Suchergebnisse

AbstractCorporate environmental responsibility, in response to the call for sustainable development, has become a critical ethical capital for companies to increase firm values and to obtain resources. However, this also gives rise to the concern of "greenwashing" that companies may selectively release environmental performance information to mislead the public and investors. Using the administrative environmental penalty data of all listed companies in China from 2017 to 2018, this research examines the corporate's greenwashing strategies. Our results show that greenwashing by listed companies in China is widespread, that only 13.6% environmental penalties have been disclosed by companies during our studied period. Companies choose to greenwash environmental performance mainly due to future demand for investment and financing, and companies with higher debt levels are found more likely to engage in greenwashing. Our findings suggest that it is not a rare practice for Chinese companies to risk their business ethics to pursue economic interests. It is necessary to enforce environmental information disclosure for listed companies in China and to increase their costs of unethical behaviors.

Abstract Background Tuberculosis (TB) often causes catastrophic economic effects on both the individual suffering the disease and their households. A number of studies have analyzed patient and household expenditure on TB care, but there does not appear to be any that have assessed the incidence, intensity and determinants of catastrophic health expenditure (CHE) relating to TB care in China. That will be the objective of this paper. Methods The data used for this study were derived from the baseline survey of the China Government – Gates Foundation TB Phase II program. Our analysis included 747 TB cases. Catastrophic health expenditure for TB care was estimated using two approaches, with households defined as experiencing CHE if their annual expenditure on TB care: (a) exceeded 10 % of total household income; and (b) exceeded 40 % of their non-food expenditure (capacity to pay). Chi-square tests were used to identify associated factors and logistic regression analysis to identify the determinants of CHE. Results The incidence of CHE was 66.8 % using the household income measure and 54.7 % using non-food expenditure (capacity to pay). An inverse association was observed between CHE rates and household income level. Significant determinants of CHE were: age, household size, employment status, health insurance status, patient income as a percentage of total household income, hospitalization and status as a minimum living security household. Factors including gender, marital status and type of TB case had no significant associations with CHE. Conclusions Catastrophic health expenditure incidence from TB care is high in China. An integrated policy expanding the free treatment package and ensuring universal coverage, especially the height of UHC for TB patients, is needed. Financial and social protection interventions are essential for identified at-risk groups.

Background: Tuberculosis (TB) often causes catastrophic economic effects on both the individual suffering the disease and their households. A number of studies have analyzed patient and household expenditure on TB care, but there does not appear to be any that have assessed the incidence, intensity and determinants of catastrophic health expenditure (CHE) relating to TB care in China. That will be the objective of this paper. Methods: The data used for this study were derived from the baseline survey of the China Government – Gates Foundation TB Phase II program. Our analysis included 747 TB cases. Catastrophic health expenditure for TB care was estimated using two approaches, with households defined as experiencing CHE if their annual expenditure on TB care: (a) exceeded 10 % of total household income; and (b) exceeded 40 % of their non-food expenditure (capacity to pay). Chi-square tests were used to identify associated factors and logistic regression analysis to identify the determinants of CHE. Results: The incidence of CHE was 66.8 % using the household income measure and 54.7 % using non-food expenditure (capacity to pay). An inverse association was observed between CHE rates and household income level. Significant determinants of CHE were: age, household size, employment status, health insurance status, patient income as a percentage of total household income, hospitalization and status as a minimum living security household. Factors including gender, marital status and type of TB case had no significant associations with CHE. Conclusions: Catastrophic health expenditure incidence from TB care is high in China. An integrated policy expanding the free treatment package and ensuring universal coverage, especially the height of UHC for TB patients, is needed. Financial and social protection interventions are essential for identified at-risk groups.

