摘要：
Both financial development and technological innovation are critical issues for policy-makers and academics in high-quality economic development. From the perspective of the geographical structure of the financial supply, this paper measures the number of financial institutions within a certain radius around an enterprise using the information on its geographical location and that of surrounding financial institutions to explore the impact of financial density on the enterprise's innovation quality, as well as the transmission mechanism. Rising financial density improves enterprise innovation quality, and this enhancement effect is mainly manifested in patent generality rather than patent originality. As the geographic radius expands, the effect of financial density on enterprise innovation quality increases and then decreases. Financial accessibility and competitive banking channels are the primary transmission mechanisms of financial density on enterprise innovation quality. In addition, the promotional effect of financial density on enterprise innovation quality is heterogeneous according to regional, industry, and enterprise characteristics. Finally, further analysis indicates that financial density will promote both innovation quality and quantity by motivating firms to engage in patent R&D behaviors that are highly technologically advanced. In addition, financial density enhances enterprises' innovation efficiency by increasing bank competition. Similarly, financial density is also conducive to increasing enterprises' commercial credit and financial liabilities. Therefore, the government should accelerate the development of an inclusive financial system, improve the coverage and penetration rates of the spatial layout of financial institutions, and encourage high-quality economic development by encouraging technologically advanced and innovative firms.
期刊：
Economic Analysis and Policy,2024年 ISSN：0313-5926
通讯作者：
Yi Fan
作者机构：
[Yi Fan] College of Economics, Central South University of Forestry and Technology, Changsha, CHINA;Allameh Tabataba'i University, Tehran, Iran;Trade Promotion Organization of Iran, IRAN;[Omid Ranjbar] Allameh Tabataba'i University, Tehran, Iran<&wdkj&>Trade Promotion Organization of Iran, IRAN
通讯机构：
[Yi Fan] C;College of Economics, Central South University of Forestry and Technology, Changsha, CHINA
摘要：
Energy security is affected by extreme natural, human, domestic political, geopolitical, and fossil energy price shocks/events and green energy policies. The degree of persistence in energy security determines the magnitudes of social, economic, and environmental outcomes of the shocks/policies. In this paper, we examined the degree of persistence in energy security of G7 countries using a new proxy namely the energy security risk index, and a novel second-generation panel quantile unit root test over the period 1980-2018. In addition, we applied the sequential panel selection method (SPSM), to identify the stationary members within each quantile. Our results indicated the stochastic properties of the energy security risk indexes vary across the quantile and the countries. Among the G7 countries, the energy security risk index of the US displays unit root process within all quantiles. While the energy security risk indexes of other countries display stationary processes, especially within high quantiles. Our results have important policy implications regarding the effectiveness of green policies in improving the energy security of the G7 countries and the disturbance effects of anti-energy security shocks. According to our findings, the US has to constantly pursue the risks that threaten the country's energy system while other G7 countries likely do not have such severe concerns about shocks affecting energy security, and these shocks have a short-term effect on their energy security.
期刊：
Economic Systems Research,2024年:01 ISSN：0953-5314
通讯作者：
Xie, R
作者机构：
[Zhao, Guomei] Cent South Univ Forestry & Technol, Sch Econ, Changsha, Peoples R China.;[Zhao, Guomei] Res Ctr High Qual Dev Ind Econ, Key Res Base Philosophy & Social Sci Hunan Prov, Changsha, Peoples R China.;[Xie, Rui] Hunan Univ, Sch Econ & Trade, Changsha, Peoples R China.;[Su, Bin] Natl Univ Singapore, Energy Studies Inst, Singapore, Singapore.;[Wang, Qunwei] Nanjing Univ Aeronaut & Astronaut, Sch Econ & Management, Nanjing, Peoples R China.
通讯机构：
[Xie, R ] H;Hunan Univ, Changsha 410079, Peoples R China.
