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Chuanzhe Liu
School of Economics and Management, China University of Mining and Technology, Xuzhou 221116, China

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Journal article
Published: 26 May 2021 in Sustainability
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In order to explore the influence of green credit on the optimization and rationalization of the industrial structure in China, based on the relevant data of the green credit balance, interest expenditure in six high-energy-consuming industries, and industrial structure in China from 2007–2019, the paper first measured the green credit index and the index of industrial structure optimization and rationalization by the methods of entropy weight and Theil index. Then, the coupling model was adopted to study the coupling degree and the coupling coordination degree between them, and the regression model was employed to further study the influence coefficient of green credit on the optimization and rationalization of industrial structure. Research showed that the degree of coupling between green credit and industrial structure rationalization presents three stages—extremely low coupling, low coupling, and moderate coupling—and the degree of coupling coordination presents two stages—extremely low coordination and low coordination. Similarly, the degree of coupling between them presents two stages—extremely low coupling and low coupling—and the degree of coupling coordination presents two stages—extremely low coordination and low coordination. Regression analysis showed that the influence coefficients of the green credit index on rationalization and optimization of industrial structure were 0.56 and 0.03, respectively, which supported the conclusion that the coupling degree between the former two is higher than that between the latter two on the one hand, and made it clear that green credit positively and effectively guides the rational allocation of resources and promotes secondary and tertiary industries on the other hand.

ACS Style

Chuan Shao; Jia Wei; Chuanzhe Liu. Empirical Analysis of the Influence of Green Credit on the Industrial Structure: A Case Study of China. Sustainability 2021, 13, 5997 .

AMA Style

Chuan Shao, Jia Wei, Chuanzhe Liu. Empirical Analysis of the Influence of Green Credit on the Industrial Structure: A Case Study of China. Sustainability. 2021; 13 (11):5997.

Chicago/Turabian Style

Chuan Shao; Jia Wei; Chuanzhe Liu. 2021. "Empirical Analysis of the Influence of Green Credit on the Industrial Structure: A Case Study of China." Sustainability 13, no. 11: 5997.

Journal article
Published: 25 March 2021 in Sustainability
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We consider a coal supply chain with a coal enterprise and a manufacturer, where the coal enterprise invests in clean coal technology, and the manufacturer invests in carbon reduction technology. The government offers subsidies for the investments of clean coal technology and carbon reduction technology. We examine optimal clean coal technology inputs in a coal enterprise and carbon reduction quantity in a manufacturer under the modes of coal-enterprise-led and manufacturer-led, respectively, using a Stackelberg game theory model. We obtain some interesting results. First, carbon reduction by the manufacturer is restrained when clean coal technology cost and carbon reduction cost are increased, regardless of the dominant modes, and clean coal technology input decreases when clean coal technology cost increases; however, a high carbon reduction cost has no effect on clean coal technology input when the manufacturer leads. Second, the clean coal technology subsidy for coal enterprises promotes clean coal technology inputs and carbon reductions, and the carbon reduction subsidy encourages carbon reduction without supporting clean coal technology input. Last, carbon reduction performance is better achieved under the manufacturer-led model than the coal-enterprise-led model. However, it should be noticed that the capital resource only relies on government subsidy in this article. In the future, this study could be used for green supply chain investment, and could be helpful for sustainability development.

ACS Style

Bowen Da; Chuanzhe Liu; Nana Liu; Sidun Fan. Strategies of Two-Level Green Technology Investments for Coal Supply Chain under Different Dominant Modes. Sustainability 2021, 13, 3643 .

AMA Style

Bowen Da, Chuanzhe Liu, Nana Liu, Sidun Fan. Strategies of Two-Level Green Technology Investments for Coal Supply Chain under Different Dominant Modes. Sustainability. 2021; 13 (7):3643.

Chicago/Turabian Style

Bowen Da; Chuanzhe Liu; Nana Liu; Sidun Fan. 2021. "Strategies of Two-Level Green Technology Investments for Coal Supply Chain under Different Dominant Modes." Sustainability 13, no. 7: 3643.

