Greenhouse Emissions Research Articles

Triboelectric nanogenerators for blue energy harvesting in simulated wave conditions

Energy harvesting gadgets gained momentum with the proposal of triboelectric nanogenerators (TENGs). They can be used to efficiently convert otherwise wasted surface and underwater waves into electricity to power marine-based devices and equipment without greenhouse emissions. In this study, a commercial buoy is modified and a pendulum-like TENG is proposed for ocean wave energy harvesting. Polyurethane (PU) foam and poly(dimethyl siloxane) (PDMS) were utilized as triboelectric couple. From the measurements taken under simulated real case situations in a wave flume, maximum voltage and current values were recorded as 145 V and 8.9 µA, respectively, with a maximum power output of 1.1 W/m2. No performance loss was observed even after 1200 cycles of stability measurement. While presenting the results from the simulated environment, this study highlights and reveals the importance of measurements taken in real situations for understanding the effectiveness of the wave energy harvesters.

The Experimental Investigation of a Diesel Engine Using Ternary Blends of Algae Biodiesel, Ethanol and Diesel Fuels

Algae are regarded among the most favorable feedstocks for producing sustainable biodiesel and utilizing it in diesel engines. Additionally, ethanol addition further enhanced the performance and reduce greenhouse emission. Algae biodiesel was produced, and an experimental study was performed to understand the diesel engine performance and emissions characteristics using different fuel blends by varying the ratio of diesel, biodiesel, and ethanol, such as D100, B10, B20, B5E5, and B10E10 (where number shows the percentage of the respective fuel). It was found that brake thermal efficiency was reduced by 0.49% and 1.29% for B10 and B20 blends, while the addition of ethanol enhanced the BTE by 0.37% and 1.60% respectively. However, SFC increases by 1.45%, 2.14%, 3.18%, and 3.78% respectively for B10, B20, B5E5, and B10E10 with respect to diesel fuel. Combustion characteristics were increased with increasing concentration of biodiesel and ethanol addition. Particulate matter, smoke emissions, and CO2 were slightly reduced by 3%, 4%, and 0.18%, respectively, while NOx emissions were increased by 26% for B10 blended fuel as compared to diesel fuel. Further addition of 5% (volume) ethanol in B5 fuel reduced particulate matter, smoke emissions, and CO2 emissions by 26.4%, 22%, and 23% respectively. Among the tested blends (B10, B20, B5E5, and B10E10), ethanol blended fuel was found to be more promising due to its higher combustion and performance and to have lower emissions to diesel fuel.

HVAC control in buildings using neural network

The energy consumption in buildings become the largest part of energy consumption worldwide, accounting for 40% of total global energy consumption and one third of the green house emission (Ahmed et al., Dec 2021). The optimization of building HVAC system require in many cases a thermodynamic model and mathematical solver which consumes a lot of hardware and time. However the data driven methods that presents 48% are often focusing on one type of HVAC systems (Grassi et al., May 2022). The real engineering problem is that the data driven methods are limited to specific systems and investigated for specific configurations. To overcome this problem, many machine learning solutions for optimized control data for HVAC are proposed. Those solutions are tested for real buildings in Germany. This new controller approach considers the effect of sequential inputs such as weather conditions, internal energy and time, and it consider a historical HVAC optimized data output. The investigation is done for CNN, LSTM, RNN-GRU, RNN-attention, and for each method we try to idealize the hyper-parameters and configurations. The goal of the study is to investigate the accuracy of machine learning routine on building optimization controlling and the ability to learn controlled and optimized data for different systems, to compare difference between generation of single output and multiple outputs, to decide the best inputs prepossessing, and to evaluate the extreme weather effects. This investigation proves that data control solution have limitation especially with extreme weather conditions but it can be improved by working on the pre-processing and different configurations.

Tracking spatiotemporal evolution of cementitious carbonation via Raman imaging

AbstractCarbonation of cementitious systems is an important phenomenon from both durability (rebar corrosion) and greenhouse emissions (CO2 sequestration) perspectives. Several analytical techniques are well‐established for measuring the kinetics of this dynamic phenomenon, ranging from measuring the pH change to quantifying the calcite content over time. However, the majority of these methods (with the exception of electron imaging) rely on bulk measurements which may miss the fine, microstructural changes that occur during carbonation. In this study, we investigate and evaluate the potential of Raman imaging to follow the spatiotemporal evolution of carbonation in cementitious pastes. By analysing mm‐scale maps (1 mm × 1 mm) at the micron‐scale resolution, we find that nearly ~40% of the sample surface was covered with calcite at the end of a 2‐week carbonation exposure. Simultaneously, the portlandite content declined from ~15% to 5% in the same period, suggesting portlandite consumption for the sake of calcite growth. Finally, the difference between the calcite growth rate and portlandite consumption rate strongly suggests simultaneous carbonation of CSH and ettringite—a finding that is in agreement with recent experimental investigations of carbonation kinetics. Overall, these new results on the spatiotemporal evolution of cementitious microstructure pave the way for utilizing Raman imaging for understanding dynamic phenomena in complex systems.

