The rapid rise in climate and ecological challenges have allowed policymakers to introduce stringent environmental policies. In addition, financial limitations may pose challenges for countries looking to green energy investments as energy transition is associated with geopolitical risks that could create uncertainty and dissuade green energy investments. The current study uses PTR and PSTR as econometric strategy to investigate how geopolitical risks and financial development indicators influence energy transition in selected industrial economies. Our findings indicate a non-linear DCPB-RE relationship with a threshold equal to 39.361 in PTR model and 35.605 and 122.35 in PSTR model. Additionally, when the threshold was estimated above, financial development indicators and geopolitical risk positively impacts renewable energy. This confirms that these economies operate within a geopolitical context, with the objective of investing more in clean energy. We report novel policy suggestion to encourage policymakers promoting energy transition and advance the sustainable financing development and ecological sustainability.
The Yoon-Nelson model serves as a widely used tool for describing the breakthrough behavior of organic micropollutants within fixed bed adsorbers. This study aims to augment its modeling efficacy through two proposed refinements found in the literature: a logarithmic transformation and the incorporation of steric hindrance effects. We systematically evaluated the original Yoon-Nelson model alongside the modified versions, using breakthrough data associated with micropollutant adsorption on solid materials. Three distinct cases were scrutinized: (1) caffeine adsorption on activated carbon; (2) tetracycline adsorption on hierarchical porous carbon; and (3) diclofenac adsorption on organoclay. While all three models demonstrated comparable performance with highly symmetric breakthrough data in case 1, their efficacy diverged significantly when confronted with strongly asymmetric breakthrough data in cases 2 and 3. The original Yoon-Nelson model and the logarithmically modified version fell short in accurately representing these intricate breakthrough curves. In contrast, the version incorporating steric hindrance effects showcased substantial accuracy, outperforming other models in capturing the complexities of asymmetric breakthrough data. This advancement markedly enhances the modeling accuracy and versatility of the Yoon-Nelson model, particularly in assessing the dynamic behavior of organic micropollutants within fixed bed adsorbers.
Microplastics (MPs) are a pervasive pollutant in the marine environment. Pantai Teluk Likas in Sabah, Malaysia is one of the most visited beaches where tourism, recreational, and fisheries activities are high in this area. Hence, the area suffers from severe pollution, particularly from plastics. This study aims to quantify the microplastic composition in terms of color, shapes, and polymer types in marine bivalves (Anadara granosa, Glauconome virens, and Meretrix lyrata) and water column samples from Pantai Teluk Likas. All samples were digested using sodium hydroxide (NaOH) and incubated in the oven for at least 48 h. Serial filtration was done for each sample before they were observed under the dissecting microscope. The microplastics were identified and counted based on their physical attributes which were colors and shapes. The functional group of the polymers was determined using FTIR spectroscopy. Microplastics were found present in all samples collected. G. virens had the highest abundance of microplastics at 113.6 ± 6.5 particles/g followed by M. lyrata at 78.4 ± 3.7 particles/g. On the contrary, A. granosa had the least microplastics with an abundance of 24.4 ± 0.6 particles/g. Meanwhile, 110.0 ± 36.2 particles/L of microplastics were found in water column samples from Pantai Teluk Likas. Based on the analysis, fibers were the most common shape in bivalves, while fibers and films were common in the water column. In terms of colors, black, blue, and red were a few of the most abundant colors observed in both samples. The most common polymer detected in all bivalve species and water column samples is polycarbonate (PC), followed by polymethyl methacrylate (PMMA). Future study that focuses on the correlation between microplastic abundance in the marine biota and the water column is recommended to better understand microplastic availability and exposure.