Journal article
Published: 12 August 2020 in Journal of Cleaner Production
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In this study, we first examine the dynamic dependence structure between green bonds (GBs) and several global and sectoral clean energy (CE) markets by using several time-invariant and time-varying copula approaches over the period from 5 July 2011 to 24 February 2020. We then apply conditional value-at-risk (CoVaR) and delta CoVaR to capture downside and upside risk spillovers from CE to GB, and vice versa. Our empirical analysis shows that there is positive time-varying average and tail dependence between GBs and CE stock markets. Moreover, extreme downward or upward movements in the CE stock market have a spillover effect on the GB market, and vice versa. Furthermore, the risk spillover between these markets is asymmetric. These results make an important contribution to policymakers and environmentally friendly investors with GB positions by adding unexpected tail losses. It is critical for the GB investors to invest their capital effectively in economic activities that are consistent with a low-carbon economy.

ACS Style

Nana Liu; Chuanzhe Liu; Bowen Da; Tong Zhang; Fangyuan Guan. Dependence and risk spillovers between green bonds and clean energy markets. Journal of Cleaner Production 2020, 279, 123595 .

AMA Style

Nana Liu, Chuanzhe Liu, Bowen Da, Tong Zhang, Fangyuan Guan. Dependence and risk spillovers between green bonds and clean energy markets. Journal of Cleaner Production. 2020; 279 ():123595.

Chicago/Turabian Style

Nana Liu; Chuanzhe Liu; Bowen Da; Tong Zhang; Fangyuan Guan. 2020. "Dependence and risk spillovers between green bonds and clean energy markets." Journal of Cleaner Production 279, no. : 123595.

Journal article
Published: 04 May 2020 in Sustainability
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Green finance (GF) regards social responsibility and environmental protection interests as the core of development and has become a new growth point and a new engine for promoting the development of the green economy (GE). To more accurately grasp the coordination between GF and the GE, the selection of appropriate indicators and feasible methods is worth exploring. Aiming at sustainable development by evaluating the coupling coordination between GF and the GE by means of a comprehensive index system and an integrated approach, this study establishes a coupling coordination degree model based on panel data of 30 Chinese provinces over the period 2007–2016. Furthermore, it evaluates the spatial distribution difference and dynamic evolution trend of the coordination by introducing global/local spatial autocorrelation, a space Markov chain, and a local indicators of spatial association (LISA) Markov chain. According to the research results, the coupling coordination degrees of the provinces exhibit gradual upward trends, and most regions in China are in a barely coordinated state at present. The coordination degree of GF and the GE shows strong spatial dependence overall, and partially presents the characteristics of “high-high (HH)” and “low-low (LL)” clustering patterns. The forecast results show that the future coordination of GF and the GE will remain stable and be affected by the coordinated development of surrounding areas.

ACS Style

Nana Liu; Chuanzhe Liu; Yufei Xia; Yi Ren; Jinzhi Liang. Examining the Coordination Between Green Finance and Green Economy Aiming for Sustainable Development: A Case Study of China. Sustainability 2020, 12, 3717 .

AMA Style

Nana Liu, Chuanzhe Liu, Yufei Xia, Yi Ren, Jinzhi Liang. Examining the Coordination Between Green Finance and Green Economy Aiming for Sustainable Development: A Case Study of China. Sustainability. 2020; 12 (9):3717.

Chicago/Turabian Style

Nana Liu; Chuanzhe Liu; Yufei Xia; Yi Ren; Jinzhi Liang. 2020. "Examining the Coordination Between Green Finance and Green Economy Aiming for Sustainable Development: A Case Study of China." Sustainability 12, no. 9: 3717.

