This study gives a depiction of what are the general directions taken by international institutions so to tackle the current health emergency and the most pressing environmental issues, such as climate change and COVID-19 (Schaltegger, 2020; Adebayo et al., 2021).The role of companies is crucial under disruptive events, such as a crisis or, more in line with the present time, a pandemic, and the pursue of the shareholder value cannot be the essence and the only objective in doing business anymore, since also ESG (i.e., environmental, social, and governance) dynamics have to be taken in due consideration. Moreover, an adequate and effective corporate governance should lead to higher disclosure quality, which subsequently should help protect the entire planet and ecosystems as well. In this context, the principal role of accounting and corporate reporting activities should be oriented towards making emerge what is and what is not done by companies in their business operations, and the disclosure of financial information is currently deemed inappropriate for pursuing a sustainable growth in the medium and long run (Schaltegger, J Account Org Change 16:613–619, 2020; Kirikkaleli & Adebayo, Sustain Dev 29:583–594, 2020; Tettamanzi, Venturini & Murgolo Wider corporate reporting: La possibile evoluzione della Relazione sulla Gestione Bilancio e Revisione, IPSOA - Wolters Kluwer, Philadelphia, 2021). Thus, the objective of this study is to investigate what international and European institutions have planned to do in order to align corporate objectives with environmental and societal needs in the coming years (Biondi et al., Meditari Account Res 28:889–914, 2020; Songini L et al. Integrated reporting quality and BoD characteristics: an empirical analysis. J Manag Govern, 2021).As of today, our analysis finds that IFRS Foundation (at global level) and EFRAG (at European one) have been taking steps toward the aforementioned issues so to propose disclosure standards more in line with sustainability and environmental needed improvements. In fact, we tried to give a depiction of what are the actual and future strategies that both these institutions are going to put in place: this snapshot will give scientists, engineers, lawyers, and business people an overview of what should be like the corporate world of the near future, from a corporate reporting/accounting perspective (so to better understand what will be expected from companies of all the industries worldwide).

Municipal solid waste (MSW) management currently requires critical attention in ensuring the best principles of socio-economic attributes such as environmental protection, economic sustainability, and mitigation of human health problems. Numerous surveys on the waste management system reveal that approximately 90% of the MSW systems are improperly disposing the wastages in open dumps and landfills. Classifying the wastages into biodegradable and non-biodegradable helps converting them into usable energy and disposing properly. The advancements of effective computational approaches like artificial intelligence and image processing provide wide range of solutions for the present problem identified in MSW management. The computational approaches can be programmed to classify wastes that help to convert them into usable energy. Existing methods of waste classification in MSW remain unresolved due to poor accuracy and higher error rate. This paper presents an experimented effective computer vision–based MSW management solution with the help of the Internet of Things (IoT), and machine learning (ML) techniques namely regression, classification, clustering, and correlation rules for the perception of solid waste images. A ground-up built convolutional neural network (CNN) and CNN by the inception of ResNet V2 models trained through transfer learning for image classification. ResNet V2 supports training large datasets in deep neural networks to achieve improved accuracy and reduced error rate in identity mapping. In addition, batch normalization and mixed hybrid pooling techniques are incorporated in CNN to improve stability and yield state of art performance. The proposed model identifies the type of waste and classifies them as biodegradable or non-biodegradable to collect in respective waste bins precisely. Furthermore, observation of performance metrics, accuracy, and loss ensures the effective functions of the proposed model compared to other existing models. The proposed ResNet-based CNN performs waste classification with 19.08% higher accuracy and 34.97% lower loss than the performance metrics of other existing models.

