Acceleration of urbanization and industrialization has resulted in the drastic rise of waste generation with majority of them being biowaste. This constitutes a global challenge since conventional waste management methods (i.e., landfills) present environmental issues including greenhouse gases emissions, leachate formation and toxins release. A sustainable and effective approach to treat biowaste is through composting. Various aspects of composting such as compost quality, composting systems and compost pelletization are summarized in this paper. Common application of compost as fertilizer or soil amendment is presented with focus on the low adoption level of organic waste compost in reality. Rarely known, compost which is easily combustible can be utilized to generate electricity. With the analysis on critical approaches, this review aims to provide a comprehensive study on energy content of compost pellets, which has never been reviewed before. Environmental impacts and future prospects are also highlighted to provide further insights on application of this technology to close the loop of circular bioeconomy.
Wind power is one of the most popular sources of renewable energies with an ideal extractable value that is limited to 0.593 known as the Betz-Joukowsky limit. As the generated power of wind machines is proportional to cubic wind speed, therefore it is logical that a small increment in wind speed will result in significant growth in generated power. Shrouding a wind turbine is an ordinary way to exceed the Betz limit, which accelerates the wind flow through the rotor plane. Several layouts of shrouds are developed by researchers. Recently an innovative controllable duct is developed by the authors of this work that can vary the shrouding angle, so its performance is different in each opening angle. As a wind tunnel investigation is heavily time-consuming and has a high cost, therefore just four different opening angles have been assessed. In this work, the performance of the turbine was predicted using multiple linear regression and an artificial neural network in a wide range of duct opening angles. For the turbine power generation and its rotor angular speed in different wind velocities and duct opening angles, regression and an ANN are suggested. The developed neural network model is found to possess better performance than the regression model for both turbine power curve and rotor speed estimation. This work revealed that in higher ranges of wind velocity, the turbine performance intensively will be a function of shrouding angle. This model can be used as a lookup table in controlling the turbines equipped with the proposed mechanism.
BRICS are among the rising nations which drive economic growth by excessive utilization of resources and resulting in environment degradation. Although there is bulk of research on environmental Kuznets curve (EKC), very limited studies explored the scope in context of tourism in BRICS countries. So this research is conducted to explore the association of tourism, renewable energy, and economic growth with carbon emissions by using annual data of BRICS countries from the year 1995 to 2018. By using the recent approach of method of moments quantile regression (MMQR), the finding shows that tourism has stronger significant negative effects from 10th to 40th quantile while the effects are insignificant at remaining quantiles. Furthermore, an inverted U-shape EKC curve is also apparent at all quantiles excluding 10th and 20th quantiles. For renewable energy, the results are found negatively significant across all quantiles (10th-90th) which claim that CO2 emission can be reduced by opting renewable sources. Hence, the empirical results of the current study provide insights for policymakers to consume renewable energy sources for the sustainable economic growth and solution of environmental problems.
Since the development of the service sector and renewable energy reduce fossil-based energy consumption which mitigates CO2 emissions and this nexus provides a better understanding of the environmental sustainability. Considering the substantially increasing contribution of service sector and tremendous potential for renewable energy in ASEAN5 countries, leaning forward from ASEAN's energy and growth nexus, this study examines the impact of service sector contribution and renewable energy on the environmental quality of ASEAN5 using annual data from 1990 to 2018. The results of the fully modified ordinary least squared, dynamic ordinary least squared, and canonical co-integrating regressions depicted that the service sectors of Thailand, the Philippines, and Singapore augment CO2 emissions; however, the service sectors of Malaysia and Indonesia could reduce CO2 emissions. The increasing share of renewable energy can enhance environmental quality, but its magnitude varies in ASEAN5 economies; non-renewable energy, population, and economic development deteriorate the environment. Our results confirm the existence of environmental Kuznets curve in all the ASEAN5; the Gregory-Hansen test confirmed that results are robust. Finally, the Granger causality designated that economic development and non-renewable energy have a significant causal relationship with CO2 emission of ASEAN5 countries. These findings suggest that the ASEAN5 economies need to optimize their economic structure for promoting sustainable development in the long run.Graphical abstract.