Journal article
Published: 05 September 2019 in Journal of Cleaner Production
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Carbon emissions become serious accompanying the urbanisation, which may damage the global environment through gas flow and other spatial spillover effects. Thus, it is important to study the spatial interactive effects of urbanisation and other factors on carbon emissions. Considering the rapid urbanisation in China, this paper studies the regional disparity and spatial spillover effects of urbanisation and carbon emissions of China. The law of geography is introduced to explain the mechanism of spatial effects of carbon emission theoretically. By combining STIRPAT model with spatial Dubin model, the spatial interactive effects among both independent variables and dependent variables can be clarified empirically. The results show that: (1) Technological limitation, wealth and population are the driving factors of carbon emissions, and are the most influential factors in the East, Middle and West parts of China in turn. (2) As urbanisation improves, its influence on local carbon emissions changes from positive to negative and then the negative influence becomes weaker. (3) Carbon emissions have strong spatial spillover effects among provinces. (4) Urbanisation, technology, wealth and population levels have different spatial interactive effects on carbon emissions in different parts of China. Accordingly, the influences of urbanisation and other factors on carbon emissions vary substantially among regions and have spatial spillover effects among provinces, suggesting different policies for different regions with different surroundings to reduce carbon emissions.

ACS Style

Fengyun Liu; Chuanzhe Liu. Regional disparity, spatial spillover effects of urbanisation and carbon emissions in China. Journal of Cleaner Production 2019, 241, 118226 .

AMA Style

Fengyun Liu, Chuanzhe Liu. Regional disparity, spatial spillover effects of urbanisation and carbon emissions in China. Journal of Cleaner Production. 2019; 241 ():118226.

Chicago/Turabian Style

Fengyun Liu; Chuanzhe Liu. 2019. "Regional disparity, spatial spillover effects of urbanisation and carbon emissions in China." Journal of Cleaner Production 241, no. : 118226.

Journal article
Published: 10 July 2019 in Sustainability
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Using the slacks-based measure (SBM) directional distance function and constructing the Luenberger productivity index, we measure the green total factor productivity (GTFP) of China’s provinces under resource and environmental restrictions. At the same time, based on the provincial panel data, the threshold regression method is used to empirically analyze the impact of financial development on green total factor productivity and its threshold effect. The study explores how technological innovation, foreign direct investment (FDI), and environmental governance affect green total factor productivity, as well as how financial development plays a role in the direction and intensity of the impact, with a view to providing policy recommendations for promoting green economic development. The results show that: (1) during the sample period, China’s green total factor productivity had an overall upward trend, and pure technological progress was the main reason for the growth in the green all-factor growth rate; (2) taking financial development as a threshold dependent variable, financial development had a nonlinear, double-threshold effect on green total factor productivity and diminishing marginal efficiency; (3) the increase in financial development will help attract high-quality and low-pollution FDI inflows, and can exert a technology spillover from FDI to green total factor productivity; (4) the impact of technological innovation on green total factor productivity has a nonlinear feature, with significant positive and increasing marginal efficiency; and (5) there is a positive “U” relationship between environmental governance and green total factor productivity.

ACS Style

Yingying Zhou; Yaru Xu; Chuanzhe Liu; Zhuoqing Fang; Xinyue Fu; Mingzhao He. The Threshold Effect of China’s Financial Development on Green Total Factor Productivity. Sustainability 2019, 11, 3776 .

AMA Style

Yingying Zhou, Yaru Xu, Chuanzhe Liu, Zhuoqing Fang, Xinyue Fu, Mingzhao He. The Threshold Effect of China’s Financial Development on Green Total Factor Productivity. Sustainability. 2019; 11 (14):3776.

Chicago/Turabian Style

Yingying Zhou; Yaru Xu; Chuanzhe Liu; Zhuoqing Fang; Xinyue Fu; Mingzhao He. 2019. "The Threshold Effect of China’s Financial Development on Green Total Factor Productivity." Sustainability 11, no. 14: 3776.