摘要：
This paper constructs a comparative analysis framework on how the input-output (IO) model with technical differences affects the calculation of the pollution terms of trade (PTT) and the tests of the pollution haven hypothesis. Specifically, the CO2 terms of trade (CTT) of the world's major economies are calculated based on five IO models, and chain additive structure decomposition analysis (SDA) is conducted to examine the roles of different factors in the changes in CTT. The economic phenomena reflected by the CTT measured by these IO models are found to be different, and a comparative analysis shows that different IO models are suitable for studying different economic problems. Suggestions are provided on the application of different IO models in the calculation of economic indicators and the study of economic issues. Policy makers need to be cautious about policy recommendations based on the results obtained from different IO models.
摘要：
The "Annual Report 2021" from the United Nations Environment Programme (UNEP) highlights that the transportation sector is the fastest-growing greenhouse gas emissions sector, accounting for approximately 25% of energy-related emissions. What is even more concerning is that, at a time when carbon emissions need to be urgently reduced across various industries globally, carbon emissions from the transportation sector continue to rise. This is because the improvement in the efficiency of vehicle power combustion struggles to offset the increasing emissions resulting from the massive volume of travel. With the enhancement of transportation networks in various countries, it is projected that the growth rate of carbon emissions in the transportation sector will surpass that of the industrial and power sectors, presenting a significant challenge to achieving the emission reduction goals outlined in the Paris Agreement. Carbon emissions in the global transportation sector encompass various modes of transportation, including road, rail, aviation, and maritime, with road transportation being the largest contributor to carbon emissions. This study utilized the Stacking technique to build the X-MARL model for predicting
$$\hbox {CO}_{2}$$
emissions from vehicles and formulated recommendations for carbon reduction in the transportation industry. The model was tested using a dataset of vehicle
$$\hbox {CO}_{2}$$
emissions officially recorded by the Canadian government, comprising 7385 data points and covering 12 different vehicle parameter attributes. During the experimentation process, three statistical evaluation metrics were employed, namely mean squared error (MSE), root-mean-squared error (RMSE), and the coefficient of determination (R2). The dataset was randomly split into a training set (80% of the total data) and a testing set (20% of the total data). The experimental results demonstrated that the X-MARL model exhibited the highest prediction accuracy. This study provides an original strategy for accurately predicting carbon emissions from road transportation, which can offer support and guidance to decision-makers in formulating and implementing effective environmental policies.
期刊：
Journal of Economic Policy Reform,2024年 ISSN：1748-7870
通讯作者：
Tao, L
作者机构：
[Tong, Jing] Cent South Univ Forestry & Technol, Coll Bangor, Changsha, Hunan, Peoples R China.;[Tao, Li; Tao, L] Cent South Univ Forestry & Technol, Coll Econ, Changsha, Hunan, Peoples R China.;[Luo, Shouyi] Nanyang Technol Univ, Nanyang Business Sch, Singapore, Singapore.
通讯机构：
[Tao, L ] C;Cent South Univ Forestry & Technol, Coll Econ, Changsha, Hunan, Peoples R China.
关键词：
Digital economy;environmental pollution;air pollution;public attention to environmental pollution;environmental governance efficiency;C23;E0;O13
摘要：
Our study aims to investigate whether and how the development of the digital economy can reduce air pollutant emissions in China. Specifically, using a fixed effect model and an empirical test that exploits pollutant emissions in 30 Chinese provinces from 2011 to 2020, we explore the impact of digital economy development on air pollution through three mechanisms: public attention to environmental pollution, production efficiency, and environmental governance efficiency. Additionally, we provide heterogeneity analysis based on regions, industrial structure, and years of schooling. Findings are crucial for policymakers and environmentalists concerned about economic development's effects on the environment.
作者机构：
[Du, Yuxia] Guangdong Univ Foreign Studies, Sch Econ & Trade, Guangzhou 510000, Guangdong, Peoples R China.;[Li, Mingjie; Du, Yuxia] Guangzhou Huashang Coll, Sch Econ & Trade, Guangzhou 510000, Guangdong, Peoples R China.;[Li, Mingjie] Cent South Univ Forestry & Technol, Sch Econ, Changsha 410000, Peoples R China.
