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学者姓名：郭亚伟
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银行承担环境责任如何影响其信用风险是当今可持续发展背景下亟须研究的重要议题.本文从绿色信贷的视角出发,使用2005～2022年中国131家上市和非上市银行样本数据,构造倾向性得分匹配-多时点双重差分模型,分析承担环境责任如何影响银行的信用风险.研究发现,绿色信贷对商业银行的信用风险有显著降低作用.其次,调节机制分析发现,地区市场化水平对实施绿色信贷商业银行的风险水平具有正向调节作用,银行声誉水平起着负向调节作用.异质性分析表明,绿色信贷对已上市、资产规模较大、流动性充足、位于东部的商业银行而言,其信用风险降低效应更加显著.研究结论为深入理解银行承担环境责任的影响和完善商业银行信用风险监管体系提供了参考依据.
Keyword ：
信用风险 信用风险 倾向性得分匹配 倾向性得分匹配 商业银行 商业银行 多时点双重差分 多时点双重差分 绿色信贷 绿色信贷
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| GB/T 7714 | 郭亚伟 , 郭冰倩 , 林婉龄 et al. 承担环境责任如何影响银行信用风险? [J]. | 电子科技大学学报（社会科学版） , 2025 , 27 (1) : 83-102 . |
| MLA | 郭亚伟 et al. "承担环境责任如何影响银行信用风险?" . | 电子科技大学学报（社会科学版） 27 . 1 (2025) : 83-102 . |
| APA | 郭亚伟 , 郭冰倩 , 林婉龄 , 汪宁丽 . 承担环境责任如何影响银行信用风险? . | 电子科技大学学报（社会科学版） , 2025 , 27 (1) , 83-102 . |
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Using panel data on China’s carbon-covered listed enterprises, this study employs the Poisson pseudo-maximum likelihood with high-dimensional fixed effects (PPMLHDFE) approach to investigate the impact of the carbon emissions trading (CET) policy on firms’ green technology innovation. A novel categorization is proposed to classify the enterprises in carbon-covered industries into CET-included and non-CET-included groups. Results show that China’s CET policy plays a vital role in green technology innovation, with a stronger positive effect on CET-included enterprises. Furthermore, heterogeneity exists among eight carbon-covered industries. The green technology innovation of enterprises in the power and petrochemical industries, particularly the CET-included ones, is stimulated by the CET policy. However, it inhibits green technology innovation in the aviation and chemical industries and plays no role in other industries. Additionally, the effect exhibits heterogeneity across pilot areas. It is positive in Hubei and Tianjin, negative in Chongqing and Shanghai, and ineffective in Beijing, Guangdong, and Shenzhen. Finally, the effect of the CET policy on green technology innovation is stronger for state-owned enterprises than for non-state-owned ones. © 2024 Informa UK Limited, trading as Taylor & Francis Group.
Keyword ：
carbon-covered industries carbon-covered industries Carbon emissions trading Carbon emissions trading Cet-included and non-cet-included enterprises Cet-included and non-cet-included enterprises green technology innovation green technology innovation PPMLHDFE method PPMLHDFE method
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| GB/T 7714 | Guo, Y. , Lin, Y. , Yi, M. et al. How does China’s carbon emissions trading policy affect green technology innovation? Evidence from enterprises in carbon-covered industries [J]. | Applied Economics , 2024 . |
| MLA | Guo, Y. et al. "How does China’s carbon emissions trading policy affect green technology innovation? Evidence from enterprises in carbon-covered industries" . | Applied Economics (2024) . |
| APA | Guo, Y. , Lin, Y. , Yi, M. , You, W. . How does China’s carbon emissions trading policy affect green technology innovation? Evidence from enterprises in carbon-covered industries . | Applied Economics , 2024 . |
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Abstract ：
Using panel data on China's carbon-covered listed enterprises, this study employs the Poisson pseudo-maximum likelihood with high-dimensional fixed effects (PPMLHDFE) approach to investigate the impact of the carbon emissions trading (CET) policy on firms' green technology innovation. A novel categorization is proposed to classify the enterprises in carbon-covered industries into CET-included and non-CET-included groups. Results show that China's CET policy plays a vital role in green technology innovation, with a stronger positive effect on CET-included enterprises. Furthermore, heterogeneity exists among eight carbon-covered industries. The green technology innovation of enterprises in the power and petrochemical industries, particularly the CET-included ones, is stimulated by the CET policy. However, it inhibits green technology innovation in the aviation and chemical industries and plays no role in other industries. Additionally, the effect exhibits heterogeneity across pilot areas. It is positive in Hubei and Tianjin, negative in Chongqing and Shanghai, and ineffective in Beijing, Guangdong, and Shenzhen. Finally, the effect of the CET policy on green technology innovation is stronger for state-owned enterprises than for non-state-owned ones.