Journal article
Published: 08 July 2019 in Sustainability
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A prominent claim within the literature is that corporate social responsibility-disclosured firms are fundamentally more resilient to financial shocks, relative to firms that take no corporate social responsibility action. To test this, we examine the impact of corporate social responsibility (CSR) information disclosure on financial constraints (FC). Our sample is composed of A-share publicly listed firms from Shanghai and Shenzhen in China during 2013–2017. We find that CSR disclosure influences negatively financial constraints. The quantile regression results also indicate that the influences would more obvious when a company faces stronger financial constraints. Further, CSR disclosure influences negatively financial constraints in financially opaque firms, and the effect of financial opaque on the relationship strengthens when the company faces great financial constraints. After considering the problems of missing variables and endogenous, changing the level of CSR and FC measurement, using 2SLS and two-step GMM methods, the conclusion is still robust. However, the results should not be generalized, since the sample was based on 434 A-share publicly listed firms for 2013–2017. From the perspective of FC, this study contributes to the literature in the field of CSR and expands the empirical research on the economic consequences of CSR. It also can encourage enterprises to voluntarily disclose social responsibility information and it is of great significance to promote the stable development of the capital market and society.

ACS Style

Nana Liu; Chuanzhe Liu; Quan Guo; Bowen Da; Linna Guan; Huiying Chen. Corporate Social Responsibility and Financial Performance: A Quantile Regression Approach. Sustainability 2019, 11, 3717 .

AMA Style

Nana Liu, Chuanzhe Liu, Quan Guo, Bowen Da, Linna Guan, Huiying Chen. Corporate Social Responsibility and Financial Performance: A Quantile Regression Approach. Sustainability. 2019; 11 (13):3717.

Chicago/Turabian Style

Nana Liu; Chuanzhe Liu; Quan Guo; Bowen Da; Linna Guan; Huiying Chen. 2019. "Corporate Social Responsibility and Financial Performance: A Quantile Regression Approach." Sustainability 11, no. 13: 3717.

Journal article
Published: 28 May 2019 in Sustainability
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For reliving the pressure of air pollution and corresponding the sustainability development policy in China, the companies are urging the creation of a highly productive low-carbon supply chain. This work uses price regulation, the cap-and-trade model, and a green financial policy background to establish a strategy for the coal–electric power supply chain with two-level carbon reduction and operation with financial constraints. A Stackelberg model was built to help investigate the rate of thermal order realization, the carbon reduction strategy in the coal enterprise, and the amount of thermal energy ordered in the electric enterprise. Results show that under a green financial background, a high bank loan discount rate for investing in carbon reduction technology equates to large carbon reduction in coal enterprises, large quantities of thermal energy ordered in electric enterprises, and high profit for coal and electric enterprises. However, the realization rate of thermal power ordered decreased when the price regulation become strict, thereby reducing the profit and carbon emission in electric enterprise. Therefore, the thermal price regulation level increased, the profit on both company and the production did not respond with sensitivity, and the government could encourage a low carbon model by controlling the bank loan rate.

ACS Style

Bowen Da; Chuanzhe Liu; Nana Liu; Yufei Xia; Fangming Xie. Coal-Electric Power Supply Chain Reduction and Operation Strategy under the Cap-and-Trade Model and Green Financial Background. Sustainability 2019, 11, 3021 .

AMA Style

Bowen Da, Chuanzhe Liu, Nana Liu, Yufei Xia, Fangming Xie. Coal-Electric Power Supply Chain Reduction and Operation Strategy under the Cap-and-Trade Model and Green Financial Background. Sustainability. 2019; 11 (11):3021.

Chicago/Turabian Style

Bowen Da; Chuanzhe Liu; Nana Liu; Yufei Xia; Fangming Xie. 2019. "Coal-Electric Power Supply Chain Reduction and Operation Strategy under the Cap-and-Trade Model and Green Financial Background." Sustainability 11, no. 11: 3021.

