De-greening of Credit Allocation and Credit Risk under Systemic Financial Stress
LIN Shihan, YANG Zihui, DAI Zhiying, WEN Xuelian
School of Finance, Shanghai University of Finance and Economics; Shanghai Institute of International Finance and Economics; Advanced Institute of Finance, Sun Yat-sen University; School of Economics and Management, South China Normal University
Summary:
The Fourth Plenary Session of the 20th Central Committee of the Communist Party of China emphasized the imperative to “accelerate the green transition in all areas of economic and social development in an effort to build a beautiful China”. This transition, however, typically entails substantial, long-term financial commitments. In the face of heightened global financial pressures and the declining profitability of banks, credit resources are currently shifting from green to non-green sectors, increasing the pressure of “de-greening”. Against this backdrop, optimizing credit allocation and guiding financial resources to support the green economy have become critical challenges for China’s economic and financial strategy. To address this, this paper is the first to propose a novel index of “De-greening of Credit Allocation (DGCA)”, which provides a quantitative measure of the extent to which credit resources shift from green to brown firms within Chinese provinces, and further investigates the relationship among systemic financial stress, credit de-greening, and credit risk. In China’s bank-dominated financial system,credit allocation plays a pivotal role in facilitating the transition toward a green economy. However, as global systemic financial pressures intensify, empirical research fails to provide clear and systematic conclusions regarding whether credit resources shift away from green sectors and whether such shifts generate latent financial risks. Although a growing number of studies have examined resource reallocation under financial stress scenarios, most of them focus on capital markets such as equities and bonds, while paying limited attention to credit allocation. Moreover, prior research lacks both a clear consensus on the direction of financial resource reallocation and a systematic evaluation of the risks it entails. To this end, this paper establishes an analytical framework of “systemic financial stress - credit de-greening - credit risk,” which sheds light on the internal logic of credit reallocation in periods of financial stress and further explores the financial vulnerabilities arising from credit de-greening. These findings offer important implications for enhancing credit support for the green transition and safeguarding financial stability during the transition process. Specifically, this paper constructs a provincial-level DGCA index based on a sample consisting of 4,652 Chinese listed firms from 2010Q1 to 2023Q1. The value of the DGCA index appears to be higher in several western provinces, implying tighter credit de-greening constraints on the local firms that may hinder the low-carbon transition. Empirical analysis reveals that systemic financial stress has a significantly positive effect on the DGCA index. Banks’ “short-termism” serves as the primary channel: as financial pressure intensifies, banks shift their focus from long-term sustainability to short-term economic returns, discount the long-run benefits of green firms, and interpret the declines in green firms’ stock returns as signals of short-term deterioration, thereby tightening green credit and inducing credit de-greening. Heterogeneity analysis indicates that the effect of financial stress on credit de-greening is more pronounced during economic downturns and periods of high policy uncertainty. We also decompose financial stress series into positive and negative variations and find that credit de-greening significantly worsens as financial pressure rises but is difficult to reverse when financial pressure decreases. Further analysis reveals that an increase in the credit de-greening index significantly amplifies credit risk. In particular, the DGCA index exerts a significantly positive effect on the credit risk of green firms, while this effect is negative but statistically insignificant for brown firms. These results indicate that credit de-greening primarily drives overall credit risk by increasing the default likelihood of green firms. Spatial Durbin model estimates reveal significant direct, indirect, and total effects of the DGCA index on most credit risk indicators, suggesting that credit de-greening in a given province not only significantly increases local default probabilities, but also generates positive spatial spillovers to neighboring provinces, thereby exacerbating regional credit risk. Based on these findings, the paper offers three key policy recommendations. First, greater attention should be paid to the issue of credit de-greening in western regions, with enhanced targeted credit support for green industries in key areas. Second, counter-cyclical policy efforts should be strengthened to mitigate the driving effect of systemic financial stress on credit de-greening. Third, inter-provincial coordination in financial regulation should be enhanced to prevent the cross-regional transmission of risks arising from localized credit de-greening.
林师涵, 杨子晖, 戴志颖, 温雪莲. 系统性金融压力下的信贷“脱绿”与信用风险研究[J]. 金融研究, 2026, 549(3): 20-38.
LIN Shihan, YANG Zihui, DAI Zhiying, WEN Xuelian. De-greening of Credit Allocation and Credit Risk under Systemic Financial Stress. Journal of Financial Research, 2026, 549(3): 20-38.
