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Using Chinese A-share listed companies from 2006 to 2013 as the research sample, this paper empirically examines the impact and mechanism of the mandatory CSR disclosure policy on Chinese firms' risk-taking by combining the difference-in-differences (DID) approach. This study finds that the implementation of the policy increases firms' operating costs and leads to an increase in their financing constraints, which ultimately creates a disincentive for firms to take risks. Second, we also find that, relative to firms that do not disclose CSR reports, the sales revenues, R&D investment and investment levels of firms subject to CSR disclosure are significantly reduced, which may be the result of firms' tendency to operate conservatively. In addition, heterogeneity analyses suggest that the dampening effect of mandatory CSR disclosure policies on risk-taking is stronger for firms with higher financing costs and for non-state-owned firms. This study further explores the mechanism of the impact of mandatory CSR disclosure on firms' risk-taking, which will help the government to formulate and improve the information disclosure policy regarding driving the transformation of corporate development in the future. [ABSTRACT FROM AUTHOR]

Driving analysis of urban expansion (DAUE) is usually implemented to identify the driving factors and their corresponding driving effects/mechanisms for the expansion processes of urban land, aiming to provide scientific guidance for urban planning and management. Based on a thorough analysis and summarization of the development process and quantitative models, four major limitations in existing DAUE studies have been uncovered: (1) the interactions in hierarchical urban systems have not been fully explored; (2) the employed data cannot fully depict urban dynamic through finer social perspectives; (3) the employed models cannot deal with high-level feature correlations; and (4) the simulation and analysis models are still not intrinsically integrated. Four future directions are thus proposed: (1) to pay attention to the hierarchical characteristics of urban systems and conduct multi-scale research on the complex interactions within them to capture dynamic features; (2) to leverage remote sensing data so as to obtain diverse urban expansion data and assimilate multi-source spatiotemporal big data to supplement novel socio-economic driving factors; (3) to integrate with interpretable data-driven machine learning techniques to bolster the performance and reliability of DAUE models; and (4) to construct mechanism-coupled urban simulation to achieve a complementary enhancement and facilitate theory development and testing for urban land systems. [ABSTRACT FROM AUTHOR]