Our current dependency on the oil and gas (O&G) industry for economic development and social activities necessitates research into the sustainability of the industry's supply chains. At present, studies on sustainable supply chain management (SSCM) practices in the industry do not include firm-internal factors that affect the sustainability strategies employed by different functional areas of its supply chains. Our study aims to address this gap by identifying the relevant internal factors and exploring their relationship with SSCM strategies. Specifically, we discuss the commitment to and preparedness for sustainable practices of companies that operate in upstream and downstream O&G supply chain. We study the impact of these factors on their sustainability strategies of four key supply chain functions: supplier management, production management, product stewardship and logistics management. The analyses of data collected through a survey among 81 companies show that management preparedness may enhance sustainable supply chain strategies in the O&G industry more than commitment does. Among the preparedness measures, management of supply chain operational risks is found to be vital to the sustainability of all supply chain functions except for production management practices. The findings also highlight the central importance of supplier and logistics management to the achievement of sustainable O&G supply chains. Companies must also develop an organizational culture that encourages, for example, team collaboration and proactive behaviour to finding innovative sustainability solutions in order to translate commitment to sustainable practices into actions that can produce actual difference to their SSCM practices.
Much of the environmental policy literature tends to focus on democratic contexts where environmental innovation is a product of pluralistic interactions among state and non-state actors. By bringing the (authoritarian) politics into the analysis, this article seeks to discover the processes leading to environmental innovation under nondemocratic conditions. We utilise case studies in China and then-nondemocratic Malaysia, both grappling with the twin imperatives of rapid development and social control, where the governments initiated environmental innovations to expand space for public participation and monitoring against noxious plants. We adapt the conceptual framework of "environmental innovation strategies" to illustrate the mechanisms underpinning innovative practices that address environmental issues by going beyond pre-existing public regulatory provisions. We highlight aspects distinguishing the interactive processes under authoritarianism. First, the drivers of environmental innovation are contingent on the government's role and concerns over social control and state legitimacy. Second, due to limits over political space, environmental nongovernmental organisations (ENGOs) act as issue entrepreneurs-instead of policy entrepreneurs-who turn conditions into problems deserving government attention and solution, as they engage in conflictual interactions with state authorities. Third, such innovations can strengthen nondemocratic governance while not fully plugging the gaps in existing environmental regulations. This contributes to illuminating the behaviours of state-based environmental innovators under illiberal political regimes, potentially offering lessons to activists on how to stimulate further innovations in such contexts.
The integrated economic reforms in recent years have transformed human life, however, the subsequent rise in environmental challenges necessitates sustainable development goals to ensure net-zero transformation. Within the context of modern energy, economic, and environmental transformation, we deliberate how environmental taxes, energy transition, and sustainable environmental innovation impact climate change in 38 OECD economies. Our robust empirical investigation allows us to report that environmental taxation, sustainable environmental technology, and energy transition lower but GDP and trade openness exacerbate ecological challenges. We also divide the dataset in G7 and the rest of the OECD groups to document the varying impact of environmental policies within OECD economies. Our econometric analysis helps us report novel policy frameworks to solve climate challenges under the UN SDG agenda.
Cadmium (Cd) pollution leads to soil degradation, decreases crop yield and affects human health through the food chain. Iron-modified woody peat (IMP) is an organic passivation material that significantly affects the migration of heavy metals in soil. Nitrification inhibitors are widely used to reduce greenhouse gas emissions. This study investigated the effects of the IMP and nitrification inhibitors dicyandiamide (DCD) and 3, 4-dimethylpyrazole phosphate on Cd content and form, crop yield, nitrous oxide (N2O) emission and bacterial communities in soil-lettuce systems. The simultaneous additions of IMP and DCD substantially reduced the soil available Cd content by 22.6 % and significantly promoted the lettuce yield by 42.9 %. Lettuce yield was significantly and negatively correlated with soil available Cd (correlation coefficient = -0.52). The simultaneous applications of IMP and nitrification inhibitors stimulated N2O emission risk by enhancing the soil NH4+-N contents and the relative abundances of Firmicutes, which could also decrease soil bacterial community stabilities. Therefore, tradeoffs among yield, Cd bioavailability, N2O emission and bacterial community stability should be comprehensively considered when evaluating the combined performances of IMP and nitrification inhibitors.