Keyword ：
carbon-covered industries carbon-covered industries Carbon emissions trading Carbon emissions trading Cet-included and non-cet-included enterprises Cet-included and non-cet-included enterprises green technology innovation green technology innovation O12 O12 O32 O32 PPMLHDFE method PPMLHDFE method Q55 Q55 Q56 Q56
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| GB/T 7714 | Guo, Yawei , Lin, Youfen , Yi, Meimei et al. How does China's carbon emissions trading policy affect green technology innovation? Evidence from enterprises in carbon-covered industries [J]. | APPLIED ECONOMICS , 2024 . |
| MLA | Guo, Yawei et al. "How does China's carbon emissions trading policy affect green technology innovation? Evidence from enterprises in carbon-covered industries" . | APPLIED ECONOMICS (2024) . |
| APA | Guo, Yawei , Lin, Youfen , Yi, Meimei , You, Wanhai . How does China's carbon emissions trading policy affect green technology innovation? Evidence from enterprises in carbon-covered industries . | APPLIED ECONOMICS , 2024 . |
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This study uses a wavelet-based quantile approach to document the effects of disaggregated oil shocks on the Chinese non-ferrous metal market under various time horizons and quantiles. Oil shocks are decomposed into supply, aggregate demand, and oil-specific demand shocks based on a structural vector autoregressive model. Empirical results suggest that oil shocks have notable impacts on the Chinese non-ferrous metal market in the middle and long term, whereas the impacts in the short term are not significant. The impacts remain remarkably heterogeneous across quantile levels in the long term. Oil supply shocks significantly affect the non-ferrous metal market positively, but only at higher quantiles. The positive effect of aggregate demand shocks and the negative effect of oil-specific demand shocks are significant at lower quantiles. Moreover, copper is susceptible to oil shocks, compared with aluminium. These findings provide several implications for policymakers and investors.
Keyword ：
C21 C21 C51 C51 heterogeneity heterogeneity L61 L61 non-ferrous metal market non-ferrous metal market Oil price shocks Oil price shocks Q43 Q43 wavelet-based quantile wavelet-based quantile
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| GB/T 7714 | Ren, Yinghua , Yang, Jiahui , Ying, Wanming et al. Do disaggregated oil price shocks drive the Chinese non-ferrous metal market across different time horizons and quantiles? [J]. | APPLIED ECONOMICS , 2024 . |
| MLA | Ren, Yinghua et al. "Do disaggregated oil price shocks drive the Chinese non-ferrous metal market across different time horizons and quantiles?" . | APPLIED ECONOMICS (2024) . |
| APA | Ren, Yinghua , Yang, Jiahui , Ying, Wanming , Guo, Yawei . Do disaggregated oil price shocks drive the Chinese non-ferrous metal market across different time horizons and quantiles? . | APPLIED ECONOMICS , 2024 . |
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Using the annual report text information of Chinese listed banks from 2007 to 2020, this paper constructs multiple kinds of spatial weight matrices from the perspective of business similarity to identify the systemic risk spillover channels. Furthermore, the Bayesian posterior probability methodology proposed by Debarsy and LeSage (2018) is employed to assess the relative impor-tance of each spillover channel. Besides, we discuss the risk spillovers of specific factors. The empirical results show that the loan type, loan region, investment industry, and income structure are all effective risk spillover channels, and the loan type channel is of the utmost importance. And also the spillover effects of bank-specific factors are identified and continuous. Our results are validated by robust analysis.