Green growth is an extension of traditional economic growth. Financial fragility and ICT penetration are important pillars of green growth sustainability. However, very limited studies have explored this association and provided conflicting results. Thus, our study intends to fill this vacuum by exploring the impact of financial fragility and ICT penetration on renewable energy consumption and green growth for the top five polluting economies over the period 1996-2020. In this study, financial fragility is measured by bank costs and bank non-performing loans. Panel ARDL technique is used to find out long-run and short-run results estimates. Financial fragility reduces renewable energy consumption and green growth in the long run. However, internet penetration enhances renewable energy consumption and green growth in the long run. Our findings suggest imperative policy implications for the green economy.
A major challenge in renewable energy planning and integration with existing systems is the management of intermittence of the resources and customer demand uncertainties that are attributed to climates. In emerging distributed grids, state-of-the-art optimization techniques were used for cost and reliability objectives. In the existing literature, power dispatch and demand side management schemes were implemented for various techno-economic objectives. In renewable energy-based distributed grids, power dispatch is strategic to system operations. However, demand side management is preferred, as it allows more options for customer participation and active management of energy in buildings. Moreover, the demand side management can simply follow supplies. This paper investigates the implications of demand side management as it affects planning and operations in renewable energy-based distributed grids. Integration of demand side management in customer-oriented plans such as the time-of-use and real-time-pricing on residential and commercial demands is conceptualised to ensure effective customer participation which maintains the valued comforts. Moreover, the optimised tariff integrated demand side management implementations based on the utility-initiated demand response programmes are envisaged to offset conflicting objectives of the economy and customer comforts within residential and commercial demands and are also viewed as a step towards efficient management of energy in buildings.
The objective of the study is to extend the existing literature by investigating the effects of foreign direct investment, gross domestic products and per capita and energy diversification on the nitrogen oxide emissions in Brazil, Russia, India, China and South Africa (BRICS) by using annual data during the period 1992-2019. As per our knowledge, the present study is a first of its kind to examine the impact of a new energy diversification index, based on Herfindahl-Hirschman framework on pollution. This study has adopted a new quantile regression augmented method of moments, which is capable of producing the total impacts of the independent variables across the entire distribution of nitrogen oxides emissions. The findings suggest that an increase in foreign direct investment leads to a decrease in nitrogen oxides emissions at the aggregate level and in both manufacturing and service sectors. We observe that foreign direct investment leads to an increase in nitrogen oxides emissions in the agricultural sector in most of the quantiles. Diversification towards renewable energy causes a decrease in nitrogen oxides emissions in most quantiles at aggregate level, agricultural and manufacturing sectors, whilst diversification leads to an increase in nitrogen oxides emissions in the service sector. The findings also suggest that GDP per capita leads to an increase in NOx emissions in all the quantiles. The study suggests the policy to use and attract more clean energy through foreign direct investment for towards the achievement of sustainable development.
Schedule overrun is one of the greatest hindrances to construction project performance, thereby making schedule management an integral part of construction project management. The aim of this study is to examine how the effect of sustainable energy management (SEM) and sustainable waste management (SWM) on schedule performance (SP) is mediated by technological complexity (TC) and moderated by project size (PS). Data were obtained by means of a questionnaire survey of 168 completed construction projects in Nigeria. The partial least squares structural equation modelling (PLS-SEM) technique was adopted in analysing the collected data. The results show that TC partially mediates the relationship between SWM and SP, while TC fully mediates the relationship between SEM and SP. Also, the findings of the study indicate that the negative effect of SWM on SP will be greater with large PS. This study contributes to previous studies in the area of schedule management, by providing empirical proof to explain the means through which SWM and SEM could lead to SP through TC, and how this would vary depending on PS. The study proffers ways for contractors to improve the SP of their projects considering PS and TC.
