Agriculture, meteorological, and hydrological drought is a natural hazard which affects ecosystems in the central India of Maharashtra state. Due to limited historical data for drought monitoring and forecasting available in the central India of Maharashtra state, implementing machine learning (ML) algorithms could allow for the prediction of future drought events. In this paper, we have focused on the prediction accuracy of meteorological drought in the semi-arid region based on the standardized precipitation index (SPI) using the random forest (RF), random tree (RT), and Gaussian process regression (GPR-PUK kernel) models. A different combination of machine learning models and variables has been performed for the forecasting of metrological drought based on the SPI-6 and 12 months. Models were developed using monthly rainfall data for the period of 2000-2019 at two meteorological stations, namely, Karanjali and Gangawdi, each representing a geographical region of Upper Godavari river basin area in the central India of Maharashtra state which frequently experiences droughts. Historical data from the SPI from 2000 to 2013 was processed to train the model into machine learning model, and the rest of the 2014 to 2019-year data were used for testing to forecast the SPI and metrological drought. The mean square error (MSE), root mean square error (RMSE), adjusted R2, Mallows' (Cp), Akaike's (AIC), Schwarz's (SBC), and Amemiya's PC were used to identify the best combination input model and best subregression analysis for both stations of SPI-6 and 12. The correlation coefficient ([Formula: see text]), mean absolute error (MAE), root mean square error (RMSE), relative absolute error (RAE), and root relative squared error (RRSE) were used to perform evaluation for SPI-6 and 12 months of both stations with RF, RT, and GPR-PUK kernel models during the training and testing scenarios. The results during testing phase revealed that the RF was found as the best model in forecasting droughts with values of [Formula: see text], MAE, RMSE, RAE (%), and RRSE (%) being 0.856, 0.551, 0.718, 74.778, and 54.019, respectively, for SPI-6 while 0.961, 0.361, 0.538, 34.926, and 28.262, respectively, for SPI-12 scales at Gangawdi station. Further, the respective values of evaluators at Karanjali station were 0.913 and 0.966, 0.541 and 0.386, 0.604 and 0.589, 52.592 and 36.959, and 42.315 and 31.394 for PUK kernel and RT models, respectively, during SPI-6 and SPI-12. Machine learning models are potential drought warning techniques because they take less time, have fewer inputs, and are less sophisticated than dynamic or scientific models.
The interrelationships between air quality, land cover change, and road networks in the Lagos megacity have not been explored. Globally, there are knowledge gaps in understanding these dynamics, especially using remote sensing data. This study used multi-temporal and multi-spectral Landsat imageries at four epochs (2002, 2013, 2015, and 2020) to evaluate the aerosol optical thickness (AOT) levels in relation to land cover and road networks in the Lagos megacity. A look-up table (LUT) was generated using Py6S, a python-based 6S module, to simulate the AOT using land surface reflectance and top of atmosphere reflectance. A comparative assessment of the method against in situ measurements of particulate matter (PM) at different locations shows a strong positive correlation between the imagery-derived AOT values and the PMs. The AOT concentration across the land cover and road networks showed an increasing trend from 2002 to 2020, which could be explained by urbanization in the megacity. The higher concentration of AOT along the major roads is attributed to the high air pollutants released from vehicles, including home/office generators and industries along the road corridors. The continuous rise in pollutant values requires urgent intervention and mitigation efforts. Remote sensing-based AOT monitoring is a possible solution.
The current research presents fresh insights on empirically presenting the relationship between ownership structure and corporate sustainable performance of two emerging markets: Malaysia and Pakistan. Moreover, the moderating role of gender diversity is observed on the relationship between ownership structure and corporate sustainable performance. Dynamic estimator, named system generalized method of moments, is used for estimations that control for potential dynamic endogeneity, simultaneity, and reverse causality. Findings reveal that ownership concentration and state ownership are negatively related to economic, social, and environmental indicators of CSP both in Malaysia and Pakistan, whereas directors' ownership is positively associated with all sustainability indicators. Financial institution's ownership showed a positive significant impact on CSP in Malaysia, whereas an insignificant relationship is observed in Pakistan. Meanwhile, the moderating impact of women directors on the relationship between ownership structure and corporate sustainable performance reveals a significant impact in Malaysia and an insignificant impact in Pakistan. Generally, the findings of the study have practical implications for regulatory authorities, securities commissions, and policymakers of Malaysia and Pakistan. Moreover, there is a need to bring reforms into corporate governance structures in Pakistan, where weak economic conditions leave a frail impact of ownership structure on CSP and an insignificant moderating impact of board gender diversity.
