Evidence map›Paper›PMID 37002227›Full record

ArticleScientific data2023

A dataset on corporate sustainability disclosure.

Jinfang Tian, Qian Cheng, Rui Xue, Yilong Han, Yuli Shan

Abstract readDataset
In one paragraph

Article in Scientific data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Jinfang Tian *Research Center for Statistics and Interdisciplinary Sciences | School of Statistics and Mathematics, Shandong University of Finance and Economics, Jinan, 250014, China.
Qian Cheng *Research Center for Statistics and Interdisciplinary Sciences | School of Statistics and Mathematics, Shandong University of Finance and Economics, Jinan, 250014, China.
Rui Xue *Centre for Corporate Sustainability and Environmental Finance, Department of Applied Finance, Macquarie University, Sydney, NSW, 2109, Australia. rui.xue@mq.edu.au.
Yilong HanSchool of Economics and Management, Tongji University, Shanghai, 200092, China.ORCID 0000-0002-8818-9674
Yuli ShanSchool of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, B15 2TT, UK. y.shan@bham.ac.uk.ORCID 0000-0002-5215-8657

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Enterprises, as key emitters, play a vital role in promoting sustainable development. Corporate sustainability disclosure provides a key channel for stakeholders to gain insights into a company's sustainability progress. However, few studies have been conducted to measure sustainability disclosure at the firm level. In this study, we apply the machine learning techniques to listed companies' management discussion and analysis (MD&A) documents and construct a dataset on corporate sustainability disclosure, including the Corporate Sustainability Disclosure Index (CSDI), CSDI_Economic Dimension (CSDI_ECO), CSDI_Environmental Dimension (CSDI_ENV), and CSDI_Social Dimension (CSDI_SOCI). The dataset will be updated annually. To the best of our knowledge, this is the first sustainability disclosure dataset constructed at the firm level. Our dataset reflects corporate managements' sustainability attitudes and promotes the implementation of corporate sustainability strategies and subsequent sustainable economic and social outcomes.

Identifiers

PMID37002227
PMCPMC10064614

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.