Digital finance is an innovative financial model of great significance for sustainable economic growth. By constructing indicators of sustainable economic growth, we explore the impact of digital finance on sustainable economic growth using the fixed effect model, mediating effect model, threshold regression model, and dynamic spatial Dubin model. The study finds that digital finance can drive sustainable economic growth, and the robustness and endogenous treatment results strongly verify this. Digital finance promotes sustainable growth mainly through technological innovation. In addition, with technological innovation and the development of renewable energy, there is a significant nonlinear relationship between digital finance and sustainable economic growth. Finally, the spatial spillover effect results show that digital finance's impact on sustainable economic growth has a positive effect, whether it is a direct effect or an indirect effect. This article provides possible ideas for digital finance to promote sustainable economic growth.
This study employs dynamic panel data for 34 Sub Saharan Africa (SSA) countries for the period 1984-2016 to estimate the effects of renewable energy on environmental quality measured by three indicators, namely, per capita CO2 emissions, energy intensity (EI) and Aggregate National Savings (ANS). The study leveraged a battery of second-generation econometric tests and estimation and causality methods to obtain the coefficients between the regressed and the regressors. Results reveal that use of renewable energy reduces CO2 emissions and energy intensity while it enhances ANS. Economic growth still seems to be expensive for the region as it stimulates CO2 emissions. However, it has a positive effect on ANS. As expected, fossil fuels exacerbate CO2 emissions and energy intensity. FDI is found to be detrimental for the environment of SSA region with its positive significant coefficient on CO2 emissions. Financial development is reported to reduce CO2 emissions. Some causal links between variables are also noted.
In this paper, the suitability and performance of ANFIS (adaptive neuro-fuzzy inference system), ANFIS-PSO (particle swarm optimization), ANFIS-GA (genetic algorithm) and ANFIS-DE (differential evolution) has been investigated for the prediction of monthly and weekly wind power density (WPD) of four different locations named Mersing, Kuala Terengganu, Pulau Langkawi and Bayan Lepas all in Malaysia. For this aim, standalone ANFIS, ANFIS-PSO, ANFIS-GA and ANFIS-DE prediction algorithm are developed in MATLAB platform. The performance of the proposed hybrid ANFIS models is determined by computing different statistical parameters such as mean absolute bias error (MABE), mean absolute percentage error (MAPE), root mean square error (RMSE) and coefficient of determination (R2). The results obtained from ANFIS-PSO and ANFIS-GA enjoy higher performance and accuracy than other models, and they can be suggested for practical application to predict monthly and weekly mean wind power density. Besides, the capability of the proposed hybrid ANFIS models is examined to predict the wind data for the locations where measured wind data are not available, and the results are compared with the measured wind data from nearby stations.
The study estimates the long-run dynamics of a cleaner environment in promoting the gross domestic product of E7 and G7 countries. The recent study intends to estimate the climate change mitigation factor for a cleaner environment with the GDP of E7 countries and G7 countries from 2010 to 2018. For long-run estimation, second-generation panel data techniques including augmented Dickey-Fuller (ADF), Phillip-Peron technique and fully modified ordinary least square (FMOLS) techniques are applied to draw the long-run inference. The results of the study are robust with VECM technique. The outcomes of the study revealed that climate change mitigation indicators significantly affect the GDP of G7 countries than that of E7 countries. The GDP of both E7 and G7 countries is found depleting due to less clean environment. However, green financing techniques helps to clean the environment and reinforce the confidence of policymakers on the elevation of green economic growth in G7 and E7 countries. Furthermore, study results shown that a 1% rise in green financing index improves the environmental quality by 0.375% in G7 countries, while it purifies 0.3920% environment in E7 countries. There is a need to reduce environmental pollution, shift energy generation sources towards alternative, innovative and green sources.The study also provides different policy implications for the stakeholders guiding to actively promote financial hedging for green financing. So that climate change and envoirnmental pollution reduction could be achieved effectively. The novelty of the study lies in study framework.