One of the strategies for agricultural development is the optimal use of irrigation and drainage networks, which leads to higher productivity and economic benefits. In this regard, quantitative and qualitative studies of drainage water from networks are essential for efficient water management. In the present study, we develop a model using a system dynamics approach to simulate the cropping pattern of an irrigation and drainage network as well as the discharge and salinity of drainage water from network farms. We apply the Powell algorithm to optimize the economic profitability of cultivated crops by considering the salinity and discharge of drainage water from the fields. With three aims, i.e., (1) maximizing benefit–cost ratio, (2) minimizing drainage water salinity and discharge of network, and (3) economic and environmental considerations simultaneously, the optimization of cropping pattern within the Kosar irrigation and drainage network is performed. Results based on five consecutive years under different scenarios showed that some crops, such as watermelon, are not economically recommened for production due to high costs, water consumption, and low selling price causes environmental pollution. On the other hand, wheat, grain maize, silage maize, sorghum, and alfalfa have different conditions, and their production is suitable by considering all scenarios. By comparing with experimental data, we find that the proposed model is accurate to simulate and optimize the irrigation network and to detect its cropping pattern.

Both arsenic (As) and antimony (Sb) are primary environmental contaminants that often co-exist at contaminated sites. Though the microbial community compositions of As- and Sb-contaminated sites have been previously described, the changes in microbial community interactions and community functions remain elusive. In the current study, several key metabolic processes, such as As/Sb detoxification and carbon fixation, were enriched under heavily contaminated conditions. Furthermore, the identified keystone taxa, which are associated with the families Nitrosomonadaceae, Pedosphaeraceae, Halieaceae, and Latescibacterota, demonstrated positive correlations with As and Sb concentrations, indicating that they may be resistant to As and Sb toxicities. Accordingly, arsenic resistance–related functions, along with several functions such as carbon fixation, were found to be enriched in heavily contaminated sites. The current study elucidated the key microbial populations in As- and Sb-contaminated rice terraces and may provide useful information for remediation purposes.

The unprecedented increase in anthropogenic activities, coupled with the prevailing climatic conditions, has increased the aerosol load over East Africa (EA). Given this, the present study examined the trends in total, absorption, scattering, and total aerosol extinction optical depth (TAOD, AAOD, SAOD, and TAEOD) over EA, alongside trends in single scattering albedo (SSA). For this purpose, the AOD of different optical properties retrieved from multiple sensors and the Modern-Era Retrospective Analysis for Research and Applications (MERRA-2) model between January 2001 to December 2019 were utilized to estimate trends and assess their statistical significance. The spatial patterns of seasonal mean AOD from the Moderate-resolution Imaging Spectroradiometer (MODIS) sensor and MERRA-2 model were generally characterized with high (>0.35) and low (<0.2) AOD centers over EA observed during the local dry and wet seasons, respectively. Also, the spatial trend analysis revealed a general increase in TAOD, being positive and significant over the arid and semi-arid zones of the northeastern part of EA, which is majorly dominated by locally derived dust. The local dry (wet) months generally experienced positive (negative) trends in TAOD, associated with seasonal cycles of rainfall. High and significant positive trends in AAOD were dominated over the study domain, attributed to an increased amount of biomass burning, variations in soil moisture, and changes in the rainfall pattern. The trends in TAEOD showed a distinct pattern, except over some months that depicted significant increasing trends attributed to changes in climatic conditions and anthropogenic activities. At last, the study domain exhibited decreasing trends in SSA, signifying strong absorption of direct solar radiation resulting in a warming effect. The study revealed patterns of trends in aerosol optical properties and forms the basis for further research in aerosols over EA.

This study investigated atmospheric changes that occurred due to changes in energy production and consumption before and during the COVID-19 pandemic. We analyzed nitrogen dioxide (NO₂), aerosol optical depth (AOD), and rainfall patterns to understand the associated changes in emissions, especially from the power generation sector, before (2018 and 2019) and during the lockdown of 2020 across Pakistan. Regression analysis indicated a strong association between energy production by thermal power plants and tropospheric NO₂ concentrations. Notably, a comparison between emission sources showed that the NO₂ emissions from a single thermal power plant were equivalent to the emissions from a major city. During the lockdown, we observed a 40% reduction in NO₂ emissions from coal-based power plants and a 30% reduction in mega- and major cities compared to the same retro in 2019. We also observed an approximate 25% decrease in AOD in the industrial and energy sectors, although no major decrease was obvious in the cities. Rainfall contributed to reducing the NO₂ concentrations during monsoon season across all power plants in Pakistan, whereas it did not significantly correlate with AOD. The findings highlight the need for appropriate management and use of renewable energy in the industrial sector and transportation systems. Future research could estimate the environmental and public health costs linked to pollution originating from thermal energy production and poor transportation infrastructure.

