Economic Radiating Effects of the Guangdong–Hong Kong–Macao Greater Bay Area in China Effects of the Guangdong–Hong Kong–Macao Greater Bay Area

Li, Cheng

doi:10.1142/9789811218682_0006

Cited by 14 publications

(10 citation statements)

References 103 publications

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“…For instance, Zhou and Xu (2022) demonstrate that a U-shaped relationship exists between green finance and ecological development. In addition, Li and Huang (2022) verify the negative effect of green finance on CO 2 emissions. Yet, there are few studies that examine the promoting effect on low-carbon transformation in power generation industry from a new perspective of green finance.…”

Section: Introductionmentioning

confidence: 83%

Could green finance facilitate low-carbon transformation of power generation? Some evidence from China

Lin

Liao

Jia

2022

IJCCSM

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Purpose The decarbonization of power generation is key to achieving carbon neutrality in China by the end of 2060. This paper aims to examine how green finance influences China’s low-carbon transition of power generation. Using a provincial panel data set as an empirical study example, green finance is assessed first, then empirically analyses the influences of green finance on the low-carbon transition of power generation, as well as intermediary mechanisms at play. Finally, this paper makes relevant recommendations for peak carbon and carbon neutrality in China. Design/methodology/approach To begin with, an evaluation index system with five indicators is constructed with entropy weighting method. Second, this paper uses the share of coal-fired power generation that takes in total power generation as an inverse indicator to measure the low-carbon transition in power generation. Finally, the authors perform generalized method of moments (GMM) econometric model to examine how green finance influences China’s low-carbon transition of power generation by taking advantage of 30 provincial panel data sets, spanning the period of 2007–2019. Meanwhile, the implementation of the 2016 Guidance on Green Finance is used as a turning point to address endogeneity using difference-in-difference method (DID). Findings The prosperity of green finance can markedly reduce the share of thermal power generation in total electricity generation, which implies a trend toward China’s low-carbon transformation in the power generation industry. Urbanization and R&D investment are driving forces influencing low-carbon transition, while economic development hinders the low-carbon transition. The conclusions remain robust after a series of tests such as the DID method, instrumental variable method and replacement indicators. Notably, the results of the mechanism analysis suggest that green finance contributes to low-carbon transformation in power generation by reducing secondary sectoral share, reducing the production of export products, promoting the advancement of green technologies and expanding the proportion of new installed capacity of renewable energy. Research limitations/implications This paper puts forward relevant suggestions for promoting the green finance development with countermeasures such as allowing low interest rate for renewable energy power generation, facilitating market function and using carbon trade market. Additional policy implication is to promote high quality urbanization and increase R&D investment while pursuing high quality economic development. The last implication is to develop mechanism to strengthen the transformation of industrial structure, to promote high quality trade from high carbon manufactured products to low-carbon products, to stimulate more investment in green technology innovation and to accelerate the greening of installed structure in power generation industry. Originality/value This paper first attempts to examine the low-carbon transition in power generation from a new perspective of green finance. Second, this paper analyses the mechanism through several aspects: the share of secondary industry, the output of exported products, advances in green technology and the share of renewable energy in new installed capacity, which has not yet been done. Finally, this study constructs a system of indicators to evaluate green finance, including five indicators with entropy weighting method. In conclusion, this paper provides scientific references for sustainable development in China, and meanwhile for other developing countries with similar characteristics.

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Section: Introductionmentioning

confidence: 83%

Could green finance facilitate low-carbon transformation of power generation? Some evidence from China

Lin

Liao

Jia

2022

IJCCSM

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“…As an important node of global economy and trade, Hong Kong is on par with New York and London. When Shenzhen and Hong Kong are driven by the difference in systems, and at the same time, Shenzhen is driven by the economic "spillover effect" of Hong Kong and the "front factory and back store" model (Luo & Shen, 2010), Shenzhen's GDP surpassed that of Hong Kong in 2019 (Lin, 2019). In other words, Hong Kong's absolute advantage over Shenzhen in financial services diminishes.…”

Section: Endogeneity and Externalities: "One Country Two Systems" Pro...mentioning

confidence: 99%

“One Country, Two Systems” and the Realization of the Complete Reunification of China: A Synthesis of Narrative Reviews

Chao-yi¹

2023

ChnStd

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This study is a review of the paper on "one country, two systems". First, this analysis considers "one country, two systems" as a policy, but not a traditional national-level policy. This policy works as a governance policy. Second, Beijing also regards one country, two systems as a long-term and stable strategy. Objectively, the implementation of "one country, two systems" in Hong Kong and Macau has expanded a new theoretical and practical field for the original political and economic structure of China. Of course, it is the high degree of autonomy and strong self-healing capacity of "one country, two systems" that makes it the best policy tool to the Realization of the Complete Reunification of China.

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“…In the event of an intersection between the UF k and UB k curves, the point of intersection within the critical line range is considered to be a potential mutation point, but its validity must be confirmed through a sliding t-test. In the case of overlap between the two results, the point of overlap is considered to be the mutation point [6] .…”

Section: ) 1 (mentioning

confidence: 99%

Analysis and prediction of urban drought characteristics: Nanchang City as an example

Yang,

Xu,

et al. 2023

Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023)

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This study was undertaken in Nanchang City with the aim of enhancing resilience against droughts, which have become a growing concern and have inflicted significant harm to both the economy and the quality of life of the city's inhabitants. The research efforts utilized the monthly data of precipitation and average temperature in Nanchang from January 1960 to December 2018 to accomplish the following objectives: assess the trend and abrupt variations of precipitation and temperature via a combination of linear regression, M-K test, and sliding t-test; analyze the drought intensity and frequency through two drought indices, using different time scales; and employ NAR Artificial Neural Network to predict the SPI-1 and SPEI-1 indices, and evaluate their accuracy. The findings indicated that there was a significant increase in annual precipitation and average temperature in Nanchang over a period of 59 years. At the annual scale, droughts were more frequent and light to medium droughts were the most prevalent, whereas heavy and exceptional droughts were less frequent. At the seasonal scale, summer and autumn were more susceptible to droughts than spring and winter. On the whole, the SPEI index was found to be more effective in identifying droughts in Nanchang, being more sensitive to the variations of drought and capable of offering varying assessments of drought intensity at different time scales. The overall performance of the prediction model was deemed satisfactory, with the prediction accuracy of SPI-1 and SPEI-1 indices being 69.4% and 81.5% respectively in the short-term prediction period. This resulted in the conclusion that the model had a superior prediction effect on the SPEI-1 index. The findings of the research could serve as a reference for future predictions of drought trends in Nanchang in the short term.

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Economic Radiating Effects of the Guangdong–Hong Kong–Macao Greater Bay Area in China Effects of the Guangdong–Hong Kong–Macao Greater Bay Area

Cited by 14 publications

References 103 publications

Could green finance facilitate low-carbon transformation of power generation? Some evidence from China

Could green finance facilitate low-carbon transformation of power generation? Some evidence from China

“One Country, Two Systems” and the Realization of the Complete Reunification of China: A Synthesis of Narrative Reviews

Analysis and prediction of urban drought characteristics: Nanchang City as an example

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