Keyword ：
Annual reports Annual reports Business similarity Business similarity Spatial spillover channels Spatial spillover channels Systemic risk Systemic risk
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| GB/T 7714 | Nan, Shijing , Wang, Minna , You, Wanhai et al. Making text count: Identifying systemic risk spillover channels in the Chinese banking sector using annual reports text [J]. | FINANCE RESEARCH LETTERS , 2023 , 55 . |
| MLA | Nan, Shijing et al. "Making text count: Identifying systemic risk spillover channels in the Chinese banking sector using annual reports text" . | FINANCE RESEARCH LETTERS 55 (2023) . |
| APA | Nan, Shijing , Wang, Minna , You, Wanhai , Guo, Yawei . Making text count: Identifying systemic risk spillover channels in the Chinese banking sector using annual reports text . | FINANCE RESEARCH LETTERS , 2023 , 55 . |
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This paper employs the spatial panel model to explore the interaction between globalization and economic complexity on CO2 emissions. To capture different potential mechanisms of diffusions, this paper builds hybrid W matrices based on trade distance and geographical distances. Further, to reduce small sample bias and capture "true interactions" across countries, the multiple imputation algorithm is used to address missingness pattern. This approach can efficiently enhance data quality and inferences validity. Furthermore, to distinguish the spatial spillover accurately, cross-sectional averages are used to solve the issue caused by the omission of common factors or shocks that affect differently the different spatial units. Results show that there is a positive spatial spillover of CO2 emissions from neighboring countries to the local country. Furthermore, the results show that globalization on its own has no significant effect on CO2 emissions in the local country but decreases CO2 emissions in neighboring countries. More importantly, when taking economic complexity into account, high level of economic complexity may decrease the negative indirect impact of globalization on carbon emissions. The more complex countries are to the economic structure, the less is the negative impact of globalization on carbon emissions in spatially related countries. These findings highlight that the role of economic complexity and spatial spillovers effects are imperative to be considered. Based on this study, several policy recommendations are provided.
Keyword ：
CO2 emissions CO2 emissions Common factors Common factors Economic complexity Economic complexity Globalization Globalization Multiple imputation Multiple imputation Spatial panel model Spatial panel model
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| GB/T 7714 | Nan, Shijing , Huo, Yuchen , You, Wanhai et al. Globalization spatial spillover effects and carbon emissions: What is the role of economic complexity? [J]. | ENERGY ECONOMICS , 2022 , 112 . |
| MLA | Nan, Shijing et al. "Globalization spatial spillover effects and carbon emissions: What is the role of economic complexity?" . | ENERGY ECONOMICS 112 (2022) . |
| APA | Nan, Shijing , Huo, Yuchen , You, Wanhai , Guo, Yawei . Globalization spatial spillover effects and carbon emissions: What is the role of economic complexity? . | ENERGY ECONOMICS , 2022 , 112 . |
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This paper explores the quantile-specific short-and long-term impacts of political risk and crude oil on stock price, and tests whether the effects are different for China and the US. The recently developed method of the QARDL by Cho et al. (2015) is used to address this issue by considering any nonlinear, asymmetric and endogenous characteristics. Empirical results show that the impacts of political risk and oil price are heterogeneous across distinct stock market circumstances and economic development levels. Before 2008 crisis, Chinese stock performance is mainly positively affected by itself, whereas crude oil shock plays a vital role in the US stock in the short term; in the long term, the effects of crude oil and political risk on the Chinese stock market are significant at few quantile, while crude oil shock has an optimistic influence on the US stock when the market is bullish or bearish. After the crisis, the influence of political risk becomes more remarkable, especially for China. An interesting phenomenon is that the past performance of US stock negatively affects the current stock at medium and high quantiles. Besides, the impact mechanisms of political risk on these two stocks appear quite different characteristics. Almost all individual components can not do much on the US market. However, the effects of individual components on Chinese stock differ according to the market environments. In the pre-crisis period, the impacts of all components except government actions and investment profile abide by the classic risk-return relationship. Whereas, exactly the opposite happens after the crisis. A decrease in most of political risk components is associated with higher stock price, supporting the political risk paradox. These empirical evidences provide further insight into how political risk and crude oil shocks transmit to stock market. (c) 2021 Elsevier B.V. All rights reserved.