Circular Economy in the Food Chain: Production, Processing and Waste Management.

Food processing, from agricultural production to domestic consumption, is responsible for generating great amounts of waste per year, resulting in soil, water, and air pollution. These pollutants, together with the uses of petrochemical process inputs such as solvents, additives, or fuels, increase the food chain's environment impacts resulting in wasted resources. In response to this scenario, the circular economy (CE) theory is presented in literature as a liable alternative for the design of more sustainable production chains. In this context, this work was aimed at evaluating the literature's approach on the CE concept within the food processing and food waste management. The works show the centrality of "food waste" as a focus for the application of the CE. However, despite the relevance of management, reuse, or valuation of food waste, particularly due to its contribution to carbon footprint and decrease of food safety, studies have found other strategies for improvement of CE in the food chain. In this case, works in literature were allocated within the framework presented by the Ellen Macarthur Foundation called ReSOLVE, with proposals for modification of production chain to promote the CE. Among the proposals, one should highlight: modification of productive systems for mitigation of environmental impacts and greenhouse emissions, processes optimization for decreasing the use of natural resources and wastes, use of 4.0 Industry such as IoT, big data, or machine learning techniques for improvement of the whole supply chain, development of collaborative platforms for production and market, use of residues or co-products by design of intra- or inter-chain loops, and exchange of process or inputs with high environmental impacts for greener ones.

Adaptive Aquila Optimization Controlled Deep Convolutional Neural Network for Power Management in Supercapacitors/Battery of Electric Vehicles

Electric Vehicles (EVs) may be a viable solution to reduce the huge energy consumption and greenhouse emissions of global transportation. However, the cost and range of batteries are two major obstacles for EV. An efficient Power management system for EVs, which includes Supercapacitor (SC) and battery with an optimized converter, is proposed in this paper. An optimal Direct Current (DC)-DC Bi-directional Buck-Boost Converter (BBBC) with a Proportional Integral Derivative (PID) controller is used for the optimal flow of power from the energy source to the drive during EV acceleration. The regenerative braking energy is allowed to return through the same bidirectional converter and retained in the Hybrid Energy Storage System (HESS) during the deceleration mode. A novel optimization is attained in the converter controller circuit using a Deep Convolution Neural Network (DCNN) and Adaptive Aquila Optimization Algorithm (AAqOA). The proposed strategy is validated using the results compared to conventional algorithms. In particular, the settling time of the suggested AAqOA model is 55.44%, 96.94%, 97.03%, and 91.87% better than the extant PI, DA, SSA, and AOA methods, respectively.

Renewable energy, carbon capture & sequestration and hydrogen solutions as enabling technologies for reduced CO2 energy transition at a national level: an application to the 2030 Italian national energy scenarios

Globally, climate change, fossil fuel depletion and greenhouse emissions are fundamental problems requiring massive effort from the international scientific community to be addressed and solved. Following the Clean Energy for all Europeans Package (CEP) guidelines, the Italian Government has established challenging and tight objectives both on energy and climate matter to be targeted by 2030. Accordingly, research activities on different topics are carried out in Italy, looking at the installation of intermittent renewable energy systems (IRES), implementation of carbon capture and sequestration (CCS) on existing power plants and hydrogen technology and infrastructure penetration for accomplishing the end-users demands. The optimal integration of the above-mentioned technologies is one of the most effective weapons to address these objectives. The paper investigates different energy scenarios for meeting the Italian National Energy and Climate Plan (NECP) 2030 targets showing how the combined implementation of around +12 GW of IRES and +6 GW of electrolyzers compared to the national estimates simultaneously with the CCS of around 10 Mt of CO2 per year can reduce the CO2 emissions up to about 247 Mt/year. Thanks to the adoption of the well-established software platform EnergyPlan, the integration of IRES plants, CCS and hydrogen-based technologies have been explored, and the most successful results for concurrently reducing the impact of industrial, transport, residential and energy sectors and mitigating the greenhouse emissions substantially relies on the diversifications. Results show both the technical and economic convenience of a 2030 energy scenario which implements properly hydrogen, IRES and CCS penetration in the energy system, meeting the NECP 2030 targets and maintaining both the over-generation of the power plants below 5 TWh and the initial capital expenditure to be sustained for this scenario to occur below +80% compared to the 2019 energy scenario.

