In the context of globalization and regional economic integration, urban agglomerations have become key spatial units driving economic growth in East Asia. This paper investigates the spatial spillover effects and coordinated development paths of East Asian urban agglomerations. This study constructs an evaluation index system based on economic resilience, transportation hubs, and the digital economy, and then applies the Spatial Durbin Model (SDM) to conduct an in-depth empirical analysis on panel data from major East Asian urban agglomerations. The findings reveal a significant positive spatial autocorrelation in the economic development of East Asian urban agglomerations,while the spillover effects of the digital economy and transportation infrastructure far exceed their local direct effects. However, the homogeneity of traditional industries at a specific development stage has triggered a significant "siphon effect," widening regional development disparities. Based on these findings, this paper proposes an optimization path for regional coordinated development by reshaping a multi-centered networked spatial structure and strengthening the dual-track coordination between digital and green development. Thus, this study offers solid empirical evidence to help policymakers formulate scientific spatial planning, overcome regional administrative barriers, and achieve inclusive and sustainable economic growth across East Asia.
As one of the informal environmental regulations, the environmental information disclosure system plays a significant role in energy conservation and emissions reduction due to its flexible and efficient nature. Based on urban panel data from 2012 to 2022, this study conducts an empirical analysis of the carbon emission reduction effects of urban green innovation. The analysis focuses on 120 cities nationwide included in the Pollution Source Oversight Information Disclosure Index, as reported in the annual report jointly published by the Institute of Public and Environmental Affairs (IPE) and the Natural Resources Defense Council (NRDC).Furthermore, this study examines the moderating role of environmental information disclosure in the carbon emission reduction effects of urban green innovation, providing valuable insights for further exploring the effectiveness of promoting and implementing the environmental information disclosure system.
This paper examines 606 listed companies on the STAR Market from August 2020 to December 2024 and systematically tests the applicability of the CAPM, the Fama-French three-factor model, and the five-factor model in this emerging market. This paper constructed the size factor using log-transformed market capitalization, proxy the book-to-market ratio with the inverse of the price-to-book ratio to build the value factor, and construct the profitability and investment factors based on return on equity and total asset growth rate, respectively. The empirical analysis employs time-series regression, the Fama-MacBeth cross-sectional regression, and sub-sample robustness tests. The results show that the CAPM exhibits significantly insufficient explanatory power in the STAR Market, with an average R² of merely 0.015. The three-factor model improves explanatory power by approximately 19 times compared with the CAPM, and both the size and value factors demonstrate significant explanatory power, though the value premium direction is opposite to that in the main board market. The five-factor model achieves a marginal increment of 15.0%, while the profitability and investment factors show relatively limited explanatory power. Factor risk premiums fail to reach conventional significance levels due to the short sample period, but sub-sample robustness tests confirm the stability of this conclusions. This study provides empirical references for investor risk management and regulatory efforts to improve the multi-tier capital market structure.
This paper focuses on the credit risk of the commercial banks' supply chain finance business. It traces the concept, theory, and three stages of development of the supply chain finance, analyzes three main models for Chinese commercial banks—the inventory, accounts receivable, and prepayments—and identifies two core credit risks: excessive reliance on the credit of core enterprises and fluctuations in the value of collateralized inventory. Using the Agricultural Bank of China's "Chain-Link Loan" as an example, this paper analyzes its product content and risk control methods based on a "high entry threshold + electronic business platform," and proposes optimization suggestions. Commercial banks should accelerate the digitalization of their business, build risk control systems based on big data, raise the entry standards for core enterprises and financing enterprises, and improve their post-loan management systems. Through comprehensive risk control, they can reduce credit risk and promote the sustainable development of supply chain finance.