Keyword ：
Crude oil price Crude oil price Political risk Political risk QARDL QARDL Stock market Stock market
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| GB/T 7714 | Guo, Yawei , Li, Jianping , Li, Yehua et al. The roles of political risk and crude oil in stock market based on quantile cointegration approach: A comparative study in China and US [J]. | ENERGY ECONOMICS , 2021 , 97 . |
| MLA | Guo, Yawei et al. "The roles of political risk and crude oil in stock market based on quantile cointegration approach: A comparative study in China and US" . | ENERGY ECONOMICS 97 (2021) . |
| APA | Guo, Yawei , Li, Jianping , Li, Yehua , You, Wanhai . The roles of political risk and crude oil in stock market based on quantile cointegration approach: A comparative study in China and US . | ENERGY ECONOMICS , 2021 , 97 . |
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This research explores the effects of income inequality and country risk on CO(2)emissions and examines whether the effects change across countries with different development stages or income levels. A new panel quantile regression approach is used to conduct a comprehensive analysis of the impacts of affecting factors on CO(2)emissions at various quantiles, while addressing econometric challenges such as endogeneity and heterogeneity. From a global perspective, we can conclude that the marginal impact of inequality on emissions drops constantly with decreasing country risk at 10th to 50th quantiles, which even performs negative, whereas at the other quantiles, the marginal impact of inequality always remains negative. When we focus on the different income groups, the nexus of inequality emissions is negative first and then positive with decrease of country risk in low-income countries but shows no significant in low-middle- and upper-middle-income countries. Additionally, we validate the detrimental impact of income inequality in upper-income countries. Besides, country risk adversely moderates the nexus of inequality and emissions in low- and upper-income countries. Empirical results confirm that the nexus of inequality emissions lies in country risk, income level, and existing emission degree. These findings provide some important recommendations for policy-makers.
Keyword ：
CO(2)emissions CO(2)emissions Country risk Country risk Endogeneity Endogeneity Income inequality Income inequality Panel quantile regression Panel quantile regression
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| GB/T 7714 | Guo, Yawei , You, Wanhai , Lee, Chien-Chiang . CO(2)emissions, income inequality, and country risk: some international evidence [J]. | ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH , 2020 , 29 (9) : 12756-12776 . |
| MLA | Guo, Yawei et al. "CO(2)emissions, income inequality, and country risk: some international evidence" . | ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 29 . 9 (2020) : 12756-12776 . |
| APA | Guo, Yawei , You, Wanhai , Lee, Chien-Chiang . CO(2)emissions, income inequality, and country risk: some international evidence . | ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH , 2020 , 29 (9) , 12756-12776 . |
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This paper investigates the interaction effects of income inequality and democracy on CO2 emissions. The spatial panel model, which accounts for the spatial spillover effects across countries, is used. Using the panel data covering 41 Belt and Road initiative countries, the results indicate significant positive spatial spillovers effect to country-level CO2 emission activity. The Kuznets Curve hypothesis, which assumes that reverse U relation presents between income and CO2 emissions, is identified. Empirical results provide evidence that democracy levels promote the nonlinear nexus between income inequality and CO2 emissions. High levels of inequality, ceteris paribus, in conjunction with poor democratic institutions are likely to result in higher pollution. The findings are robust to various robustness tests.
Keyword ：
Belt and road initiative countries Belt and road initiative countries CO2 emissions CO2 emissions Democracy Democracy Income inequality Income inequality Spatial panel data model Spatial panel data model
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| GB/T 7714 | You, Wanhai , Li, Yehua , Guo, Peng et al. Income inequality and CO2 emissions in belt and road initiative countries: the role of democracy [J]. | ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH , 2020 , 27 (6) : 6278-6299 . |
| MLA | You, Wanhai et al. "Income inequality and CO2 emissions in belt and road initiative countries: the role of democracy" . | ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 27 . 6 (2020) : 6278-6299 . |
| APA | You, Wanhai , Li, Yehua , Guo, Peng , Guo, Yawei . Income inequality and CO2 emissions in belt and road initiative countries: the role of democracy . | ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH , 2020 , 27 (6) , 6278-6299 . |
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