Biogas plant operators often face huge challenges in the monitoring, controlling and optimisation of the anaerobic digestion (AD) process, as it is very sensitive to surrounding changes, which often leads to process failure and adversely affects biogas production. Conventional implemented methods and mechanistic models are impractical and find it difficult to model the nonlinear and intricate interactions of the AD process. Thus, the development of machine learning (ML) algorithms has attracted considerable interest in the areas of process optimization, real-time monitoring, perturbation detection and parameter prediction. This paper provides a comprehensive and up-to-date overview of different machine learning algorithms, including artificial neural network (ANN), fuzzy logic (FL), adaptive network-based fuzzy inference system (ANFIS), support vector machine (SVM), genetic algorithm (GA) and particle swarm optimization (PSO) in terms of working mechanism, structure, advantages and disadvantages, as well as their prediction performances in modelling the biogas production. A few recent case studies of their applications and limitations are also critically reviewed and compared, providing useful information and recommendation in the selection and application of different ML algorithms. This review shows that the prediction efficiency of different ML algorithms is greatly impacted by variations in the reactor configurations, operating conditions, influent characteristics, selection of input parameters and network architectures. It is recommended to incorporate mixed liquor volatile suspended solids (MLVSS) concentration of the anaerobic digester (ranging from 16,500 to 46,700 mg/L) as one of the input parameters to improve the prediction efficiency of ML modelling. This review also shows that the combination of different ML algorithms (i.e. hybrid GA-ANN model) could yield better accuracy with higher R2 (0.9986) than conventional algorithms and could improve the optimization model of AD. Besides, future works could be focused on the incorporation of an integrated digital twin system coupled with ML techniques into the existing Supervisory Control and Data Acquisition (SCADA) system of any biogas plant to detect any operational abnormalities and prevent digester upsets.
Sub-Saharan African nations face multifaceted environmental problems, especially those associated with carbon discharges. Hence, this study calculates a composite carbon index in the context of 39 developing nations from this region and uses it as a proxy for the carbon emission-related environmental problems they have faced during the 2000-2020 period. This index is estimated by utilizing data regarding annual carbon dioxide discharges, output-based carbon productivity rates, and energy consumption-based carbon intensity levels in the concerned countries. Hence, policy takeaways from this study have critical relevance for the selected sub-Saharan African nations to help them achieve the objectives related to the Sustainable Development Goals agenda and the Paris Accord. Overall, the findings from the econometric analyses verify that more receipt of foreign direct investment initially raises but later on reduces environmental problems. Thus, the nexus concerning these variables depicts an inverse U-shape. Besides, the results endorse that greening the energy consumption structures of the sampled sub-Saharan African countries helps to abate their environmental problems in the long run while financial development aggravates the extent of environmental adversities that take place. Lastly, improving the quality of regulatory agencies enables the Sub-Saharan African nations to further mitigate their environmental problems. Moreover, these aforementioned findings are observed to be heterogeneous across low- and middle-income categories of the selected Sub-Saharan African countries. Furthermore, the heterogeneity of the findings is also confirmed by the outcomes derived from the country-specific analyses. Nevertheless, these nations should attract clean energy-embodying foreign direct investment, make their energy consumption structures greener by amplifying renewable energy adoption rates, introduce green funds to develop their financial sectors, and make their environmental regulatory agencies more transparent with their activities.
Benthic microbial fuel cell (BMFC) is the most promising type of bioelectrochemical approach for producing electrons and protons from natural organic waste. In the present work, a single-chamber BMFC was used, containing sago (Cycas revoluta) waste as the organic feed for microorganisms. The local wastewater was supplemented with heavy metal ions (Pb2+, Cd2+, Cr3+, Ni2+, Co2+, Ag+, and Cu2+) and used as an inoculation source to evaluate the performance of BMFC against the toxic metal remediations. According to the experimental results, the maximum power density obtained was 42.55 mW/m2 within 25 days of the BMFC operation. The maximum remediation efficiency of the metal ion removal from the wastewater was found to be 99.30% (Ag+). The conductive pili-type bacteria species (Acinetobacter species, Leucobacter species, Bacillus species, Proteus species. and Klebsiella pneumoniae) were found in the present study during isolation and identification processes. This study's multiple parameter optimization revealed that pH 7 and room temperature is the best condition for optimal performance. Finally, this study included the mechanism, future recommendations, and concluding remarks.
