The role of European businesses in addressing environmental issues and climate change has taken center stage with the European Green Deal. With increasing attention to the effect of board gender diversity (BGD) on firms' environmental performance, the question arises whether BGD has any influence on carbon emissions. Based on legitimacy and critical mass theory, this study empirically investigates the impact of BGD on firms' carbon performance (CP), based on total carbon emissions intensity. The paper relies on two‐stage least squares (2SLS) regressions with instrumental variable (IV) and a two‐step generalized method of moments (GMM) system approach to analyze a cross‐country sample of 3123 observations from non‐financial firms in the European STOXX600 index over the 2009–2018 period. Our findings add to the growing empirical evidence twofold: (1) there is a robust linear and positive relationship between BGD and CP, whereas some indication of a U‐shaped relationship is found; and (2) we find that a critical mass of at least two women directors needs to be reached to increase CP. Our research results contribute to the current discussion on sustainable corporate governance, especially in the European capital market, and have implications for researchers, business practice, and regulatory issues alike.

Board gender diversity and carbon emissions: European evidence on curvilinear relationships and critical mass

Attitude of Chinese public towards municipal solid waste sorting policy: A text mining study.

With the tremendous growth of scientific literature in recent years, methods of detecting and analysing research topics have become more and more important This study proposes a topic analysis met

A topic analysis method based on a three-dimensional strategic diagram:

Financial data science and econometrics are highly complementary. They share an equivalent research process with the former’s intellectual point of departure being statistical inference and the latter’s being the data sets themselves. Two challenges arise, however, from digitalisation. First, the ever-increasing computational power allows researchers to experiment with an extremely large number of generated test subjects (i.e. p-hacking). We argue that p-hacking can be mitigated through adjustments for multiple hypothesis testing where appropriate. However, it can only truly be addressed via a strong focus on integrity (e.g. pre-registration, actual out-of-sample periods). Second, the extremely large number of observations available in big data set provides magnitudes of statistical power at which common statistical significance levels are barely relevant. This challenge can be addressed twofold. First, researchers can use more stringent statistical significance levels such as 0.1% and 0.5% instead of 1% and 5%, respectively. Second, and more importantly, researchers can use criteria such as economic significance, economic relevance and statistical relevance to assess the robustness of statistically significant coefficients. Especially statistical relevance seems crucial, as it appears far from impossible for an individual coefficient to be considered statistically significant when its actual statistical relevance (i.e. incremental explanatory power) is extremely small.

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Financial data science: the birth of a new financial research paradigm complementing econometrics?

https://bura.brunel.ac.uk/bitstream/2438/21677/1/FullText.pdf

Impact of directors' networks on corporate social responsibility: A cross country study

This paper performs topic modeling using all publicly available CSR (Corporate Social Responsibility) reports for all constituent firms of the major stock market indices of 15 industrialized countr...

/pdf/corporate-social-responsibility-reports-topic-analysis-and-3enq4q5auy.pdf

Irina Goloshchapova

Papers

Corporate social responsibility reports: topic analysis and big data approach