Articles
Published: 23 May 2019 in Applied Economics
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Financial risk derived from housing price fluctuations in China garnered much public concern recently. Based on the theoretical analyses of the transmission of financial risk from housing price fluctuations, this paper establishes panel spatial Durbin models to empirically analyse housing price fluctuations and financial risks transmission from a spatial economic perspective. Employing the panel provincial data from 1999–2015, we conduct an analysis on the 30 provinces in China as well as a comparison among the Eastern, Middle and Western regions of China. The results indicate that: (1) The soaring housing prices driven by bank credit, real estate developers’ heavy investment, local governments’ land revenue and individuals and households demands leads to financial risk in various sectors; (2) due to the ‘substitution effect’, the capital agglomeration in metropolis from bank credits, real estate developers, and individuals and households furthers the amassment of financial risks; (3) housing prices have a significant spatial contagion effect throughout the country, and financial risk could directly transmit across provinces through housing price fluctuations; (4) financial risks could indirectly transmit across provinces via the ‘imitative behaviour’ or ‘driving effect’ of different sectors for different regions of China.

ACS Style

Fengyun Liu; Honghao Ren; Chuanzhe Liu. Housing price fluctuations and financial risk transmission: a spatial economic model. Applied Economics 2019, 51, 5767 -5780.

AMA Style

Fengyun Liu, Honghao Ren, Chuanzhe Liu. Housing price fluctuations and financial risk transmission: a spatial economic model. Applied Economics. 2019; 51 (53):5767-5780.

Chicago/Turabian Style

Fengyun Liu; Honghao Ren; Chuanzhe Liu. 2019. "Housing price fluctuations and financial risk transmission: a spatial economic model." Applied Economics 51, no. 53: 5767-5780.

Journal article
Published: 16 May 2019 in International Journal of Environmental Research and Public Health
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The spatial autocorrelation analysis method was applied to panel data from the provinces of China (including autonomous regions and municipalities directly under the central government) for the period 2003 to 2016 in order to construct a spatial Durbin model of technological progress and financial support in relation to reductions in carbon emissions. The results show that China’s carbon intensity presents significant spatial spillover effects under different spatial weights, which indicates that the carbon intensity of a province is influenced not only by its own characteristics, but also by the carbon emission behaviors of geographically adjacent and economically similar provinces and regions. Financial structure, financial scale, and financial efficiency all have significant effects on carbon intensity within a province, while financial structure is also linked to carbon intensity in other regions, but financial scale has no significant spillover effect on carbon intensity in space. Areas with high financial efficiency can reduce their own carbon intensity as well as that of surrounding areas. The inter-regional spillover effect of technological progress on carbon intensity is stronger than the spillover effect, but there is a time lag.

ACS Style

Yingying Zhou; Yaru Xu; Chuanzhe Liu; Zhuoqing Fang; Jiayi Guo. Spatial Effects of Technological Progress and Financial Support on China’s Provincial Carbon Emissions. International Journal of Environmental Research and Public Health 2019, 16, 1743 .

AMA Style

Yingying Zhou, Yaru Xu, Chuanzhe Liu, Zhuoqing Fang, Jiayi Guo. Spatial Effects of Technological Progress and Financial Support on China’s Provincial Carbon Emissions. International Journal of Environmental Research and Public Health. 2019; 16 (10):1743.

Chicago/Turabian Style

Yingying Zhou; Yaru Xu; Chuanzhe Liu; Zhuoqing Fang; Jiayi Guo. 2019. "Spatial Effects of Technological Progress and Financial Support on China’s Provincial Carbon Emissions." International Journal of Environmental Research and Public Health 16, no. 10: 1743.

Journal article
Published: 30 March 2019 in Sustainability
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Commercial banks follow sustainable development strategies to provide green credit for companies. As the main target for banks to issue green credit, environmental protection companies carry out research and development (R&D) activities for the purposes of protecting the environment and sustainable development. Therefore, the R&D level of environmental protection companies is a measure of whether the green credit policy meets its purpose. Therefore, it is particularly important to study green credit from a company perspective. This paper used panel data from 24 environmental protection companies listed on the Shanghai and Shenzhen Stock Exchange from 2012 to 2017 and adopted a threshold model to examine the relationship between green credit and company R&D level. The results indicate that there is a positive but nonlinear relationship between green credit and company R&D levels. The empirical analysis through the threshold model shows that a threshold effect remains on the relationship and this threshold effect is caused by the levels of firm size, bank lending, and government subsidies.