Climate change repercussions such as temperature shifts and more severe weather occurrences are felt globally. It contributes to larger-scale challenges, such as climate change and biodiversity loss in food production. As a result, the purpose of this research is to develop strategies to grow the economy without harming the environment. Therefore, we revisit the environmental Kuznets curve (EKC) hypothesis, considering the impact of climate policy uncertainty along with other control variables. We investigated yearly panel data from 47 Belt and Road Initiative (BRI) nations from 1998 to 2021. Pooled regression, fixed effect, and the generalized method of moment (GMM) findings all confirmed the presence of inverted U-shaped EKC in BRI counties. Findings from this paper provide policymakers with actionable ideas, outlining a framework for bringing trade and climate agendas into harmony in BRI countries. The best way to promote economic growth and reduce carbon dioxide emissions is to push for trade and climate policies to be coordinated. Moreover, improving institutional quality is essential for strong environmental governance, as it facilitates the adoption of environmentally friendly industrialization techniques and the efficient administration of climate policy uncertainties.
The large use of renewable sources and plug-in electric vehicles (PEVs) would play a critical part in achieving a low-carbon energy source and reducing greenhouse gas emissions, which are the primary cause of global warming. On the other hand, predicting the instability and intermittent nature of wind and solar power output poses significant challenges. To reduce the unpredictable and random nature of renewable microgrids (MGs) and additional unreliable energy sources, a battery energy storage system (BESS) is connected to an MG system. The uncoordinated charging of PEVs offers further hurdles to the unit commitment (UC) required in contemporary MG management. The UC problem is an exceptionally difficult optimization problem due to the mixed-integer structure, large scale, and nonlinearity. It is further complicated by the multiple uncertainties associated with renewable sources, PEV charging and discharging, and electricity market pricing, in addition to the BESS degradation factor. Therefore, in this study, a new variant of mixed-integer particle swarm optimizer is introduced as a reliable optimization framework to handle the UC problem. This study considers six various case studies of UC problems, including uncertainties and battery degradation to validate the reliability and robustness of the proposed algorithm. Out of which, two case studies defined as a multiobjective problem, and it has been transformed into a single-objective model using different weight factors. The simulation findings demonstrate that the proposed approach and improved methodology for the UC problem are effective than its peers. Based on the average results, the economic consequences of numerous scenarios are thoroughly examined and contrasted, and some significant conclusions are presented.
Contamination of groundwater by harmful substances poses significant risks to both drinking water sources and aquatic ecosystems, making it a critical environmental concern. Most on-land spill events release organic molecules known as light non-aqueous phase liquids (LNAPLs), which then seep into the ground. Due to their low density and organic composition, they tend to float as they reach the water table. LNAPLs encompass a wide range of non-aqueous phase liquids, including various petroleum products, and can, over time, develop carcinogenic chemicals in water. However, due to frequent changes in hydraulic head, the confinement may fail to contain them, causing them to extend outward. When it contaminates water wells, people cannot reliably consume the water. The removal of dangerous contaminants from groundwater aquifers is made more challenging by LNAPLs. It is imperative to analyze the mechanisms governing LNAPL migration. As a response to this need and the associated dispersion of contaminants into adjacent aquifers, we have conducted a comprehensive qualitative literature review encompassing the years 2000-2022. Groundwater variability, soil structure, and precipitation have been identified as the three primary influential factors, ranked in the following order of significance. The rate of migration is shown to rise dramatically in response to changes in groundwater levels. Different saturation zones and confinement have a major effect on the lateral migration velocity. When the various saturation zones reach a balance, LNAPLs will stop moving. Although higher confinement slows the rate of lateral migration, it speeds up vertical migration. Beyond this, the lateral or vertical movement is also influenced by differences in the permeability of soil strata. Reduced mobility and tighter containment are the outcomes of migrating through fine-grained, low-porosity sand. The gaseous and liquid phases of LNAPLs move more quickly through coarse-grained soils. Due to the complexities and uncertainties associated with LNAPL behavior, accurately foreseeing the future spread of LNAPLs can be challenging. Although studies have utilized modeling techniques to simulate and predict LNAPL migration, the inherent complexities and uncertainties in the subsurface environment make it difficult to precisely predict the extent of LNAPL spread in the future. The granular soil structure considerably affects the porosity and pore pressure.