OECD countries have encountered the challenges of improving the environmental sustainability while maintaining economic growth by not impairing employment. This study attempts to reexamine the environmental Kuznets curve (EKC) hypothesis by using ecological footprint as an indicator of environmental degradation. Besides, our study aims to test the validity of environmental Phillips curve (EPC) and role of clean energy on ecological footprint. Our data cover a panel of 36 OECD countries from 1995 to 2015. We adopt the second-generation panel unit root and cointegration test to account for the presence of cross-section dependence (CSD). Moreover, the long-run relationship is estimated using Common Correlated Effect Mean Group (CCEMG) and Augmented Mean Group (AMG) that are robust to CSD. Our findings reveal that the EKC hypothesis is not valid while EPC is confirmed in OECD countries. Though there is a trade-off between unemployment and environmental degradation in OECD countries, the development of new technologies, especially in the clean energy sector, could be a key factor contributing to sustainable growth and better environmental quality. Thus, it is recommended that OECD countries should focus on the development of innovative green technologies and strengthen the initiatives that promote renewable energy consumption.
Prior studies on environmental standards have highlighted the significance of urbanization and transportation in affecting environmental sustainability worldwide. As the empirical and theoretical debates are still unresolved and divisive, the argument of whether urbanization, transportation and economic growth in Association of Southeast Asian Nations (ASEAN) countries cause greenhouse gas (GHG) emissions remains unclear. This study aim is to examine dynamic linkage between transportation, urbanization, economic growth and GHG emissions, as well as the impact of environmental regulations on GHG emission reduction in ASEAN countries over the years 1995-2018. On methodological aspects, the study accompanies a few environmental studies that check the cross-sectional dependence and slope heterogeneity issues. Moreover, the new cross-sectionally augmented autoregressive distributed lags (CS-ARDL) methodology is also applied in the study to estimate the short-run and long-run effects of the factors on GHG emissions. Substantial evidence is provided that GHG emissions increase with transportation, urbanization and economic growth but decrease with the imposition of environmental-related taxations. Augmented mean group (AMG) and common correlated effect mean group (CCEMG) also support the findings of CS-ARDL estimates. Finally, the study calls for drastic actions in ASEAN countries to reduce GHG emissions, including environmentally friendly transportation services and environmental regulation taxes. This study also provides the guidelines to the regulators while developing policies related to control the GHG emission in the country.
The current study attempted to inspect the influence of green human resource management (GHRM) practices and green innovation (GI) on environmental performance. Besides, the study considered green corporate social responsibility (GCSR) as a mediator to elaborate on the influence of GHRM and green innovation on environmental performance. Additionally, the current study assessed the role of green transformational leadership (GTFL) by the focus on GHRM and GI on GCSR. Data were gathered from 310 employees who are working in public and private banks by using a survey questionnaire. Subsequently, the data were analysed by using the partial least square structural equation modelling technique. The study findings showed that GHRM and GI positively influenced GCSR. In addition, the results revealed an insignificant relationship between GHRM and environmental performance (EP), whereas the influence of GI on environmental performance was significant. Moreover, GCSR positively influenced environmental performance. The results supported the mediator task of GCSR between the influence of GHRM and GI on EP. Finally, the findings indicated GTFL as a significant moderator. The study was theoretically grounded on NRBV theory. The study adds to the GHRM, GTFL, GCSR, green innovation and environmental performance theory in novel ways. The study also added to the literature by providing evidence on how transformational leadership can serve as a booster to transform the influence of GHRM on GCSR.
