Purpose
The purpose of this study is to investigate whether and how policy uncertainty affect corporate environmental information disclosure.
Design/methodology/approach
This study conducts a difference-in-difference estimation and systematically investigates the relationship between policy uncertainty and corporate environmental information disclosure. The baseline regression results are robust to a series of robustness and endogeneity tests.
Findings
The authors show that firms located in cities with stronger policy uncertainty disclose less information on environmental issues. Furthermore, this negative relationship is stronger in the Midwest and in pre-industrial regions and for stated-owned firms and firms in highly polluting industries.
Practical implications
This study argues that policy uncertainty reduce the corporate disclosure of environmental information. Therefore, the results provide evidence on how to better emphasize the importance of green gross domestic product in the performance appraisal system for officials.
Social implications
This study confirms that corporate environmental disclosure is a response to public pressure. The results encourage the government and the public to increase corporate awareness of environmental protection.
Originality/value
This study contributes to the literature in the following ways. First, the authors provide a new perspective to study the relationship between policy uncertainty and corporate finance. Second, it contributes to the literature on corporate environmental information disclosure by linking policy uncertainty with firms’ disclosure of environmental information. Third, this study is a serious attempt to solve the problem of endogeneity between policy uncertainty and corporate environmental information disclosure.
Benefiting from recent advantages in location-aware technologies, movement data are becoming ubiquitous. Hence, numerous research topics with respect to movement data have been undertaken. Yet, the research of dynamic interactions in movement data is still in its infancy. In this paper, we propose a hybrid approach combining the multi-temporal scale spatio-temporal network (MTSSTN) and the continuous triangular model (CTM) for exploring dynamic interactions in movement data. The approach mainly includes four steps: first, the relative trajectory calculus (RTC) is used to derive three types of interaction patterns; second, for each interaction pattern, a corresponding MTSSTN is generated; third, for each MTSSTN, the interaction intensity measures and three centrality measures (i.e., degree, betweenness and closeness) are calculated; finally, the results are visualized at multiple temporal scales using the CTM and analyzed based on the generated CTM diagrams. Based on the proposed approach, three distinctive aims can be achieved for each interaction pattern at multiple temporal scales: (1) exploring the interaction intensities between any two individuals; (2) exploring the interaction intensities among multiple individuals, and (3) exploring the importance of each individual and identifying the most important individuals. The movement data obtained from a real football match are used as a case study to validate the effectiveness of the proposed approach. The results demonstrate that the proposed approach is useful in exploring dynamic interactions in football movement data and discovering insightful information.
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