[1] 李力、吴施美和陈贞竹,2023,《极端天气风险与宏观经济波动——基于网络关联与空间溢出双重视角》,《金融研究》第9期,第58～75页。 [2] 栾稀、朱浣君、彭雨洁和徐奇渊,2024,《地缘政治冲击下的主权债务风险:溢出效应和传导渠道》,《金融研究》第8期,第1～19页。 [3] 清华大学国家金融研究院金融与发展研究中心课题组,2019,《中国系统性金融压力的监测》,《国际金融研究》第12期,第3～12页。 [4] 谭小芬、耿亚莹和苟琴,2024,《跨境资本流动、信贷配置风险与银行体系稳定》,《经济研究》第1期,第129～147页。 [5] 杨子晖、李东承和陈雨恬,2024,《金融市场的“绿天鹅”风险研究——基于物理风险与转型风险的双重视角》,《管理世界》第2期,第47～67页。 [6] 中国人民银行广东省分行课题组和张奎,2025,《气候金融风险的传染机制与政策应对——基于金融加速器的模拟分析》,《南方金融》第2期,第3～25页。 [7] Aglietta, M., and É. Espagne, 2016, “Climate and Finance Systemic Risks, More than an Analogy？ The Climate Fragility Hypothesis”, CEPII, Centre d'etudes Prospectives Et D'informations Internationales. [8] Avramov, D., S. Cheng, A. Lioui, and A. Tarelli, 2022, “Sustainable Investing with ESG Rating Uncertainty”, Journal of Financial Economics, 145(2), pp.642～664. [9] Bansal, R., D. Wu, and A. Yaron, 2022, “Socially Responsible Investing in Good and Bad Times”, The Review of Financial Studies, 35(4), pp.2067～2099. [10] Bird, A., A. Ertan, S. A. Karolyi, and T. G. Ruchti, 2022, “Short-termism Spillovers from the Financial Industry”, The Review of Financial Studies, 35(7), pp.3467～3524. [11] Bolton, P., M. Despres, L. A. P. Da Silva, F. Samama, and R. Svartzman, 2020, “The Green Swan”, BIS Books. [12] Brandão-Marques, L., Q. Chen, C. Raddatz, J. Vandenbussche, and P. Xie, 2022, “The Riskiness of Credit Allocation and Financial Stability”, Journal of Financial Intermediation, 51, 100980. [13] Butler, A. W., and H. Yi, 2022, “Aging and Public Financing Costs: Evidence from US Municipal Bond Markets”, Journal of Public Economics, 211, 104665. [14] Che, S., M. Tao, E. Silva, M. S. Sheng, C. Zhao, and J. Wang, 2024, “Financial Misallocation and Green Innovation Efficiency: China's Firm-level Evidence”, Energy Economics, 136, 107697. [15] Chu, Y., S. Deng, and C. Xia, 2020, “Bank Geographic Diversification and Systemic Risk”, The Review of Financial Studies, 33(10), pp.4811～4838. [16] Döttling, R., and S. Kim, 2024, “Sustainability Preferences under Stress: Evidence from COVID-19”, Journal of Financial and Quantitative Analysis, 59(2), pp.435～473. [17] Falato, A., and D. Scharfstein, 2025, “The Stock Market and Bank Risk‐taking”, The Journal of Finance, 80(6), pp. 3223～3261. [18] Fatica, S., and R. Panzica, 2024, “Sustainable Investing in Times of Crisis: Evidence from Bond Holdings and the COVID-19 Pandemic”, Journal of Banking & Finance, 166, 107238. [19] Fiorillo, P., A. Meles, D. Salerno, and V. Verdoliva, 2024, “Geopolitical Turmoil and Investor Green Preference: Evidence from the Corporate Bond Market”, Journal of International Money and Finance, 149, 103218. [20] Gormley, T. A., and D. A. Matsa, 2014, “Common Errors: How to (and Not to) Control for Unobserved Heterogeneity”, The Review of Financial Studies, 27(2), pp. 617～661. [21] Greenwood, R., and S. G. Hanson, 2013, “Issuer Quality and Corporate Bond Returns”, The Review of Financial Studies, 26(6), pp.1483～1525. [22] Grieser, W., C. Hadlock, J. LeSage, and M. Zekhnini, 2022, “Network Effects in Corporate Financial Policies”, Journal of Financial Economics, 144(1), pp. 247～272. [23] Huang, Y., and P. Luk, 2020, “Measuring Economic Policy Uncertainty in China”, China Economic Review, 59, 101367. [24] Islam, E., and M. Singh, 2025, “Geographic Diversification, Climate Risk, and Bank Lending: Evidence from Farm Loans”, Journal of Financial Intermediation, 101152. [25] Lee, L. F., and J. Yu, 2010, “Estimation of Spatial Autoregressive Panel Data Models with Fixed Effects”, Journal of Econometrics, 154(2), pp. 165～185. [26] LeSage, J. and R. K. Pace, 2009, “Introduction to Spatial Econometrics”, Chapman and Hall/CRC. [27] Milbradt, K., and M. Oehmke, 2015, “Maturity Rationing and Collective Short-termism”, Journal of Financial Economics, 118(3), pp. 553～570. [28] Nakhli, M. S., B. Gaies, W. Hemrit, and J. M. Sahut, 2024, “Twenty-year Tango: Exploring the Reciprocal Influence of Macro-financial Instability and Climate Risks”, Journal of Economic Behavior & Organization, 220, pp.717～731. [29] Neagu, F., L. Tatarici, F. Dragu, and A. Stamate, 2024, “Are Green Loans Less Risky？ Micro-evidence from a European Emerging Economy”, Journal of Financial Stability, 70, 101208. [30] Owolabi, A., M. M. Mousavi, G. Gozgor, and J. Li, 2024, “The Impact of Carbon Risk on the Cost of Debt in the Listed Firms in G7 Economies: The Role of the Paris Agreement”, Energy Economics, 139, 107925. [31] Safiullah, M., M. N. Kabir, and M. D. Miah, 2021, “Carbon Emissions and Credit Ratings”, Energy Economics, 100, 105330. [32] Vereshchagina, G., 2023, “Financial Constraints and Economic Development: The Role of Firm Productivity Investment”, Review of Economic Dynamics, 51, pp.322～342. [33] Wen, H., C.C.Lee, and F.Zhou, 2021, “Green Credit Policy, Credit Allocation Efficiency and Upgrade of Energy-intensive Enterprises”, Energy Economics, 94, 105099. [34] Zhang, D., D. Bai, and Y. Wang, 2024, “Green vs. Brown: Climate Risk Showdown-Who's Thriving, Who's Diving？”, Journal of International Money and Finance, 149, 103198.