As an important indicator of sustainable development, industrial eco-efficiency (IEE) has aroused growing attention from governments all over the world including China, in recent decades. The Chinese government has introduced numerous environmental regulations; however, the environmental pollution issue does not appear to have been solved. Moreover, although several earlier studies have shown that environmental regulations may promote innovation, there is no consensus on their ultimate effects on IEE. Therefore, this study took a critical look at the connection between environmental regulations and IEE in 36 Chinese sub-sectors from 2009 to 2018. Based on the weak Porter hypothesis (weak PH) and strong Porter hypothesis (strong PH), this paper constructed two panel regression models and conducted group analysis by pollution intensity to check the relationships among environmental regulations, technological innovation, and IEE. It was found that environmental regulations can improve technological innovation and IEE, but these impacts vary across different pollution groups. Specifically, environmental regulations have a U-shaped or inverted U-shaped relationship with technological innovation and IEE. Of the 36 sub-sectors, 26 prove the existence of the Weak PH while 10 verify the Strong PH, indicating that environmental regulations generally advocate technological innovation for most sub-sectors but only promote IEE in a few sub-sectors at present. Finally, differentiated policy implications for environmental regulations and technological innovation are provided for decision-makers.
Due to significant requirement of energy, water, material, and other resources, the manufacturing industries significantly impact environmental, economic, and social dimensions of sustainability (triple bottom-line). In response, today's research is focused on finding solution towards sustainable manufacturing. In this regard, sustainability assessment is an essential strategy. In the past, a variety of tools was developed to evaluate the environmental dimension. Because of this fact, previous review studies were grounded mostly on tools for green manufacturing. Unlike previous review articles, this study was aimed to review and analyze the emerging sustainability assessment methodologies (published from 2010 to 2020) for manufacturing while considering the triple bottom-line concept of sustainability. In this way, the paper presents a decade review on this topic, starting from 2010 as the guidelines for the social dimension became available in 2009. This paper has analyzed various methods and explored recent progress patterns. First, this study critically reviewed the methods and then analyzed their different integrating tools, sustainability dimensions, nature of indicators, difficulty levels, assessment boundaries, etc. The review showed that life cycle assessment and analytic hierarchy process-based approaches were most commonly used as integrating tools. Comparatively, still, environmental dimension was more commonly considered than economic and social dimensions by most of the reviewed methods. From indicators' viewpoint, most of the studied tools were based on limited number of indicators, having no relative weights and validation from the experts. To overcome these challenges, future research directions were outlined to make these methods more inclusive and reliable. Along with putting more focus on economic and social dimensions, there is a need to employ weighted, validated, and applicable indicators in sustainability assessment methods for manufacturing.
The Sustainable Development Goal (SDG) 10 focuses on combating the climate change and its effects. The inclusion of this agenda in the Sustainable Development Goals by the United Nations has shown that worsened environmental degradation is currently a major threat facing humankind. The World Commission on Environment and Development 2015 has highlighted that income inequality is one of the major causes for environmental deterioration. Hence, reducing environmental degradation requires a look at the problem of unequal income distribution. Moreover, educational attainment plays a vital role in providing relevant knowledge and skills to people in handling environmental problems. Thus, the objective of the study is to investigate the relationship between income inequality, educational attainment, and CO2 emissions by employing a panel data analysis for a group of 64 countries from 1990 to 2016.The study uses mainly dynamic common correlated effects (DCCE) estimator to take into account the issue of cross-section dependence which has been ignored by most of the previous studies. By tackling the problem of cross-section dependence, unbiased and reliable results could be produced in estimations. Our results portray that an inverted U-shaped environmental Kuznets curve (EKC) is found to be valid. Additionally, income inequality has a negative impact on environmental degradation. Likewise, educational attainment and CO2 emissions are revealed to be negatively correlated. The findings of the study could provide a better understanding on the root causes of environmental degradation, and further suggest remedial actions to overcome the problem.
A wastewater treatment plant (WWTP) is an essential part of the urban water cycle, which reduces concentration of pollutants in the river. For monitoring and control of WWTPs, researchers develop different models and systems. This study introduces a new deep learning model for predicting effluent quality parameters (EQPs) of a WWTP. A method that couples a convolutional neural network (CNN) with a novel version of radial basis function neural network (RBFNN) is proposed to simultaneously predict and estimate uncertainty of data. The multi-kernel RBFNN (MKRBFNN) uses two activation functions to improve the efficiency of the RBFNN model. The salp swarm algorithm is utilized to set the MKRBFNN and CNN parameters. The main advantage of the CNN-MKRBFNN-salp swarm algorithm (SSA) is to automatically extract features from data points. In this study, influent parameters (if) are used as inputs. Biological oxygen demand (BODif), chemical oxygen demand (CODif), total suspended solids (TSSif), volatile suspended solids (VSSif), and sediment (SEDef) are used to predict EQPs, including CODef, BODef, and TSSef. At the testing level, the Nash-Sutcliffe efficiencies of CNN-MKRBFNN-SSA are 0.98, 0.97, and 0.98 for predicting CODef, BODef, and TSSef. Results indicate that the CNN-MKRBFNN-SSA is a robust model for simulating complex phenomena.
