimized by the Sparrow search algorithm and considers the structural and nonstructural influencing factors in the model. Firstly, empirical mode decomposition (EMD) is used to decompose the carbon price data and variational mode decomposition (VMD) is used to decompose Intrinsic Mode Function 1 (IMF1), and the decomposition of carbon prices is used as part of the input of the prediction model. Then, a maximum correlation minimum redundancy algorithm (mRMR) is used to preprocess the structural and nonstructural factors as another part of the input of the prediction model. After the Sparrow search algorithm (SSA) optimizes the relevant parameters of Extreme Learning Machine with Kernel (KELM), the model is used for prediction. Finally, in the empirical study, this paper selects two typical carbon trading markets in China for analysis. In the Guangdong and Hubei markets, the EMD-VMD-SSA-KELM model is superior to other models. It shows that this model has good robustness and validity.

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Analysis of Revenue Incentive Dynamic Mechanism of Financial Supply Chain from the Perspective of the Internet of Things

Complexity ◽

10.1155/2021/5595979 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Yue Yin

Keyword(s):

Supply Chain ◽

Life Cycle ◽

Influencing Factors ◽

Incentive Mechanism ◽

Influencing Factor ◽

Blockchain Technology ◽

Supply Chain Finance ◽

The Incentive Mechanism ◽

Relationship Factors ◽

The Internet Of Things

With the rapid development of society, all walks of life need the support of the Internet of Things, and the financial industry is no exception. This article integrates blockchain technology with supply chain finance and builds a supply chain financial alliance architecture based on blockchain technology and an underlying model of the Ethereum blockchain system suitable for supply chain finance. We innovated new supply chain finance models and operating mechanisms and proposed business scenarios for supply chain finance from the perspective of blockchain. Taking into account the actual operation of the blockchain supply chain financial platform, the principal-agent model and the incentive theory are applied, and the supply chain financial accounts receivable model is taken as an example in the case of complete information and incomplete information. The incentive mechanism between the service provider of the chain supply chain financial platform and the core enterprise promotes the better implementation of blockchain technology and supply chain finance. Based on the existing theoretical research, this paper identifies the key influencing factors of the supply chain’s cross-enterprise incentive mechanism. These influencing factors system includes two dimensions: transaction factors and relationship factors. Transaction factors include resource dependence, uncertainty, and cooperation experience; relationship factors include corporate reputation, trust level, and relationship commitment. Based on the nature of the incentive mechanism, information sharing and revenue sharing are extracted as the measurement dimensions of the supply chain’s cross-enterprise incentive mechanism. On this basis, this article draws on the existing enterprise life cycle division method and constructs a hypothetical model of the influencing factors of the incentive mechanism in the incubation period, the growth period, and the maturity period. Relevant data was collected through questionnaires, and SPSS and AMOS software were used to perform statistical analysis, reliability analysis, exploratory factor analysis, confirmatory factor analysis, and structural equation hypothesis testing on the data. The performance of each influencing factor in different stages of the enterprise’s life cycle and the importance of each influencing factor in the same life cycle stage are obtained.

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Guangdong’s Carbon Trading System: A Review of Liquidity and Influencing Factors

Sustainable Energy and Green Finance for a Low-carbon Economy ◽

10.1007/978-3-030-35411-4_4 ◽

2020 ◽

pp. 65-93

Author(s):

Jingyan Fu ◽

Menghao Wu ◽

Junyan Liu

Keyword(s):

Influencing Factors ◽

Carbon Trading ◽

Trading System ◽

System A

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When Is Blockchain Worth It? A Case Study of Carbon Trading

Energies ◽

10.3390/en13081980 ◽

2020 ◽

Vol 13 (8) ◽

pp. 1980 ◽

Cited By ~ 1

Author(s):

Fangyuan Zhao ◽

Wai Kin (Victor) Chan

Keyword(s):