通讯机构：
[Li, MJ ] G;Guangzhou Huashang Coll, Sch Econ & Trade, Guangzhou 510000, Guangdong, Peoples R China.;Cent South Univ Forestry & Technol, Sch Econ, Changsha 410000, Peoples R China.
关键词：
Digital transformation;carbon emission;technological progress;total factor productivity
摘要：
At present, on the one hand, domestic enterprises carry out digital transformation one after another due to the pressure of survival and development, and on the other hand, due to environmental regulations, enterprises are facing the pressure of carbon emission reduction. This paper discusses the impact of enterprises' digital transformation on carbon emissions and finds that enterprises' digital transformation will increase carbon emissions, mainly because technological progress will bring about more extensive use of computers in production services, and the use of fossil energy will increase carbon emissions before the energy structure is effectively improved. Even if enterprises' digital transformation can improve of total factor productivity, it is difficult to offset the increase in carbon emissions caused by the use of more fossil energy. In addition, it is also found that for state-owned enterprises, the carbon emission pressure brought about by digital transformation is higher than that of non-state-owned enterprises; environmental regulation can significantly restrain carbon emissions. This study reveals the real impact of enterprises' digital transformation on carbon emissions and provides a direction for enterprises to implement digital transformation and carbon emission reduction.
作者机构：
[Li, Huiqin; Tang, Yajiao; Zhu, Yulin; Song, Mengjia] Cent South Univ Forestry & Technol, Coll Econ, Changsha 410004, Peoples R China.;[Zhu, Yulin] Hunan Res Ctr High Qual Dev Ind Econ, Changsha 410004, Peoples R China.
通讯机构：
[Zhu, YL ] C;Cent South Univ Forestry & Technol, Coll Econ, Changsha 410004, Peoples R China.;Hunan Res Ctr High Qual Dev Ind Econ, Changsha 410004, Peoples R China.
关键词：
ecological zoning;zoning management policies;ecosystem service value;ecological risk;the Wuling Mountains area of Hunan Province
摘要：
Based on land use data from the Wuling Mountains area of Hunan Province for 2000, 2010, and 2020, we used tools such as frastats4.8 and ArcGIS10.8 to construct a model for assessing ecosystem service value and the ecological risk index. We divided the area into four regions based on ecosystem service value and ecological risk indicators, which served as the foundation for ecological zoning and a proposed strategy for an ecological security pattern that suits the ecology of the region. The results showed a general increase in both ecosystem service value and ecological risk in the study area from 2000 to 2020. The annual ecosystem service value exceeded CNY 300 x 109, with forests providing more than 77% of this value, and the regulating services value accounted for 68% of the total value. The mean ecological risk indexes for the periods of 2000, 2010, and 2020 were 0.0384, 0.0383, and 0.0395, respectively. The sizes of the four zones within the study area remained relatively stable: the ecological barrier zone accounted for more than 53% over three years; the ecological improvement zone, approximately 32%; the ecological control zone comprised 8.62% of the total area in 2000, and this proportion rose to 9.56% in 2020. The ecological conservation zone had the smallest proportion of the total area among the four zones. Our research provides a comprehensive analytical framework for constructing ecological security patterns in other developing countries and offers a new perspective for regional ecological zoning management and conservation planning.
期刊：
International Journal of Finance & Economics,2023年28(2):1201-1213 ISSN：1076-9307
通讯作者：
Huang, Chuangxia
作者机构：
[Yang, Xin; Chen, Shan; Huang, Chuangxia] Changsha Univ Sci & Technol, Sch Math & Stat, Hunan Prov Key Lab Math Modeling & Anal Engn, Changsha 410114, Hunan, Peoples R China.;[Liu, Hong] Cent South Univ Forestry & Technol, Sch Econ, Changsha, Hunan, Peoples R China.;[Yang, Xiaoguang] Chinese Acad Sci, Acad Math & Syst Sci, Beijing, Peoples R China.
通讯机构：
[Huang, Chuangxia] C;Changsha Univ Sci & Technol, Sch Math & Stat, Hunan Prov Key Lab Math Modeling & Anal Engn, Changsha 410114, Hunan, Peoples R China.