The current global trend in sustainable business practices is to optimize green innovation performance. To protect the environment and maintain their own survival, organizations must strengthen their green innovation capabilities. Drawing on the recourse-based view and ecology modernization theory (EMT), this study examines the direct effect of green strategic orientations, green entrepreneurial orientation, green market orientation, green innovation orientation, and green organizational culture on the firm's green innovation capability, as well as the mediating effect of green innovation capability on the relationship of these four factors and green innovation performance. Besides, this study also explored the moderating effects of green management system implementation and firm size on the association between green innovation capability and green innovation performance. To test the hypothesized model, a questionnaire survey was administered to gather responses from 293 medium-sized and large manufacturing firms operating in Pakistan. The partial least squares method was used for data analysis. The results revealed that green entrepreneurial orientation, green market orientation, green innovation orientation, and green organizational culture positively impacted green innovation capability, which subsequently positively influenced green innovation performance. Moreover, effective implementation of green management systems can bolster the effect of green innovation capability on green innovation performance, and the mediating effect of green innovation capability has also been confirmed. These indicated that the management of medium and large manufacturing firms operating in Pakistan should focus on encouraging green innovation and training employees regarding the latest eco-friendly technologies to attain performance and sustainable development goals. Policymakers should implement green business development programs and offer rewards or penalties for promoting compliance. The present study contributes greatly to the literature by applying EMT as an alternative to address the mediating role of green innovation capability and the moderating effect of green management system implementation in enhancing firms' green innovation performance.
Sustainable crowdfunding has emerged as a significant factor in the quest for alternative funding streams in recent times. The process has entailed the removal of financial obstacles and intermediaries, facilitating proximity between entrepreneurs' initiatives and fund providers, thereby initiating modifications in conventional investment and profitability criteria. The correlation between corporate social responsibility (CSR) and sustainable business returns is a significant metric that may enhance funding costs. CSR initiatives and crowdfunding possess the potential for mutually beneficial outcomes in terms of fundraising. However, fundraisers encounter obstacles and competition in their efforts to attain their donation objectives. As an illustration, CSR endeavors may provide a chance to raise capital via crowdfunding. Conversely, crowdfunding has the potential to serve as a means of micro-funding various social initiatives that align with a corporation's corporate social responsibility objectives. The present research investigates the correlation between efficacious donation fundraising campaigns in the context of crowdfunding endeavors that hold the possibility of transforming into corporate social responsibility initiatives. The present study investigates the correlation between the initial amount of funds raised on the first day of a fundraising campaign and the target amount of funds sought by the fundraiser, as well as the type of activities involved. The present study utilizes data derived from crowdfunding endeavors in Southeast Asian nations to scrutinize the funds amassed through donations by juxtaposing trends, cultures, and characteristics of fundraisers employing donation-based crowdfunding. The present investigation employs data collected between the period spanning from the beginning of September 2021 to the end of September 2021 in the economies of Southeast Asia, including Singapore, Indonesia, Malaysia, Thailand, and the Philippines. The present investigation utilizes the partial least squares structural equation modeling (PLS-SEM) approach for the estimation of the variables. The findings of the hypothesis indicate that there exists a positive correlation between crowdfunding, environmental nonprofit organizations, organizational profitability, and CSR.