As global concerns over climate change and sustainability grow, Environmental, Social, and Governance (ESG) factors have become critical in evaluating corporate practices. In China, the increasing adoption of ESG ratings by investors has highlighted discrepancies in these ratings, which may impact corporate risk. While extensive research exists on ESG performance, the effects of ESG rating disparities on corporate risk, particularly in Chinese enterprises, remain underexplored, especially the mediating role of financing constraints. Utilizing data from Chinese A-share listed companies from 2015 to 2022, this study examines the impact of Environmental, Social, and Governance (ESG) rating disparities on corporate risk, focusing on the mediating role of financing constraints. The findings indicate that discrepancies in ESG ratings significantly increase corporate risk, particularly in non-state-owned enterprises and heavily polluting industries, while having no significant impact on state-owned enterprises. Discrepancies in governance ratings exert the greatest impact on corporate risk, underscoring the critical role of corporate governance. Financing constraints further exacerbate the impact of rating discrepancies on corporate risk. These results provide new insights into enhancing the ESG rating system and mitigating corporate risk, offering a foundation for relevant policy-making.
Global economies have recently been concerned about sustainable environmental management by reducing emissions and tackling ecological footprints. The rapid economic expansion and investment in traditional manufacturing further raises environmental degradation. China surpasses other emerging economies in the economic growth race yet has remained the top pollution-emitting economy for the last few decades, necessitating scholarly attention. This study examines the influencing factors of ecological footprints in China from the perspective of COP27. Using the extended dataset from 1988 to 2021, this study uses several time series diagnostic tests and verifies the existence of the long-run association between the study variables. Consequently, the non-linear scattered data leads to non-parametric (method of moment quantile regression) adoption. The empirical results indicate that only economic growth is a significant factor in environmental quality degradation in China. However, improving renewable energy usage, research and development, and foreign direct investment reduces the country's ecological footprint. Hence, the latter variables substantially lead to environmental sustainability. The robustness of the results is confirmed via a robust non-parametric estimator and causality test. Based on the empirical results, this study recommends increased investment in research and development, renewable production, and foreign direct investment enhancement.
The aim of this study is to evaluate the performance and antifouling properties of polyethersulfone (PES) membrane incorporated with dual nanofiller, zinc oxide (ZnO) and multi-walled carbon nanotube (MWCNT). The synergistic effect of the these nanofillers in PES membrane is studied by blending different ratio of ZnO/MWCNT nanofiller into the PES membrane. The fabricated membranes were characterized in terms of cross-section and surface morphology, surface hydrophilicity, pore size and porosity. The filtration performance of the membranes was tested using 50 mg/L humic acid (HA) solution as model solution. SEM image and gravimetric evaluation reported that the incorporation of both MWCNT and ZnO into the PES membrane improved porosity significantly up to 46.02%. Lower water contact angle of PES membrane incorporated with equal ratio of MWCNT and ZnO (PES 3) revealed that it has neat PES membrane properties and more hydrophilic membrane surface than single filler. PES 3 outperform other membranes with excellent HA permeate flux of 40.00 L/m2.h and rejection of 88.51%. Due to hydrophilic membrane surface, PES 3 membrane demonstrate efficient antifouling properties with lower relative flux reduction (RFR) and higher flux recovery ratio (FRR). PES 3 also showed notable antibacterial properties with less bacterial attached to the membrane compared to neat PES membrane (PES 0).
The production of renewable biofuel through microalgae and green technology can be a promising solution to meet future energy demands whilst reducing greenhouse gases (GHG) emissions and recovering energy for a carbon-neutral bio-economy and environmental sustainability. Recently, the integration of Energy Informatics (EI) technology as an emerging approach has ensured the feasibility and enhancement of microalgal biotechnology and bioenergy applications. Integrating EI technology such as artificial intelligence (AI), predictive modelling systems and life cycle analysis (LCA) in microalgae field applications can improve cost, efficiency, productivity and sustainability. With the approach of EI technology, data-driven insights and decision-making, resource optimization and a better understanding of the environmental impact of microalgae cultivation could be achieved, making it a crucial step in advancing this field and its applications. This review presents the conventional technologies in the microalgae-based system for wastewater treatment and bioenergy production. Furthermore, the recent integration of EI in microalgal technology from the AI application to the modelling and optimization using predictive control systems has been discussed. The LCA and techno-economic assessment (TEA) in the environmental sustainability and economic point of view are also presented. Future challenges and perspectives in the microalgae-based wastewater treatment to bioenergy production integrated with the EI approach, are also discussed in relation to the development of microalgae as the future energy source.