The government of any country can play a great role in promoting economic and environmental policy reforms in both normal and crisis periods, but during the crisis period, the role of the government should take the economy into a recovery position. The stock market is the backbone of the financial system that needs the government's attention, especially in the period of financial stress and environmental protection is the responsibility of every economy to live in a healthy environment. Combining this motive, this study analyzed the role of the government towards the stock market and carbon emission by using different approaches, including the wavelet approach, OLS regression, and the Granger causality test. The wavelet approach is useful for analyzing the role of the government at different time intervals by using the time horizon from 1993 to 2021. World governance's six indicators in terms of voice and accountability, control of corruption, rules of law, regulatory quality, political stability, and government effectiveness are used as the proxy for the role of the government. Our findings show that all WGI indicators have a positive relationship with the stock market of Malaysia except voice and accountability while concerning voice and accountability, the role of the government of Malaysia is negative on the stock market. Similarly, our findings also show that the effective government governance mechanism through WGI indicators has a significant positive impact on CO2 emission due to industrialization. Furthermore, findings of the Granger causality test reveal that all the WGI indicators cause to stock market of Malaysia, and political stability has bi-directional causality indicating stock market index is also a factor that caused the political stability within Malaysia. In the Granger causality results of the CO2 and WGI indicators, there is unidirectional causality found between rules of law and regulatory quality with CO2 emission. This study advocated strong implementations for the investors for investment decisions in effective governance countries and implications for the government to remove their weakness by making effective governance related to the economy and as well as the environments within the country.
As an inland dryland lake basin, the rivers and lakes within the Lake Bosten basin provide scarce but valuable water resources for a fragile environment and play a vital role in the development and sustainability of the local societies. Based on the Google Earth Engine (GEE) platform, combined with the geographic information system (GIS) and remote sensing (RS) technology, we used the index WI2019 to extract and analyze the water body area changes of the Bosten Lake basin from 2000 to 2021 when the threshold value is -0.25 and the slope mask is 8°. The driving factors of water body area changes were also analyzed using the partial least squares-structural equation model (PLS-SEM). The result shows that in the last 20 years, the area of water bodies in the Bosten Lake basin generally fluctuated during the dry, wet, and permanent seasons, with a decreasing trend from 2000 to 2015 and an increasing trend between 2015 and 2019 followed by a steadily decreasing trend afterward. The main driver of the change in wet season water bodies in the Bosten Lake basin is the climatic factors, with anthropogenic factors having a greater influence on the water body area of dry season and permanent season than that of wet season. Our study achieved an accurate and convenient extraction of water body area and drivers, providing up-to-date information to fully understand the spatial and temporal variation of surface water body area and its drivers in the basin, which can be used to effectively manage water resources.
This study critically examines the dynamic interplay between green finance and environmental sustainability using a systematic review and bibliometric analysis. The analysis is centered on 507 scholarly articles published between 2013 and 2023 in the Scopus database and leverages Microsoft Excel, Harzing Publish or Perish, and VOSviewer to identify publication trends, key contributors, research impact, and emergent themes in this rapidly evolving field. The findings reveal that research on green finance and environmental sustainability has increased exponentially over the past decade, with China and institutions in Asia emerging as prominent contributors compared to other regions. This study also identified the Environmental Science and Pollution Research journal as the most active source title, demonstrating its commitment to publishing current findings on the topic. Through keyword analysis, several research avenues have been proposed to guide future research on enhancing the strategic role of green finance in promoting environmental sustainability. These avenues include broadening the geographical scope of research, exploring the synergies between green finance and emerging fintech innovations, developing robust metrics to quantify the socioeconomic impacts of green finance, establishing a risk and resilience framework to protect green finance against uncertainties, and creating a Green Finance Performance Index to evaluate the dual returns of environmental and financial performance.