This study analyses the relationship between democracy and environmental pollution in the MINT countries using a panel data spanning 1971-2016. It also investigates the interactive effect of income and democracy on CO2 emissions. We used various estimation techniques for the analysis, ranging from the quantile regression, OLS-fixed effect and GLS-random effect regressions with Driscoll-Kraay standard errors to control for cross-sectional dependence while a panel threshold regression is used for robustness check. The results showed existence of long-run relationship between CO2 emissions and the explanatory variables. The quantile regression results for interaction model indicate that economic growth, democracy and trade openness promote environmental pollution via their positive effects on CO2 emissions. Primary energy however reduces pollution across the lower and middle quantiles but enhances it in higher quantiles. The interaction effect is negative and statistically significant across all quantiles. This implies that democracy has a significant role in moderating the impact of income on CO2 emission in the MINT countries. It thus follows that if the MINT countries radically strengthen democracy and enhance income, it would be possible for them to achieve greater economic development and reduce CO2. In addition, a single threshold model is used to identify the asymmetry in response to CO2 emissions at lower and upper levels of democratic regimes. The results showed that once the degree of democracy is above the threshold level, an increase in income would reduce CO2 emissions but once it is below the threshold level, the effect of income becomes insignificant. Based on these results, the MINT countries need to strengthen democracy, enhance income level and relax trade barriers.
Sugarcane vinasse has been reported as a high strength industrial wastewater that could cause severe environmental pollution due to its complex and bio-refractory compounds. Thus, the combined coagulation and sequencing batch biofilm reactor (SBBR) system was employed for the sugarcane vinasse treatment. This study aims to determine the recommended conditions of various parameters under coagulation and SBBR and investigate the effectiveness of combined processes. First, the approach of the coagulation process could achieve the maximum COD reduction and decolorization efficiencies of 79.0 ± 3.4% and 94.1 ± 1.9%, respectively, under the recommended conditions. Next, SBBR as an integrated biofilm reactor showed excellent synergistic biodegradability, removing 86.6 ± 4.3% COD concentration and 94.6 ± 3.8% color concentration at 3.0 g·COD/L of substrate loading concentration. The kinetic studies of SBBR revealed that the first-order kinetic model was the best fit for COD reduction efficiency. In contrast, the second-order kinetic model was the best fit for decolorization efficiency. The SBBR reaction was further investigated by ultraviolet-visible spectrophotometry (UV-Vis). In the combined processes, SBBR followed by the coagulation process (SBBR-CP) showed greater COD reduction and decolorization efficiencies (97.5 ± 0.3 and 99.4 ± 0.1%) when compared to the coagulation process followed by SBBR (CP-SBBR). This study demonstrated the removal performance and potential application of the combined sequential process to produce effluent that can be reused for bioethanol production and fertigation. This finding provides additional insight for developing effective vinasse treatment using combined chemical and biological processes.
We employ the new Method of Moments Quantile Regression approach to expose the role of natural resources, renewable energy, and globalization in testing Environment Kuznets Curve (EKC) in MINT panel covering the years 1995-2018. The outcome validates the EKC curve between economic progress and carbon emissions from the third quantile to the extreme highest quantile. The result also shows that natural resources increase CO2 emissions at the lowest quantile and then turn insignificant from the middle to the highest quantiles due to the potential utilization of resources in a sustainable manner. The renewable energy mitigates CO2 emissions at the lower half quantiles. Still, for upper quantiles, the results are unexpected and imply that the countries' total energy mix depends heavily on fossil fuels. As far as globalization is concerned, the significant results from medium to upper quantiles reveal that as globalization heightens due to foreign direct investment or trade, energy consumption also expands, leading to the worst environment quality. Thus, the present study's consequences deliver guidelines for policymakers to utilize natural resources sustainably and opt technologies based on clean energy, which may offset environmental degeneration.