High nutrient loading in aquatic environment has become the main causative of harmful algae blooms (HABs) in water resources particularly pond, lake and river. HABs are mostly dominated by microalgae derived from the group of blue-green algae which are capable of releasing harmful toxins. Therefore, this study aims to investigate the inhibitory effects of thiourea derivatives on the growth of such blue-green algae. Thiourea derivatives have been proven to exhibit antifungal and antibacterial effects. However, there is still limited study had been conducted on the effect of thiourea derivatives toward blue-green algae species in recent years. In this research, a species of blue-green algae from Kenyir Lake, Terengganu, Malaysia was successfully isolated using morphological characters and molecularly identified as Synechoccus elongatus. Four new thiourea derivative compounds were also successfully synthesised. The compounds were designed with variation on different R-substitution group and characterised using Nuclear Magnetic Resonance (NMR) to confirm their molecular structure. Those compounds were characterised as 1-Benzyl-3-(3,5-dimethoxy-benzoyl)-thiourea (C1), 1-(3-Chloro-benzyl)-3-(3,5-dimethoxy-benzoyl)-thiourea (C2), 1-(3,5-Dimethoxy-benzoyl)-3-(3-methyl-benzyl)-thiourea (C3) and 1-(3,5-Dimethoxy-benzoyl)-3-(3-trifluoromethyl-benzyl)-thiourea (C4). For the inhibition assessment,S. elongatus were treated with C1-C4 for 5 day at concentration of 2, 5, 10 and 20 μg/ml, respectively. C3 compound showed the highest inhibition percentage with 98% of inhibition after 5 days treatment. By using Bradford method, protein extraction of S. elongatus was conducted at the highest inhibition percentage. Protein concentration of treated species was observed with 3.28 μg/ml as compared to protein concentration of control with 6.48 μg/ml. This result indicated the reduction of protein content after the treatment. Protein band pattern was identified intensed after the treatment SDS PAGE was carried out. The thiourea derivatives compound proved to have successfully inhibited the growth of blue-green algae. Hence, further study should be carried out to ensure the compound can be practically utilized in the pond and in natural environment.
Sequencing batch reactor (SBR) is one of the various methods of biological treatments used for treating wastewater and landfill leachate. This study investigated the treatment of landfill leachate and domestic wastewater by adding a new adsorbent (powdered ZELIAC; PZ) to the SBR technique. ZELIAC consists of zeolite, activated carbon, lime stone, rice husk ash, and Portland cement. The response surface methodology and central composite design were used to elucidate the nature of the response surface in the experimental design and describe the optimum conditions of the independent variables, including aeration rate (L/min), contact time (h), and ratio of leachate to wastewater mixture (%; v/v), as well as their responses (dependent variables). Appropriate conditions of operating variables were also optimized to predict the best value of responses. To perform an adequate analysis of the aerobic process, four dependent parameters, namely, chemical oxygen demand (COD), color, ammonia-nitrogen (NH3-N), and phenols, were measured as responses. The results indicated that the PZ-SBR showed higher performance in removing certain pollutants compared with SBR. Given the optimal conditions of aeration rate (1.74 L/min), leachate to wastewater ratio (20%), and contact time (10.31 h) for the PZ-SBR, the removal efficiencies for color, NH3-N, COD, and phenols were 84.11%, 99.01%, 72.84%, and 61.32%, respectively.