At present, there are many studies on microbial reduction of Pd (II), although few of these studies assess the bacterial toxicity of Pd (II) before and after reduction by microorganisms. In this study, the toxicity of Pd (II) to Bacillus wiedmannii MSM (B. wiedmannii MSM) was assessed before and after reduction by live B. wiedmannii MSM cells (referred to as “Pd (0)-loaded cells”) and after biosorption by dead B. wiedmannii MSM cells (referred to as “Pd (II)-loaded cells”). B. wiedmannii MSM is a widely occurring, nonpathogenic aerobic bacterium. Compared with Pd (II), the EC₂₀, EC₅₀, and EC₈₀ of Pd (0)-loaded cells increased by 77.73-, 112.75-, and 163.91-fold, respectively, while the EC₂₀, EC₅₀, and EC₈₀ of Pd (II)-loaded cells increased by only 11.55-, 9.77-, and 8.29-fold, respectively. The sterilization contribution rates of the mechanisms of action of Pd (0)-loaded cells were ranked in the order of: remaining Pd (II) > oxidative stress > physical puncture. Pd (II) was found to increase cell membrane permeability, glutathione S-transferase (GST) enzyme activity, and reactive oxygen species levels in cells and decrease the cell membrane potential. XPS results indicated that Pd (II) increased the content of polysaccharides and peptides and decreased the content of hydrocarbons in cells. These findings reveal the bactericidal mechanism of toxicity of Pd (II) and Pd (0)-loaded cells on Bacillus wiedmannii MSM and provide an environmentally friendly and inexpensive method for Pd (II) detoxification.

Surfacing the stress of global CO₂ emission reduction and the change into a low-emission economy has become one of the prominent economic concerns in the twenty-first century. The essence of evolving a low-emission economy is to raise carbon productivity that can be estimated as the cost-effective paybacks of CO₂ emissions. A panel threshold model was applied to approximate the threshold effect of globalization on carbon productivity under the development of human capital by using the panel data of thirty provinces of China from 2009 to 2017. The empirical findings demonstrate that China’s carbon productivity increases, while economic growth shape moves towards sustainable development with low-carbon emission. Moreover, the driving force of globalization on carbon productivity is not tediously decreasing/increasing, but it has a double threshold effect of human capital. In line with this, this study finding found a single and double threshold of 9.3478 and 10.8800, respectively, as a benchmark where the relationship turns positive. The empirical findings have suggested several policy implications for the Chinese Government, policymakers, and regulatory authorities regarding this critical issue.

Carwash wastewater treatment with potassium ferrate (VI) (K₂FeO₄) was optimized by response surface methodology. The optimum conditions for chemical oxygen demand removal were established a pH 3.5, 0.328 g/L dose of K₂FeO₄, and with a process duration of 48 min. At these conditions, chemical oxygen demand, total organic carbon, total nitrogen, and total phosphorus decreased by 70.3, 58.9, 73.3, 82.0%, respectively; and the putrid odor was reduced. Simultaneously, the total viable count, total coli count, most probable number of fecal enterococci, and the total proteolytic bacteria count decreased by 89.5, 93.1, 92.9, and 95.0 %, respectively. Comparatively, an application of 0.450 g/L FeCl₃·6H₂O corresponding to the iron content in 0.328 g/L of K₂FeO₄ resulted in a decrease of total viable count, total coli count, most probable number of fecal enterococci and the total proteolytic bacteria count only by 38.1, 31.2, 42.9, and 58.0%, respectively. Therefore, flocculation with polyacrylamide anionic flocculant combined with potassium ferrate (VI) oxidation is a more effective alternative to coagulation with FeCl₃ and the same flocculant. The use of potassium ferrate (VI) is a viable option for the treatment of carwash wastewater.