Rapid increases in energy consumption and economic growth over the past three decades are considered the driving force behind rising environmental degradation, which remain a threat to people and healthy environment. This study investigates the impact of energy consumption on environmental quality in the MINT countries using a panel PMG/ARDL modelling technique, and the Granger causality test spanning from 1971 to 2017. The empirical results confirm the existence of long-run nexus among the variables employed. The results also reveal that economic growth, energy consumption and bio-capacity have a positive and statistically significant effect on environmental degradation during the long run period. We find that a 1% increase in primary energy consumption leads to 0.4172% increase in environmental deterioration in the long-run period, but it is insignificant in the short run. This implies that energy consumption deteriorates environmental quality through a negative effect of ecological footprint. The result also suggests that as MINT countries increase the use of energy to accelerate pace of economic growth, environmental quality would deteriorate through increased ecological footprints. The coefficient of the error correction term (ect) is negative and significant (- 0.2306), suggesting that ecological footprint, a measure of environmental degradation would converge to its long-run equilibrium in the MINT region by 23.06% speed of adjustment every year due to contribution of economic growth, energy consumption, urbanization and biocapacity. The Granger non-causality test results reveal a unidirectional causal relationship from economic growth, energy consumption, and urbanization to ecological footprint and from economic growth to biocapacity. The results further show bi-directional causality between biocapacity and ecological footprint as well as between biocapacity and economic growth. Moreover, urbanization causes economic growth and biocapacity Granger-causes urbanization. Based on these findings, policy implications are adequately discussed.
In the face of mounting climate change challenges, reducing emissions has emerged as a key driver of environmental sustainability and sustainable growth. Despite the fact that research has been conducted on the environmental Kuznets curve (EKC), few researchers have analyzed this in the light of economic complexity. Thus, the current research assesses the effect of economic complexity on CO2 emissions in the MINT nations while taking into account the role of financial development, economic growth, and energy consumption for the period between 1990 and 2018. Using the novel method of moments quantile regression (MMQR) with fixed effects, an inverted U-shape interrelationship is found between economic growth and CO2 emissions, thus validating the EKC hypothesis. Energy consumption and economic complexity increase CO2 emissions significantly from the 1st to 9th quantiles. Furthermore, there is no significant interconnection between financial development and CO2 emissions across all quantiles (1st to 9th). The outcomes of the causality test reveal a feedback causal connection between economic growth and CO2, while a unidirectional causality is established from economic complexity and energy use to CO2 emissions in the MINT nations. Based on the findings, we believe that governments should stimulate the financial sector to provide domestic credit facilities to industrialists, investors, and other business enterprises on more favorable terms so that innovative technologies for environmental protection can be implemented with other policy recommendations.
Zinc (Zn) was identified as one of the most toxic heavy metals and often found contaminating the water sources as a result of inefficient treatment of industrial effluent. A green emulsion liquid membrane (GELM) was proposed in this study as a method to minimize the concentration of Zn ions in an aqueous solution. Instead of the common petroleum-based diluent, the emulsion is reformulated with untreated waste cooking oil (WCO) collected from the food industry as a sustainable and cheaper diluent. It also includes Bis(2-ethylhexyl) phosphate (D2EHPA) as a carrier, Span 80 as a surfactant, sulfuric acid (H2SO4) as an internal phase, and ZnSO4 solution as an external phase. Such formulation requires a thorough understanding of the oil characteristics as well as the interaction of the components in the membrane phase. The compatibility of WCO and D2EHPA, as well as the external phase pH, was confirmed via a liquid-liquid extraction (LLE) method. To obtain the best operating conditions for Zn extraction using GELM, the extraction time and speed, carrier, surfactant and internal phase concentrations, and W/O ratio were varied. 95.17% of Zn ions were removed under the following conditions; 0.001 M of H2SO4 in external phase, 700 rpm extraction speed for 10 min, 8 wt% of carrier and 4 wt% of surfactant concentrations, 1:4 of W/O ratio, and 1 M of internal phase concentration.
This study looked at the state-of-the-art present knowledge base and trends in the area of using rejuvenators in reclaimed asphalt pavement (RAP) by systemic analysis and visualisation using VOSviewer and Scopus analyser; a total of 1872 studies were mined from the Scopus database for the purpose of this study. This quantitative approach to the review of literature removes author bias. The study was able to identify keywords and their cluster groups making up of core research domains ((1) asphalt binder composition and properties, (2) reclaimed asphalt mixtures (recycling), (3) reclaimed asphalt performance characteristics, (4) reclaimed asphalt sustainability, (5) rejuvenating agents and their performance, and (6) area of application). The study was able to identify the top authors; their document counts and citations; the most influential journals, institutions, and countries leading the way in the research domain; and the link between these authors and keywords within the existing body of literature in the research area. This study will help policymakers in identifying the main research themes and possible area of investments for further research in RAP. This study will also be a valuable compendium to researchers who intend to broaden the scope of the research area.