Sustainability has become a focus area for practitioners and scholars due to the growing socio-economic issues. The sustainability of airport operations is being raised in various international platforms. This paper aims to identify the dimensions of sustainability and evaluate sustainable practices in airports of selected ASEAN countries. The various dimensions associated with the environmental aspect are energy management, emissions management, water and effluents management, solid waste management. It was understood that noise management, employee development, and community investment belong to the social dimension. Similarly, the factors such as economic contribution, passenger experience, airport safety, and security are inclined to economic dimensions of sustainability. It was found that environmentally sustainable practices have greater importance than social and economic initiatives in the airport context which provide quantifiable benefits for airports in the long term. Airport operators in South East Asia strived to mitigate carbon emissions, reduce waste and effluents, enhance the economic contribution, satisfy passengers, and meet employee needs. Compared to the total economic and social benefits obtained from these airports, the negative impacts of airport operation (such as noise emission from aircraft) are minimal but significant. The most common sustainable initiatives in airports, such as employee development, energy management, and passenger safety, supported sustainable development goals (SDG) 8, SDG 9, and SDG 11. A weak connection is observed between SDG 14 & SDG 15 and the airport's sustainable practices. The new technological innovations are concentrated in busy and profitable airports. A slow trend towards the adoption of new technologies for sustainable practices is observed in airports. The paper concludes that major airport operators in South-East Asia have effectively responded to the growing sustainability challenges in aviation markets. The sustainable dimensions and practices discussed will be valuable resource for airports striving to achieve sustainability goals.
Microalgae technology, if managed properly, has promising roles in solving food-water-energy nexus. The Achilles' heel is, however, to lower the costs associated with cultivation and harvesting. As a favorable technique, application of membrane process is strongly limited by membrane fouling. This study evaluates performance of nylon 6,6 nanofiber membrane (NFM) to a conventional polyvinylidene fluoride phase inverted membrane (PVDF PIM) for filtration of Chlorella vulgaris. Results show that nylon 6,6 NFM is superhydrophilic, has higher size of pore opening (0.22 vs 0.18 μm) and higher surface pore density (23 vs 18 pores/μm2) leading to higher permeance (1018 vs 493 L/m2hbar) and better fouling resistant. Such advantages help to outperform the filterability of PVDF PIM by showing much higher steady-state permeance (286 vs 120 L/m2hbar), with comparable biomass retention. In addition, unlike for PVDF PIM, imposing longer relaxation cycles further enhances the performance of the NFM (i.e., 178 L/m2hbar for 0.5 min and 236 L/m2hbar for 5 min). Overall findings confirm the advantages of nylon 6,6 NFM over the PVDF PIM. Such advantages can help to reduce required membrane area and specific aeration demand by enabling higher flux and lowering aeration rate. Nevertheless, developments of nylon 6,6 NFM material with respect to its intrinsic properties, mechanical strength and operational conditions of the panel can still be explored to enhance its competitiveness as a promising fouling resistant membrane material for microalgae filtration.
Energy is widely used in industry for heating and cooling, with natural gas (NG) being the largest primary energy source in Malaysia, closely followed by coal. Renewable energy, such as biogas upgrading to biomethane, could cut the use of fossil fuels by supplementing NG usage due to their similar physicochemical and thermochemical characteristics. Biogas production plants in Malaysia are more commonly seen in waste-to-energy scenarios, with the technology anaerobic digestion, and their deployment is supported via feed-in tariffs (FiT) for power generation. Other potential applications such as the conversion of biogas into biomethane, injection into the natural gas grid or transportation through a virtual pipeline may still need further technical development. This paper presents spatial techno economic optimisation modelling using BeWhere to determine decentralised biomethane production plants using feedstock from multiple sources of biogas, including palm oil mill effluent (POME), food waste, cattle manure and chicken manure. This model considered potential configurations and sizes of the biomethane plants, the transportation of biomethane using a virtual pipeline (at 250 psig) and demand in one of the states in Malaysia, namely Johor. It was found that two to four biomethane plants with capacities ranging between 125 and 700 m3/h were located in densely populated areas or heavier industrial consumers when the carbon tax was implemented at 167.71 EUR/tCO2 (800 MYR/tCO2). Sensitivity analysis suggested that biomethane production increases with the increasing country renewable energy share target to beyond 2080 MW. It is recommended that specific policy regulations and Feed-in Tariff (FiT) mechanisms are used to expand the biomethane market share in the country.
