The correlation between green finance and carbon emissions based on improved neural network

Sun, Chuang

doi:10.1007/s00521-021-06514-5

Cited by 50 publications

(22 citation statements)

References 24 publications

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“…Sartzetakis (2020) analyzed the important role of green bonds for the transition to a low-carbon development approach on the basis of the intergenerational burden theory and the need for large long-term infrastructure development [39]. Sun (2021) built an analytical model for the association between green finance and carbon emissions using neural network technology and executed simulation tests to verify the validity of the results, and the results showed that a significant association existed between green finance and carbon emissions [40]. Elheddad (2020) studied the effect of e-finance on carbon emissions by selecting panel data for 29 OECD countries from 2007 to 2016, controlling for possible heterogeneity between countries using fixed and random effects models, and testing robustness using instrumental variables estimation methods and panel quantile regressions, which showed that the development of e-finance mitigates carbon emissions in OECD countries and plays an important role in environmental protection [41].…”

Section: Literature Reviewmentioning

confidence: 99%

Research on coupling coordination and influencing factors between Urban low-carbon economy efficiency and digital finance—Evidence from 100 cities in China’s Yangtze River economic belt

2022

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China is a large country with rapid economic expansion and high energy consumption, which implies that the country’s overall carbon emissions are enormous. It is vital to increase urban low-carbon economy efficiency (ULEE) to achieve sustainable development of China’s urban economy. Digital finance is a significant tool to boost ULEE by providing a convenient and effective funding channel for urban low-carbon economic transformation. Analyzing the coupled and coordinated relationship between ULEE and digital finance is of vital importance for the sustainable development of the urban economy. This paper selects panel data of 100 cities in China’s Yangtze River Economic Belt (YEB) in 2011-2019, and analyzes the research methods such as the Global Malmquist-Luenberger index model, coupling coordination degree (CCD) model, standard deviation ellipse model, gray model, and geographic detector by The spatial and temporal distribution, dynamic evolution characteristics and influencing factors of the CCD between ULEE and digital finance are analyzed. The study shows that: (1) the CCD of ULEE and digital finance grows by 3.42% annually, reflecting the increasingly coordinated development of the two systems; (2) The CCD of ULEE and digital finance shows a distribution pattern of gradient increase from the upstream region of Yangtze River to the downstream region, meanwhile, the spatial center of gravity moves mainly in the midstream region; (3) The spatial center of gravity of CCD of ULEE and digital finance is expected to move 22.17 km to the southwest from 2019 to 2040; (4) In terms of influencing factors, the influence of informatization and industrial structure on the CCD increases over time, while the influence of factors such as population development, greening, transportation, and scientific research decreases over time. Finally, this paper proposes policy recommendations for improving the CCD of ULEE and digital finance based on the empirical results.

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Section: Literature Reviewmentioning

confidence: 99%

Research on coupling coordination and influencing factors between Urban low-carbon economy efficiency and digital finance—Evidence from 100 cities in China’s Yangtze River economic belt

2022

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“…For example, Wang et al [ 7 ] predicted the green finance index and development according to the characteristics of China's green finance. Sun [ 8 ] used the neural network to study the correlation between green finance and carbon emissions. Li and Gan [ 9 ] and Huang and Chen [ 10 ] studied the correlation between ecological environment development and green finance, and proposed the ecological issues to be solved by green finance; He et al [ 11 ] studied the relationship between green finance and smart city development, and proposed to lead the urban ecological construction through green finance.…”

Section: Introductionmentioning

confidence: 99%

Green Finance Evaluation Based on Neural Network Model

Wang

2022

Computational Intelligence and Neuroscience

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The weights of green finance indicators are established in accordance with the AHP in order to suggest an evaluation system that is more thorough and reasonable and to construct an evaluation index system. The findings indicate that the growth of urban green finance is more closely correlated with the development of environmental protection businesses, capital allocation efficiency, and governmental and social capital support. Regulation of consumption also has a significant impact. In order to encourage the growth of urban green finance, this paper analyzes the scoring outcomes and changes for each city and offers solutions and recommendations.

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“…In particular, financial development can reduce carbon emissions and be environmentally friendly if it encourages investment in environmental projects and enables firms to adopt and use advanced, energy-efficient, clean technologies, or renewable energy projects [58,59]. In addition, Sun [60] pointed out that the development of green finance has a moderating effect on the carbon emissions of high-income groups, while it has an opposite effect on the carbon emissions of middle-and lowincome groups. is is also the future research direction between green finance and carbon intensity, that is, further refinement of research samples is required to verify whether there will be differences in the regression results.…”

Section: Discussionmentioning

confidence: 99%

The Moderating Effect and Threshold Effect of Green Finance on Carbon Intensity: From the Perspective of Capital Accumulation

Zhang

2022

Complexity

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Climate change has caused serious threats to global economic development and human well-being, and green finance is a new way to achieve ecological, economic, and social sustainable development, and it also has important theoretical significance and policy value. This study firstly aims to study the impact of green finance on regional carbon intensity. Then, it aims to determine the moderating effect of capital stock per capita on the relationship between green finance and carbon intensity based on moderating effect model as well as to investigate the threshold effect of capital stock per capita on the relationship between green finance and carbon intensity based on threshold effect model. The main conclusions are as follows: (1) Every 1% increase in green finance will lead to a 2% decrease in carbon intensity, and the inhibition of green finance on carbon intensity is most obvious in northeast and east China. (2) The capital stock per capita has a significant moderating effect, which could enhance the restraining effect of green financial development on carbon intensity, and this moderating effect is most obvious in northeast China. (3) The capital stock per capita has a threshold effect in the process of green finance development to suppress carbon intensity. The higher the capital stock per capita, the stronger the inhibition effect of green finance development on carbon intensity. Therefore, in the process of capital accumulation, attention should be paid to improve the relevant legal system and policy guarantee, optimization of financial structure, and stimulation of the capital allocation effect of green finance. Simultaneously, in order to strengthen the inhibitory effect of green finance on carbon intensity, differentiated green finance development policies should be formulated based on factor endowment structure and comparative advantages of different regions.

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The correlation between green finance and carbon emissions based on improved neural network

Cited by 50 publications

References 24 publications

Research on coupling coordination and influencing factors between Urban low-carbon economy efficiency and digital finance—Evidence from 100 cities in China’s Yangtze River economic belt

Research on coupling coordination and influencing factors between Urban low-carbon economy efficiency and digital finance—Evidence from 100 cities in China’s Yangtze River economic belt

Green Finance Evaluation Based on Neural Network Model

The Moderating Effect and Threshold Effect of Green Finance on Carbon Intensity: From the Perspective of Capital Accumulation

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