The outgoing audit of natural resources assets and enterprise productivity: New evidence from difference-in-differences-in-differences in China.

Despite increasing evidence documenting the role of the outgoing audit of natural resources (OANRA) in environmental governance and enterprise innovation, little is known about its impact on enterprises' total factor productivity (TFP). To address this question, we treat the OANRA policy launched in 2014 in China as a quasi-natural experiment. We adopt a difference-in-differences-in-differences (DDD) method that exploits three-dimensional variations: city (i.e., OANRA cities versus non-OANRA cities), industry (i.e., more polluting industries versus less polluting ones), and year (i.e., before and after the OANRA policy). Employing a dataset of Chinese industrial listed companies from 2012 to 2019, we reveal that after the OANRA implementation, enterprises' TFP in more polluting industries of OANRA cities decreases by 4.0%. Our mechanism analysis shows that the OANRA restrains the TFP by reducing the financing scale of enterprises and increasing environmental investment of governments. Further, the heterogeneity analysis finds the inhibitory effect of the OANRA is more prominent in large-scale and state-owned enterprises, as well as enterprises located in eastern, low fiscal pressure, and high pollutant emission cities. Our findings provide support for the neoclassical economics hypothesis that the OANRA increases enterprises' compliance costs and decreases their productivity.

Data mining from process monitoring of typical polluting enterprise.

With the increasing volume of environmental monitoring data, extracting valuable insights from multivariate time series sensor data can facilitate comprehensive information utilization and support informed decision-making in environmental management. However, there is a dearth of comprehensive research on multivariate data analysis for process monitoring in typical polluting enterprises. In this study, an artificial neural network model based on back-propagation algorithm (BP-ANN) was developed to predict the wastewater and exhaust gas emissions using IoT data obtained from process monitoring of a typical polluting enterprise located in Taizhou, Zhejiang Province, China. The results indicate that the model constructed has a high predictive coefficient of determination (R2) with values of 0.8510, 0.9565, 0.9561, 0.9677, and 0.9061 for chemical oxygen demand (COD), potential of hydrogen (pH), electrical conductivity (EC), flue gas emission (FGE), and non-methane hydrocarbon concentration (NMHC) respectively. For the first time, the variable importance measure (VIM)-assisted BP-ANN was employed to investigate the internal and external correlations between wastewater and exhaust gas treatment, thereby enhancing the interpretability of mapping features in the BP-ANN model. The predicted errors for pH and FGE have been demonstrated to fall within the range of - 0.62 ~ 0.30 and - 0.21 ~ 0.15 m3/s, respectively, with average relative errors of 1.05% and 9.60%, which is advantageous in detecting anomalous data and forecasting pollution indicator values. Our approach successfully addresses the challenge of segregating data analysis for wastewater disposal and exhaust gas disposal in the process monitoring of polluting enterprises, while also unearthing potential variables that significantly contribute to the BP-ANN model, thereby facilitating the selection and extraction of characteristic variables.

Green credit policy and the liquidity risks of heavily polluting enterprises.

As a kind of enterprises most affected by green policies-heavily polluting enterprises, whether the government's relevant policies can achieve its policy goals and what impact will be exerted on such enterprises is a critical issue. Based on the data of listed heavily polluting enterprises in China from 2007 to 2020, this paper uses the difference in differences model to test the impact of green credit policies on the liquidity risk of heavily polluting enterprises. The results show that the green credit policies intensify the liquidity risk of heavily polluting enterprises. Moreover, the green credit policies increase the financial and stock liquidity risks of heavily polluting enterprises by reducing the long-term debt ratio and information transparency. Green innovation and equity balance weaken the positive impact of green credit policies on the liquidity risk of heavily polluting enterprises. Heavily polluting enterprises can reduce the impact of green credit policies on their liquidity risk by increasing investment in green innovation and improving ownership structure.

Green Finance and Technological Innovation in Heavily Polluting Enterprises: Evidence from China.

There is an urgent need for countries worldwide to promote the green transformation of their economies and reduce environmental pollution. Based on China's Green Credit Guidelines policy in 2012 and the data of Chinese listed companies from 2007 to 2021, we conducted an empirical test using the difference-in-differences method. The results showed that green finance policies inhibit technological innovation in heavily polluting enterprises, and the stronger the enterprise's operating capacity, the weaker this inhibiting effect. The study also shows that bank loan, loan term, corporate management motivation, and business confidence have intermediation effects. Therefore, countries should improve green financial policies and promote technological innovation in heavily polluting enterprises in order to reduce environmental pollution and promote green growth.

Green mergers and acquisitions and green innovation: an empirical study on heavily polluting enterprises.

The concept of green development has gradually penetrated into the enterprise. Green mergers and acquisitions (M&A) have gradually become a means for heavily polluting enterprises to achieve the goal of energy conservation and emission reduction and embark on the path of green transformation. Heavily polluting enterprises have acquired clean technology and resources through green M&A, and whether they will promote their green innovations after green M&A has not yet been explored. Based on the data of M&As of China's heavy polluting enterprises from 2010 to 2018, this study empirically tests whether the M&As of heavy polluting enterprises can promote green innovation. The results show that M&As by heavily polluting enterprises can promote green innovation, and this impact is promoted with the support of government subsidies. In addition, older or higher paid CEOs negatively moderate this effect. Therefore, our study believes that most of the M&As of heavy polluting enterprises are taking the initiative to take environmental protection responsibilities and embarking on the path of green transformation. The government can issue relevant policies to encourage heavily polluting enterprises to conduct green M&A in order to achieve their goal of green transformation. Our study has enriched the relevant literature on green investment and green innovation, and can be used as a reference for the government to introduce policies for the green transformation of heavily polluting enterprises.

Gestión ambiental como alternativa para la participación comunitaria en el Consejo Popular Altamira / Environmental management as an alternative for the community participation in Altamira People's Council

Se realizó un estudio observacional y descriptivo, basado en 113 habitantes y 44 trabajadores de las empresas más contaminantes del Consejo Popular Altamira de Santiago de Cuba, durante el 2008, para caracterizar los riesgos ambientales más importantes en el área desde los puntos de vista industrial y comunitario e identificar su nivel de conocimientos sobre la percepción al respecto. Se evidenció que estos habitantes desconocían sobre salud ambiental, así como tenían baja percepción de riesgo e insatisfacciones por servicios relacionados con la seguridad ambiental; asimismo, se confirmó que el bajo nivel de conocimientos que poseían los trabajadores de las empresas sobre el daño ambiental provocado, los factores de riesgo a que se exponían y la baja cobertura de medios protectores, las convertían en ambientes inseguros. El desconocimiento de la población, los trabajadores, directivos y cuadros de las empresas sobre el tema obligó a diseñar estrategias de capacitación e intervención comunitaria en función del mejoramiento del medio y la calidad de vida de los pobladores.

A descriptive, observational study was carried out, based on 113 people and 44 workers from the most polluting enterprises in Altamira People's Council of Santiago de Cuba during 2008, to characterize the major environmental risks in this area from the industrial and community points of view and to identify their knowledge of the perception in this respect. It was evidenced that these people were unaware of environmental health and had low risk perception and dissatisfactions with services related to environmental security. It was also confirmed that the low level of knowledge of environmental damage held by the workers, exposure to risk factors and inadequate protection means converted these enterprises into unsafe environments. Ignorance of population, enterprise workers, managers and cadres on the subject forced to devise strategies of community intervention and training in terms of improving the environment and quality of people's life.