Triphenylmethane dyes (TPM) are recalcitrant colorants brought into the environment. In this study, a lesser-known white rot fungus Coriolopsis sp. (1c3), isolated from compost of Empty Fruit Bunch (EFB) of oil palm, was explored for its decolorization potential of TPM dyes. The isolate 1c3 demonstrated good decolorization efficiencies in the treatment of Crystal Violet (CV; 100 mg l(-1)), Methyl Violet (MV; 100 mg l(-1)) and Cotton Blue (CB; 50 mg(-1)), with 94%, 97% and 91%, within 7, 7 and 1 day(s), respectively. Malachite Green (MG; 100 mg l(-1)) was the most recalcitrant dye, with 52% decolorization after 9 days. Dye removal by 1c3 was presumably via biosorption, whereby the process was determined to be influenced by fungal biomass, initial dye concentrations and oxygen requirements. Biodegradation was also a likely mechanism responsible for dye removal by 1c3, occurred as indicated by the reduction of dye spectra peaks. Detection of laccase, lignin peroxidase and NADH-DCIP reductase activities further substantiate the possible occurrence of biodegradation of TPM dyes by 1c3.
Urea removal is an important process in household wastewater purification and hemodialysis treatment. The efficiency of the urea removal can be improved by utilizing activated carbon fiber (ACF) for effective urea adsorption. In this study, ACF was prepared from oil palm empty fruit bunch (EFB) fiber via physicochemical activation using sulfuric acid as an activating reagent. Based on the FESEM result, ACF obtained after the carbonization and activation processes demonstrated uniform macropores with thick channel wall. ACF was found better prepared in 1.5:1 acid-to-EFB fiber ratio; where the pore size of ACF was analyzed as 1.2 nm in diameter with a predominant micropore volume of 0.39 cm(3) g(-1) and a BET surface area of 869 m(2) g(-1). The reaction kinetics of urea adsorption by the ACF was found to follow a pseudo-second order kinetic model. The equilibrium amount of urea adsorbed on ACF decreased from 877.907 to 134.098 mg g(-1) as the acid-to-fiber ratio increased from 0.75 to 4. During the adsorption process, the hydroxyl (OH) groups on ACF surface were ionized and became electronegatively charged due to the weak alkalinity of urea solution, causing ionic repulsion towards partially anionic urea. The ionic repulsion force between the electronegatively charged ACF surface and urea molecules became stronger when more OH functional groups appeared on ACF prepared at higher acid impregnation ratio. The results implied that EFB fiber based ACF can be used as an efficient adsorbent for the urea removal process.
The primary objective of this paper is to investigate the isolated impacts of hydroelectricity consumption on the environment in Malaysia as an emerging economy. We use four different measures of environmental degradation including ecological footprint, carbon footprint, water footprint and CO2 emission as target variables, while controlling for GDP, GDP square and urbanization for the period 1971 to 2016. A recently introduced unit root test with breaks is utilized to examine the stationarity of the series and the bounds testing approach to cointegration is used to probe the long run relationships between the variables. VECM Granger causality technique is employed to examine the long-run causal dynamics between the variables. Sensitivity analysis is conducted by further including fossil fuels in the equations. The results show evidence of an inverted U-shaped relationship between environmental degradation and real GDP. Hydroelectricity is found to significantly reduce environmental degradation while urbanization is also not particularly harmful on the environment apart from its effect on air pollution. The VECM Granger causality results show evidence of unidirectional causality running from hydroelectricity and fossil fuels consumption to all measures of environmental degradation and real GDP per capita. There is evidence of feedback hypothesis between real GDP to all environmental degradation indices. The inclusion of fossil fuel did not change the behavior of hydroelectricity on the environment but fossil fuels significantly increase water footprint.
The adsorption from aqueous solution of imidazolium, pyrrolidinium and pyridinium based bromide ionic liquids (ILs) having different alkyl chain lengths was investigated on two types of microporous activated carbons: a fabric and a granulated one, well characterized in terms of surface chemistry by "Boehm" titrations and pH of point of zero charge measurements and of porosity by N2 adsorption at 77 K and CO2 adsorption at 273 K. The influence of cation type, alkyl chain length and adsorbate size on the adsorption properties was analyzed by studying kinetics and isotherms of eight different ILs using conductivity measurements. Equilibrium studies were carried out at different temperatures in the range [25-55 °C]. The incorporation of ILs on the AC porosity was studied by N2 adsorption-desorption measurements at 77 K. The experimental adsorption isotherms data showed a good correlation with the Langmuir model. Thermodynamic studies indicated that the adsorption of ILs onto activated carbons was an exothermic process, and that the removal efficiency increased with increase in alkyl chain length, due to the increase in hydrophobicity of long chain ILs cations determined with the evolution of the calculated octanol-water constant (Kow). The negative values of free energies indicated that adsorption of ILs with long chain lengths having hydrophobic cations was more spontaneous at the investigated temperatures.