In response to the COVID-19 pandemic, governments have implemented a wide range of non-pharmaceutical interventions (NPIs). Monitoring and documenting government strategies during the COVID-19 crisis is crucial to understand the progression of the epidemic. Following a content analysis strategy of existing public information sources, we developed a specific hierarchical coding scheme for NPIs. We generated a comprehensive structured dataset of government interventions and their respective timelines of implementation. To improve transparency and motivate collaborative validation process, information sources are shared via an open library. We also provide codes that enable users to visualise the dataset. Standardization and structure of the dataset facilitate inter-country comparison and the assessment of the impacts of different NPI categories on the epidemic parameters, population health indicators, the economy, and human rights, among others. This dataset provides an in-depth insight of the government strategies and can be a valuable tool for developing relevant preparedness plans for pandemic. We intend to further develop and update this dataset until the end of December 2020.

International audience ; In response to the COVID-19 pandemic, governments have implemented a wide range of non-pharmaceutical interventions (NPIs). Monitoring and documenting government strategies during the COVID-19 crisis is crucial to understand the progression of the epidemic. Following a content analysis strategy of existing public information sources, we developed a specific hierarchical coding scheme for NPIs. We generated a comprehensive structured dataset of government interventions and their respective timelines of implementation. To improve transparency and motivate collaborative validation process, information sources are shared via an open library. We also provide codes that enable users to visualise the dataset. Standardization and structure of the dataset facilitate inter-country comparison and the assessment of the impacts of different NPI categories on the epidemic parameters, population health indicators, the economy, and human rights, among others. This dataset provides an in-depth insight of the government strategies and can be a valuable tool for developing relevant preparedness plans for pandemic. We intend to further develop and update this dataset until the end of December 2020.

International audience ; In response to the COVID-19 pandemic, governments have implemented a wide range of non-pharmaceutical interventions (NPIs). Monitoring and documenting government strategies during the COVID-19 crisis is crucial to understand the progression of the epidemic. Following a content analysis strategy of existing public information sources, we developed a specific hierarchical coding scheme for NPIs. We generated a comprehensive structured dataset of government interventions and their respective timelines of implementation. To improve transparency and motivate collaborative validation process, information sources are shared via an open library. We also provide codes that enable users to visualise the dataset. Standardization and structure of the dataset facilitate inter-country comparison and the assessment of the impacts of different NPI categories on the epidemic parameters, population health indicators, the economy, and human rights, among others. This dataset provides an in-depth insight of the government strategies and can be a valuable tool for developing relevant preparedness plans for pandemic. We intend to further develop and update this dataset until the end of December 2020.

International audience ; In response to the COVID-19 pandemic, governments have implemented a wide range of non-pharmaceutical interventions (NPIs). Monitoring and documenting government strategies during the COVID-19 crisis is crucial to understand the progression of the epidemic. Following a content analysis strategy of existing public information sources, we developed a specific hierarchical coding scheme for NPIs. We generated a comprehensive structured dataset of government interventions and their respective timelines of implementation. To improve transparency and motivate collaborative validation process, information sources are shared via an open library. We also provide codes that enable users to visualise the dataset. Standardization and structure of the dataset facilitate inter-country comparison and the assessment of the impacts of different NPI categories on the epidemic parameters, population health indicators, the economy, and human rights, among others. This dataset provides an in-depth insight of the government strategies and can be a valuable tool for developing relevant preparedness plans for pandemic. We intend to further develop and update this dataset until the end of December 2020.

In response to the COVID-19 pandemic, governments have implemented a wide range of non-pharmaceutical interventions (NPIs). Monitoring and documenting government strategies during the COVID-19 crisis is crucial to understand the progression of the epidemic. Following a content analysis strategy of existing public information sources, we developed a specific hierarchical coding scheme for NPIs. We generated a comprehensive structured dataset of government interventions and their respective timelines of implementation. To improve transparency and motivate collaborative validation process, information sources are shared via an open library. We also provide codes that enable users to visualise the dataset. Standardization and structure of the dataset facilitate inter-country comparison and the assessment of the impacts of different NPI categories on the epidemic parameters, population health indicators, the economy, and human rights, among others. This dataset provides an in-depth insight of the government strategies and can be a valuable tool for developing relevant preparedness plans for pandemic. We intend to further develop and update this dataset until the end of December 2020.