Estimating the asymmetrical influence of foreign direct investment is the primary goal of the current study. In addition, further controlled variables affect environmental degradation in OIC nations. Due to this, current research employs the asymmetric (NPARDL) approach and the data period from 1980 to 2021 to estimate about viability of the EKC (environmental Kuznets curve) theory. The study utilized greenhouse gas (GHG) including emissions of carbon dioxide (CO2), nitrous oxide (N2O), methane (CH4), and ecological footprint as substantial parameters of environmental quality. A nonlinear link between foreign direct investments, trade openness, economic growth, urbanization, energy consumption, and environmental pollution with CO2, N2O, CH4, and ecological footprint in the OIC nations is confirmed by the study's outcomes, which however reveals inconsistent results. Furthermore, the results also show that wrong conclusions might result from disregarding intrinsic nonlinearities. The study's conclusions provide the most important recommendations for decision-makers.
A country's financing system is essential in addressing sustainable development requirements. National sources and international financial flows contribute to economic growth and environmental quality in many ways, and their impact can be critical. This paper applied panel data analysis using a comparative approach of Pooled Mean Group Auto Regressive Distribute Lags (PMG-ARDL) and Cross Sectionally ARDL (CS-ARDL) to estimate the effects of FDI, renewable energy, and remittance on environmental quality in the top remittance-receiving countries, during 2000-2021. The study emphasized the positive relationship between FDI and carbon emissions. Moreover, renewable energy and remittances revealed an inverted U-shaped relationship with carbon emissions. In the case of developing countries from the panel, remittance improves environmental quality after reaching the threshold. Moreover, for some of the developing countries included in the panel, we found that they do not achieve the desired carbon mitigation effect in their early stages of renewable energy implementation. However, renewable energy becomes a key factor for tackling environmental pollution after a certain threshold. The mixed results determined diverse policy recommendations for various stakeholders.
This study aims to compare the impact of Construction 4.0 technologies on different organizational core values, focusing on sustainability and resiliency, well-being, productivity, safety, and integrity. To achieve that aim, the study objectives are the following: (i) identify the critical Construction 4.0 technologies between core values; (ii) appraise overlapping critical Construction 4.0 technologies between core values; (iii) examine the ranking performance of Construction 4.0 technologies between core values; and (iv) analyze the interrelationships between Construction 4.0 technologies and core values. First, twelve Construction 4.0 technologies were identified from a national strategic plan. Then, the fuzzy technique for order of preference by similarity to ideal solution (TOPSIS) that incorporates subjective and objective weights was used to evaluate the impact of the Construction 4.0 technologies on the five core values. Finally, the collected data was analyzed using the following techniques: fuzzy TOPSIS, normalization, overlap analysis, agreement analysis, sensitivity analysis, ranking comparison, and Spearman correlation. The study findings reveal four critical Construction 4.0 technologies that enhance all five core values: building information modeling (BIM), Internet of Things (IoT), big data and predictive analytics, and autonomous construction. Also, there is a high agreement on the Construction 4.0 technologies that enhance well-being and productivity. Lastly, artificial intelligence (AI) has the highest number of very strong relationships among the core values. The originality of this paper lies in its comprehensive comparison of the impact of Construction 4.0 technologies on multiple organizational core values. The study findings provide valuable insights in making strategic decisions in adopting Construction 4.0 technologies.
Enterprises across the globe are facing increasing pressure to effectively utilize resources and reduce costs through green supply chain practices. Emerging technology, such as blockchain technology which enables green practices, has become a contemporary industrial paradigm. However, enterprises need to build green intellectual capital to implement blockchain technology, which can be key to realizing green supply chain practices. This research examines the impact of green intellectual capital (GIC) on blockchain technology and its role in implementing green manufacturing to achieve sustainability. Partial least squares structural equation modeling was utilized for assessing the proposed hypotheses, and cross-sectional data was accumulated from manufacturing firms. As per the results, GIC, which includes green human capital, green structural capital, and green relational capital has a crucial role in the implementation of blockchain technology. The outcomes also indicated that the adoption of blockchain technology significantly influences green manufacturing. Moreover, green manufacturing (GM) has a substantial role in improving business sustainability. This empirical research provides a deeper understanding of how GIC and blockchain technology contribute to the implementation of GM. This research also provides guidelines that managers, policymakers, and producers can use to facilitate the incorporation of GM practice into business activities.