The concentration of soluble salts in surface water and rivers such as sodium, sulfate, chloride, magnesium ions, etc., plays an important role in the water salinity. Therefore, accurate determination of the distribution pattern of these ions can improve better management of drinking water resources and human health. The main goal of this research is to establish two novel wavelet-complementary intelligence paradigms so-called wavelet least square support vector machine coupled with improved simulated annealing (W-LSSVM-ISA) and the wavelet extended Kalman filter integrated with artificial neural network (W-EKF- ANN) for accurate forecasting of the monthly), magnesium (Mg+2), and sulfate (SO4-2) indices at Maroon River, in Southwest of Iran. The monthly River flow (Q), electrical conductivity (EC), Mg+2, and SO4-2 data recorded at Tange-Takab station for the period 1980-2016. Some preprocessing procedures consisting of specifying the number of lag times and decomposition of the existing original signals into multi-resolution sub-series using three mother wavelets were performed to develop predictive models. In addition, the best subset regression analysis was designed to separately assess the best selective combinations for Mg+2 and SO4-2. The statistical metrics and authoritative validation approaches showed that both complementary paradigms yielded promising accuracy compared with standalone artificial intelligence (AI) models. Furthermore, the results demonstrated that W-LSSVM-ISA-C1 (correlation coefficient (R) = 0.9521, root mean square error (RMSE) = 0.2637 mg/l, and Kling-Gupta efficiency (KGE) = 0.9361) and W-LSSVM-ISA-C4 (R = 0.9673, RMSE = 0.5534 mg/l and KGE = 0.9437), using Dmey mother that outperformed the W-EKF-ANN for predicting Mg+2 and SO4-2, respectively.
Heavy metals (HMs) such as Lead (Pb) have played a vital role in increasing the sediments of the Australian bay's ecosystem. Several meteorological parameters (i.e., minimum, maximum and average temperature (Tmin, Tmax and TavgoC), rainfall (Rn mm) and their interactions with the other batch HMs, are hypothesized to have high impact for the decision-making strategies to minimize the impacts of Pb. Three feature selection (FS) algorithms namely the Boruta method, genetic algorithm (GA) and extreme gradient boosting (XGBoost) were investigated to select the highly important predictors for Pb concentration in the coastal bay sediments of Australia. These FS algorithms were statistically evaluated using principal component analysis (PCA) Biplot along with the correlation metrics describing the statistical characteristics that exist in the input and output parameter space of the models. To ensure a high accuracy attained by the applied predictive artificial intelligence (AI) models i.e., XGBoost, support vector machine (SVM) and random forest (RF), an auto-hyper-parameter tuning process using a Grid-search approach was also implemented. Cu, Ni, Ce, and Fe were selected by all the three applied FS algorithms whereas the Tavg and Rn inputs remained the essential parameters identified by GA and Boruta. The order of the FS outcome was XGBoost > GA > Boruta based on the applied statistical examination and the PCA Biplot results and the order of applied AI predictive models was XGBoost-SVM > GA-SVM > Boruta-SVM, where the SVM model remained at the top performance among the other statistical metrics. Based on the Taylor diagram for model evaluation, the RF model was reflected only marginally different so overall, the proposed integrative AI model provided an evidence a robust and reliable predictive technique used for coastal sediment Pb prediction.
Striving to achieve the Sustainable Development Goals (SDGs), countries are increasingly embracing a sustainable financing mechanism via green bond financing. Green bonds have attracted the attention of the industrial sector and policymakers, however, the impact of green bond financing on environmental and social sustainability has not been confirmed. There is no empirical evidence on how this financial product can contribute to achieving the goals set out in Agenda 2030. In this study, we empirically analyze the impact of green bond financing on environmental and social sustainability by considering the S&P 500 Global Green Bond Index and S&P 500 Environmental and Social Responsibility Index, from October 1, 2010 to 31st July 2020 using a combination of Quantile-on-Quantile Regression and Wavelet Multiscale Decomposition approaches. Our results reveal that green financing mechanisms might have gradual negative transformational impacts on environmental and social responsibility. Furthermore, we attempt to design a policy framework to address the relevant SDG objectives.