Risk Analysis ◽

Cost Effective ◽

Disruptive Innovation ◽

Carbon Trading ◽

Blockchain Technology ◽

Institutional Risk ◽

Control Framework ◽

Technical Risk ◽

Potential Risks

Blockchain, as an emerging technology and a disruptive innovation, has attracted attention from both academia and industry. However, there are many potential risks associated with it, such as the technical risk, the legal risk and the privacy risk. A comprehensive risk analysis is crucial for cost-effective deployment of blockchain technology. Important adoption decisions, including when to deploy blockchain, how to plan the investment, how to transfer current businesses onto blockchain, and how to price the blockchain service depend on this risk analysis. Yet very little study exists concerning the blockchain adoption planning with risks analysis. This research presents a cost-and-risk analysis framework and an adoption planning method for the case of blockchain application in carbon trading. Design requirements implied by the analysis are inferred and the architecture of a novel hybrid blockchain system is proposed. The system leverages the advantages of blockchain technology and incorporates institutional risk control framework. The optimal adoption strategy of this system is derived through modelling of users’ and the organizer’s behavior.

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Analysis of China’s CO2 Emission and Carbon Trading Potential

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.281.704 ◽

2013 ◽

Vol 281 ◽

pp. 704-709 ◽

Cited By ~ 2

Author(s):

Zheng Chao Wang ◽

Hai Lin Mu ◽

Hua Nan Li

Keyword(s):

Global Warming ◽

Influencing Factors ◽

Developed Countries ◽

Good Choice ◽

Carbon Intensity ◽

Energy Structure ◽

Carbon Trading ◽

Trading Market ◽

The World ◽

The Developed Countries

With the development of economy and technology, the world produces more and more greenhouse gases (GHGs), which result in global warming. The Kyoto Protocol signed in 1997, marked the resolution of human fighting global warming. The developed countries are committed to achieve their GHGs emission targets under the protocol, developing nations play similar roles on a voluntary way. However, recently Copenhagen, Cancun and Durban Climate Conference, didn’t reach an agreement of the post-Kyoto era, which cause an uncertain situation in the post-Kyoto era. Due to much of the GHGs come from energy sectors and China is the largest GHGs emission country in the world. It is worthwhile to review the Chinese energy situation. A carbon intensity decomposition model is used to analyze the influencing factors of the carbon intensity because the Chinese target is reducing 40%-45% carbon intensity in 2020 on the basis of 2005. Then this paper analyzes the potential of carbon trading potential in China. This paper comes to a conclusion that the influencing factors of the carbon intensity are mainly energy structure and energy intensity. Carbon trading market is a good choice for China because China has good potential in GHGs reduction in the energy sector and carbon trading market can help China realize the target in 2020.

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Residents’ Willingness and Influencing Factors on Action Personal Carbon Trading: A Case Study of Metropolitan Areas in Tianjin, China

Sustainability ◽

10.3390/su11020369 ◽

2019 ◽

Vol 11 (2) ◽

pp. 369

Author(s):

Yong Liu

Keyword(s):

Influencing Factors ◽

Carbon Emissions ◽

Questionnaire Survey ◽

Regression Models ◽

Metropolitan Areas ◽

Carbon Trading ◽

Factors Influencing ◽

Trading Patterns ◽

Multiple Regression Models

Personal carbon trading offers a powerful and innovative instrument with which to achieve reductions in carbon emissions. Meanwhile, residents’ personal carbon trading willingness and the factors influencing such willingness have critical effects on the acceptance of personal carbon trading. Therefore, the present research uses a questionnaire survey in metropolitan areas of Tianjin China and the results indicated that most of the interviewees (74.92%) agreed or strongly agreed that they would participant in personal carbon trading. Moreover, according to the results of multiple regression models, governmental policies and residents’ environmental awareness and motivations were positively related to their personal carbon trading willingness. However, personal barriers to personal carbon trading were negatively related to personal carbon trading willingness. Control variables, such as gender and incomes, were not significantly related to personal carbon trading willingness. Thus, monitoring residents’ emission and trading patterns, emphasizing effective, transparent, and fair policies, as well as mitigating uncertainty could all be effective ways to increase the acceptance of personal carbon trading.

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