ACS Style

Huiying Chen; Chuanzhe Liu; Fangming Xie; Tong Zhang; Fangyuan Guan. Green Credit and Company R&D Level: Empirical Research Based on Threshold Effects. Sustainability 2019, 11, 1918 .

AMA Style

Huiying Chen, Chuanzhe Liu, Fangming Xie, Tong Zhang, Fangyuan Guan. Green Credit and Company R&D Level: Empirical Research Based on Threshold Effects. Sustainability. 2019; 11 (7):1918.

Chicago/Turabian Style

Huiying Chen; Chuanzhe Liu; Fangming Xie; Tong Zhang; Fangyuan Guan. 2019. "Green Credit and Company R&D Level: Empirical Research Based on Threshold Effects." Sustainability 11, no. 7: 1918.

Journal article
Published: 25 March 2019 in Sustainability
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On the basis of the original camel rating system, this study added the green indicator and formed the G-CAMELS evaluation system (An improved rating system based on the CAMELS rating system to evaluate the business operation of financial institutions more comprehensively.) to comprehensively evaluate the competitiveness of commercial banks. It followed China’s current requirements for the sustainable development of commercial banks. In this paper, factor analysis, entropy methods, and dynamic evaluation models are used to obtain the ranking of competitiveness. In addition, according to the same steps as above, the comprehensive ranking based on the CAMELS evaluation system (A comprehensive rating system which is standardized, institutionalized and indexed for business operations of commercial banks and other financial institutions.) was obtained. The two ranking systems were compared. It is found that with the entropy weight method, in the G-CAMELS system, the weight of the green index is quite large, so it magnifies the impact of the financial industry on the environment. Compared with the original CAMELS system, the newly formed system will increase the ranking of state-owned banks and there is no significant change in the ranking of joint-stock banks. In order to improve the competitiveness of banks, state-owned banks should innovate their banking business and continue to implement the green credit policy; joint-stock banks should continue to seize the opportunity of green credit and expand profitability while paying attention to safety. In addition, the government could consider relaxing green credit standards for city commercial banks to ease pressure on banks.

ACS Style

Fangyuan Guan; Chuanzhe Liu; Fangming Xie; Huiying Chen. Evaluation of the Competitiveness of China’s Commercial Banks Based on the G-CAMELS Evaluation System. Sustainability 2019, 11, 1791 .

AMA Style

Fangyuan Guan, Chuanzhe Liu, Fangming Xie, Huiying Chen. Evaluation of the Competitiveness of China’s Commercial Banks Based on the G-CAMELS Evaluation System. Sustainability. 2019; 11 (6):1791.

Chicago/Turabian Style

Fangyuan Guan; Chuanzhe Liu; Fangming Xie; Huiying Chen. 2019. "Evaluation of the Competitiveness of China’s Commercial Banks Based on the G-CAMELS Evaluation System." Sustainability 11, no. 6: 1791.

Journal article
Published: 09 November 2018 in Sustainability
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This study uses data from seven countries with high energy consumption levels in 1997–2016 (i.e., the US, China, Japan, Canada, South Korea, Germany, and France) to establish a panel threshold model and analyze the multiple threshold effects of new energy consumption transformation on economic growth. Research results show the non-linear impact of new energy consumption transformation on economic growth. On the one hand, the transformation of new energy consumption will occasionally bring economic costs, thereby resulting in a negative impact on economic growth. On the other hand, economic cost occasionally disappears, thereby resulting in the positive impact of the transformation of new energy consumption on economic growth. This study proposes that economic cost is affected by the levels of research and development (R&D), economic development, and traditional energy dependence, therefore, we use these three variables as threshold variables. Threshold variable is essential in a panel threshold model. The behavioral varies of model can be predicted when threshold variable is at different ranges of levels. In other words, the behavior of panel threshold model may change as the level of threshold variable changes. In particular, when the R&D level is used as a threshold variable, the impact of new energy consumption transformation on economic growth will change from negative to positive as the level of R&D increases. We present a similar conclusion when the level of economic development is used as a threshold variable. However, when the level of traditional energy dependence is used as the threshold variable, the impact of new energy consumption transformation on economic growth will change from positive to negative as the level of traditional energy dependence increases.