A key objective of renewable energy development in the USA is to reduce CO2 emissions by decreasing reliance on fossil fuels in the coming decades. Using quantile-on-quantile regressions, this research examines the relationship between disaggregated sources of renewable energy (biomass, biofuel, geothermal, hydroelectric, solar, wind, wood, and waste) and CO2 emissions in the USA during the period from 1995 to 2017. Our findings support the deployment of various types of renewables in combating CO2 emissions for each quantile. In particular, a negative effect of renewable energy consumption on CO2 emissions is observed for the lower quantiles in almost all types of renewables. The effect of all the renewable energy sources taken together is significant for the lower and upper quantiles of the provisional distribution of CO2 emissions. The effect of renewable energy becomes stronger and more significant in the middle quantiles, where a pronounced causal effect of return and volatility is detected for the lower and upper middle quantiles. At the same time, heterogeneity in the findings across various types of renewable energy sources reveals differences in the relative importance of each type within the energy sector taken as a whole. Future US initiatives in renewable energy deployment at both the federal and the state levels should take into consideration the relative importance of each type, so as to maximize the efficacy of renewable energy policies in combating CO2 emissions.
Economic integration in the form of Belt and Road Initiative project opens many opportunities and hazards, especially of the participating nations' environment. The current study attempted to empirically test the economic and energy usage (renewable and non-renewable) impact on some selected countries of belt and road projects. For this purpose, the panel data set of twenty-four emerging economies of belt and road projects was selected from 1995 to 2014. The autoregressive distributed lags technique of econometric applied to determine the effect of renewable and non-renewable energy, GDP and GDP2 for EKC, and gross fixed capital formation on carbon emission in the selected countries of Belt and Road Initiative project. The outcomes of this study confirm the existence of EKC in these underlined countries. Here, fossil fuel-based energy consumption is a source of environmental degradation, while renewable and clean energy usage can help sustain environmental conditions without affecting economic growth progress. Capital fixed formation in these economies can enhance economic growth and help to sustainable environmental conditions in the belt and road countries. Thus, based on these empirical outcomes, this study suggests economic and financial assistance in green renewable energy sources and clean technological innovation to enhance economic benefits of Belt and Road Initiative project without compromising the environmental conditions of the region.
In Renewable Energy (RE) integrated DC Microgrid (MG), the intermittency of power variation from RE sources can lead to power and voltage imbalances in the DC network and have an impact on the MG's operation in terms of reliability, power quality, and stability. In such case, a battery energy storage (BES) technology is widely used for mitigating power variation from the RE sources to get better voltage regulation and power balance in DC network. In this study, a BES based coordinated power management control strategy (PMCS) is proposed for the MG system to get effective utilization of RE sources while maintaining the MG's reliability and stability. For safe and effective utilization of BES, a battery management system (BMS) with inclusion of advanced BES control strategy is implemented. The BES control system with optimized FOPI controllers using hybrid (atom search optimization and particle swarm optimization (ASO-PSO)) optimization technique is proposed to get improved overall performance in terms of control response and voltage regulation in DC network under the random change in load profile and uncertain conditions of RE sources in real time.