Choosing a suitable gridded climate dataset is a significant challenge in hydro-climatic research, particularly in areas lacking long-term, reliable, and dense records. This study used the most common method (Perkins skill score (PSS)) with two advanced time series similarity algorithms, short time series distance (STS), and cross-correlation distance (CCD), for the first time to evaluate, compare, and rank five gridded climate datasets, namely, Climate Research Unit (CRU), TERRA Climate (TERRA), Climate Prediction Center (CPC), European Reanalysis V.5 (ERA5), and Climatologies at high resolution for Earth's land surface areas (CHELSA), according to their ability to replicate the in situ rainfall and temperature data in Nigeria. The performance of the methods was evaluated by comparing the ranking obtained using compromise programming (CP) based on four statistical criteria in replicating in situ rainfall, maximum temperature, and minimum temperature at 26 locations distributed over Nigeria. Both methods identified CRU as Nigeria's best-gridded climate dataset, followed by CHELSA, TERRA, ERA5, and CPC. The integrated STS values using the group decision-making method for CRU rainfall, maximum and minimum temperatures were 17, 10.1, and 20.8, respectively, while CDD values for those variables were 17.7, 11, and 12.2, respectively. The CP based on conventional statistical metrics supported the results obtained using STS and CCD. CRU's Pbias was between 0.5 and 1; KGE ranged from 0.5 to 0.9; NSE ranged from 0.3 to 0.8; and NRMSE between - 30 and 68.2, which were much better than the other products. The findings establish STS and CCD's ability to evaluate the performance of climate data by avoiding the complex and time-consuming multi-criteria decision algorithms based on multiple statistical metrics.
Current research on environmental instruments often isolates the two mainstream types, market-based and regulation-based, overlooking their real-world interactions. In response, the intensity gap variable (EII_GAP) is constructed to link various instruments into a united system. Thus, based on the spatial econometrics of the spatial panel Durbin model (SPDM), the collective effects between market- and regulation-based environmental instruments on environmental quality are explored. Moreover, the political strategies for maximizing environmental benefits are discussed. Results show that the interaction pattern between market- and regulation-based environmental instruments on environmental quality is characterized by competition rather than cooperation. A unit widening in the intensity gap leads to 17 to 18% and 12 to 18% units of environmental quality improvement in local and adjacent areas, respectively. Furthermore, the "dominate-follow" approach as the most effective mode for maximizing environmental effects is proposed. This study recommends employing one type of instrument as the dominant while the other as the auxiliary. In provinces where one kind of environmental instrument takes domination, the environmental quality could be increased by around 8 to 113% after taking another contrary instrument as the auxiliary.
This paper comprehensively examines passive and active energy retrofit strategies as a highly effective approach for reducing building energy consumption and mitigating CO2 emissions while enhancing comfort and sustainability. The paper further examines energy simulation software and assesses the integration of renewable energy systems in building to improve energy efficiency. The review used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, ensuring a rigorous and comprehensive analysis. In addition, the study utilized bibliometric analysis with VOSviewer to provide valuable insights into the research trends and influential publications in building energy retrofits. Bibliometric analysis reveals strong collaboration among 17 authors, emphasizing their significant contributions. Keywords like energy retrofitting and efficiency are prominent, indicating their importance in academic literature. Findings show passive strategies are more effective in reducing energy consumption, though a combined approach with active strategies can yield optimal results. Retrofitting presents challenges, such as substantial initial costs and regulatory barriers. User acceptance is crucial, considering potential disruptions. The review highlights the importance of energy simulation software, with tools like EnergyPlus, eQUEST, and IES VE identified for evaluating and identifying cost-effective retrofit measures in building performance. By providing comprehensive insights into the various strategies and tools available for retrofitting buildings to achieve energy efficiency and sustainability goals, this review serves as an authoritative resource for building owners, managers, and professionals in the building industry. It offers invaluable guidance for informed decision-making and facilitates implementing effective, energy-efficient, and sustainable building retrofitting practices.