ACS Style

Fangming Xie; Chuanzhe Liu; Huiying Chen; Ning Wang. Threshold Effects of New Energy Consumption Transformation on Economic Growth. Sustainability 2018, 10, 4124 .

AMA Style

Fangming Xie, Chuanzhe Liu, Huiying Chen, Ning Wang. Threshold Effects of New Energy Consumption Transformation on Economic Growth. Sustainability. 2018; 10 (11):4124.

Chicago/Turabian Style

Fangming Xie; Chuanzhe Liu; Huiying Chen; Ning Wang. 2018. "Threshold Effects of New Energy Consumption Transformation on Economic Growth." Sustainability 10, no. 11: 4124.

Journal article
Published: 27 September 2018 in Sustainability
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The regional systemic financial risks driven by escalating urban housing prices have been of great concern recently. Based on the theoretical analyses on the mechanism of formation of regional systemic financial risk driven by urban housing price fluctuations, this paper builds panel spatial economic models to empirically analyze the relationship between urban housing price fluctuations and regional systemic financial risks, in addition to their spatial linkages, in 13 cities in Jiangsu, a representative province of China. The empirical results show the following. (1) The excessive investment or speculation of local governments, banks, real estate developers, individuals, and families on the housing market stimulate the escalation in urban housing prices, leading to the systemic financial risks; (2) Urban housing prices and the land supply price of local governments have strong spatial contagion effects among cities, which will diffuse risks to adjacent cities, causing regional systemic financial risk; (3) Compared with North Jiangsu, South Jiangsu has more serious investment expansion from real estate developers and stronger spatial contagion effects, suggesting the existence of heavier regional systemic financial risks derived from housing price fluctuations; (4) North Jiangsu has slightly stronger “imitative behavior” among local governments, and fewer “substitution effects” of central cities’ demand to adjacent cities’ demand than does South Jiangsu.

ACS Style

Fengyun Liu; Chuanzhe Liu; Honghao Ren. Urban Housing Price Fluctuations and Regional Systemic Financial Risks: Panel Spatial Economic Models in Jiangsu, China. Sustainability 2018, 10, 3452 .

AMA Style

Fengyun Liu, Chuanzhe Liu, Honghao Ren. Urban Housing Price Fluctuations and Regional Systemic Financial Risks: Panel Spatial Economic Models in Jiangsu, China. Sustainability. 2018; 10 (10):3452.

Chicago/Turabian Style

Fengyun Liu; Chuanzhe Liu; Honghao Ren. 2018. "Urban Housing Price Fluctuations and Regional Systemic Financial Risks: Panel Spatial Economic Models in Jiangsu, China." Sustainability 10, no. 10: 3452.

Journal article
Published: 01 March 2018 in Expert Systems with Applications
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ACS Style

Yufei Xia; Chuanzhe Liu; Bowen Da; Fangming Xie. A novel heterogeneous ensemble credit scoring model based on bstacking approach. Expert Systems with Applications 2018, 93, 182 -199.

AMA Style

Yufei Xia, Chuanzhe Liu, Bowen Da, Fangming Xie. A novel heterogeneous ensemble credit scoring model based on bstacking approach. Expert Systems with Applications. 2018; 93 ():182-199.

Chicago/Turabian Style

Yufei Xia; Chuanzhe Liu; Bowen Da; Fangming Xie. 2018. "A novel heterogeneous ensemble credit scoring model based on bstacking approach." Expert Systems with Applications 93, no. : 182-199.