Energy security and environmental measurements are incomplete without renewable energy; therefore, there is a dire need to explore new energy sources. Hence, this study aimed to measure the wind power potential to generate renewable hydrogen (H2), including its production and supply cost. This study used first-order engineering model and net present value to measure the levelized cost of wind-generated renewable hydrogen by using the data source of the Pakistan Meteorological Department and State Bank of Pakistan. Results showed that the use of surplus wind and renewable hydrogen energy for green economic production is suggested as an innovative project option for large-scale hydrogen use. The key annual running expenses for hydrogen are electricity and storage costs, which have a significant impact on the costs of renewable hydrogen. The results also indicated that the project can potentially cut carbon dioxide (CO2) pollution by 139 million metric tons and raise revenue for wind power plants by US$2998.52 million. The renewable electrolyzer plants avoided CO2 at a rate of US$24.9-36.9/ton under baseload service, relative to US$44.3/ton for the benchmark. However, in the more practical mid-load situation, these plants have significant benefits. Further, the wind-generated renewable hydrogen delivers 6-11% larger annual rate of return than the standard CO2 catch plant due to their capacity to remain running and supply hydrogen to the consumer through periods of plentiful wind and heat. Also, the measured levelized output cost of hydrogen (LCOH) was US$6.22/kgH2, and for the PEC system, it was US$8.43/kgH2. Finally, it is a mutually agreed consensus among environmental scientists that the integration of renewable energy is the way forward to increase energy security and environmental performance by ensuring uninterrupted clean and green energy. This application has the potential to address Pakistan's urgent issues of large-scale surplus wind- and solar-generated energy, as well as rising energy demand.
Financial development is identified as one of the significant factors that affect energy consumption and has been widely discussed in the literature. However, the association between financial development and renewable energy consumption is still at its earlier stage and is limitedly explored. Therefore, the purpose of this study is to examine the non-linear association between financial development and renewable energy consumption in the top renewable energy consumption countries. The study utilized the newly introduced econometric technique panel smooth transition regression (PSTR) model with two regimes on annual panel data consisted of years 1997-2017. The result confirmed that all the financial development indicators increase renewable energy consumption but affect renewable energy consumption differently. Moreover, the economic growth and industrial structure showed a positive and significant association in both regimes, whereas the population showed a negative relationship with renewable energy consumption in a low growth regime but the association becomes positive in high growth regimes. The study suggested several policies for the top renewable consumption countries.
Power electronics and fuel cell technologies play an important role in the field of renewable energy. The demand for fuel cells will increase as fuel cells become the main power source for portable applications. In this application, a high-efficiency converter is an essential requirement and a key parameter of the overall system. This is because the size, cost, efficiency, and reliability of the overall system for portable applications primarily depend on the converter. Therefore, the selection of an appropriate converter topology is an important and fundamental aspect of designing a fuel cell system for portable applications as the converter alone plays a major role in determining the overall performance of the system. This paper presents a review of power electronics applications in fuel cell systems, which include various topology combinations of DC converters and AC inverters and which are primarily used in fuel cell systems for portable or stand-alone applications. This paper also reviews the switching techniques used in power conditioning for fuel cell systems. Finally, this paper addresses the current problem encountered with DC converters and AC inverter.
Smart grid (SG) application is being used nowadays to meet the demand of increasing power consumption. SG application is considered as a perfect solution for combining renewable energy resources and electrical grid by means of creating a bidirectional communication channel between the two systems. In this paper, three SG applications applicable to renewable energy system, namely, distribution automation (DA), distributed energy system-storage (DER) and electrical vehicle (EV), are investigated in order to study their suitability in Long Term Evolution (LTE) network. To compensate the weakness in the existing scheduling algorithms, a novel bandwidth estimation and allocation technique and a new scheduling algorithm are proposed. The technique allocates available network resources based on application's priority, whereas the algorithm makes scheduling decision based on dynamic weighting factors of multi-criteria to satisfy the demands (delay, past average throughput and instantaneous transmission rate) of quality of service. Finally, the simulation results demonstrate that the proposed mechanism achieves higher throughput, lower delay and lower packet loss rate for DA and DER as well as provide a degree of service for EV. In terms of fairness, the proposed algorithm shows 3%, 7 % and 9% better performance compared to exponential rule (EXP-Rule), modified-largest weighted delay first (M-LWDF) and exponential/PF (EXP/PF), respectively.