关键词：
Financial institution network;jump volatility;panel data regression model
摘要：
The identification of systemically important financial institutions (SIFIs) is an important measure to deal with systemic risks. To achieve this goal, we first use generalized variance decomposition method and granger causality test to construct jump volatility spillover networks of Chinese financial institutions based on the 5-min high-frequency data. Then, out-strength and in-strength are adopted to analyze the SIFI. Finally, we use panel data regression model to investigate the determinant of the SIFIs. The empirical results show that: (a) The network density reaches a peak when the financial system under pressure during the China's stock market disaster of 2015. (b) Large banks and insurances usually display systemic importance, while some small financial institutions are also SIFIs due to their high value of out-strength and in-strength. (c) There are obvious differences in the factors that affect the out-strength and in-strength based on panel data regression model, but turnover rate, jump volatility, firm size and growth rate of total assets are the common driving factors.
期刊：
POLISH JOURNAL OF ENVIRONMENTAL STUDIES,2023年32(6):5347-5363 ISSN：1230-1485
通讯作者：
Tao, L
作者机构：
[Tong, Jing] Cent South Univ Forestry & Technol, Coll Bangor, 498 Shaoshan South Rd, Changsha 410004, Hunan, Peoples R China.;[Tao, Li; Tao, L] Cent South Univ Forestry & Technol, Coll Econ, 498 Shaoshan South Rd, Changsha 410004, Hunan, Peoples R China.
通讯机构：
[Tao, L ] C;Cent South Univ Forestry & Technol, Coll Econ, 498 Shaoshan South Rd, Changsha 410004, Hunan, Peoples R China.
关键词：
carbon emissions;well-being performance;well-being performance of carbon emissions;Super-SBM model;industrial structure
摘要：
The rapid economic development has caused a continuous increase in carbon emissions, which has led to a series of problems such as environmental pollution and climate change, resulting in a decline in economic, ecological and social welfare. The increase in human well-being generated by each unit of carbon emissions can be expressed in terms of well-being performance of carbon emissions. Based on provincial panel data from 2005 to 2020 in China, this paper provides an in-depth exploration of the impact of the rationalization and upgrading of industrial structure on the well-being performance of carbon emissions by constructing a fixed-effect model. The conclusions of the study are as follows: (1) The rationalization of industrial structure at this stage has not yet had an impact on well-being performance of carbon emissions, and upgrading can significantly contribute to the improvement of well-being performance of carbon emissions. (2) the impact of industrial structure upgrading on well-being performance of carbon emissions is heterogeneous. Advancedization only significantly contributes to the well-being performance of carbon emissions of eastern regions, regions with low natural resource endowments, regions with high external dependence and regions with high environmental awareness. The conclusion of the paper provides an important reference for other countries to optimize the industrial structure to improve well-being performance.
摘要：
The firefly algorithm (FA) has gained widespread attention and has been widely applied because of its simple structure, few control parameters and easy implementation. As the traditional FA lacks a mutation mechanism, it tends to fall into local optima, leading to premature convergence, thus affecting the optimization accuracy. To address these limitations, from the perspective of population diversity, a complex network-based FA (CnFA) with scale-free properties is proposed in this paper. The scale-free properties of complex networks effectively ensure the diversity of populations to guide the populations in their search, thus avoiding random interactions of information among populations that could lead to superindividuals controlling the entire population. The property of the power-law distribution of nodes in complex networks is exploited to effectively avoid the premature convergence of the FA and falling into local optima. To verify the search performance of CnFA, we compared the FA and its variants, as well as multiple competitive approaches, on 30 different-dimension benchmark function optimization tasks and two time series prediction tasks. The experimental results and statistical analysis show that CnFA achieves satisfactory performance due to the better balance between exploitation and exploration in the search process. Additionally, we extended the proposed method to two other population-based algorithms, and the experimental results verify that the complex network -based mechanism can enhance the performance of not only the FA but also other population-based evolutionary algorithms.(c) 2023 Elsevier B.V. All rights reserved.