Hydrogen storage in gas reservoirs: A molecular modeling and experimental investigation

Hydrogen is one of the clean energy sources that can be used instead of fossil fuel sources to reduce greenhouse emissions. However, hydrogen supply intermittency significantly reduces the deployment and reliability of this energy resource. Therefore, this work investigates the underground storage of hydrogen in depleted gas reservoirs to avoid seasonal fluctuations in hydrogen supply and assure long-term energy security. The obtained results from molecular simulation (Density Functional Theory) revealed hydrogen is adsorbed physically on calcite (104) and silica (001) surfaces on different adsorption configurations. This conclusion is supported by low adsorption energies (−0.14 eV for calcite and −0.09 for silica) and by Bader charge analysis, which showed no indication of charge transfer. The experimental results illustrated that hydrogen has a very low adsorption affinity toward carbonate and sandstone rocks in the temperature range of 50–100 °C and pressure up to 20 bar. These results show the potential of depleted gas reservoirs to store hydrogen for s is useful in hydrogen recovery as no hydrogen will be adsorbed to the rock surface of conventional gas reservoirs.

LCA of Mixed Generation Systems in Singapore: Implications for National Policy Making

The decarbonization of electrical power generation systems is one of Singapore’s national political agendas to reduce national greenhouse emissions. LCA is applied to assess the trade-offs of national implementation of electricity generation from conventional fossil-fuel power plants, compared to low-carbon alternatives. The first aim of LCA is to quantify the emission inventory of national electrical generation within the geographical boundary of Singapore, and next to generate the potential environmental impacts of Global Warming Potential, Acidification, and Eutrophication. Various scenarios are tested for a projected diversity of fuel resource mixes considered for years 2030 and 2040 and a hypothetical scenario where 100% renewable energy is employed and imported as the nation transitions towards a low-carbon energy future. Further discussions on the additional LCA model indicators should be included for the potential of low-carbon hydrogen application.

Exploring the role of economic and institutional indicators for carbon and GHG emissions: policy-based analysis for OECD countries.

Most of the developed countries across the globe have targeted to attain sustainable economic growth. With this focus, the current study evaluated 29 OECD countries over the time period of 1990 to 2018 to analyze the influence of economic and environmental indicators, i.e., export diversification, institutional quality, macrocosmic variables on carbon dioxide, and greenhouse gas emissions. The current study used the quantile regression and generalized method of moments approach on the selected panel. Our comprehensive econometric approach allows us to reveal that export diversification negatively affects carbon emissions but promotes greenhouse gas emissions. Similarly, institutional quality, economic growth, financial development, and economic growth helps to reduce carbon emissions but increase greenhouse emissions. In comparison, trade openness exhibits a positive influence on carbon emissions but a negative on greenhouse gas emissions. Besides, urbanization is found one of the major reasons for environmental degradation. In light of empirical fact findings, this study commends some innovative policy insights for scholars, governors, and policymakers.

Machine Learning Approaches to Predict Electricity Production from Renewable Energy Sources

Bearing in mind European Green Deal assumptions regarding a significant reduction of green house emissions, electricity generation from Renewable Energy Sources (RES) is more and more important nowadays. Besides this, accurate and reliable electricity generation forecasts from RES are needed for capacity planning, scheduling, managing inertia and frequency response during contingency events. The recent three years have proved that Machine Learning (ML) models are a promising solution for forecasting electricity generation from RES. In this review, the 8-step methodology was used to find and analyze 262 relevant research articles from the Scopus database. Statistic analysis based on eight criteria (ML method used, renewable energy source involved, affiliation location, hybrid model proposed, short term prediction, author name, number of citations, and journal title) was shown. The results indicate that (1) Extreme Learning Machine and ensemble methods were the most popular methods used for electricity generation forecasting from RES in the last three years (2020–2022), (2) most of the research was carried out for wind systems, (3) the hybrid models accounted for about a third of the analyzed works, (4) most of the articles concerned short-term models, (5) the most researchers came from China, (6) and the journal which published the most papers in the analyzed field was Energies. Moreover, strengths, weaknesses, opportunities, and threats for the analyzed ML forecasting models were identified and presented in this paper.

Green-Resilient Supplier Selection and Order Allocation Under Disruption by Utilizing Conditional Value at Risk: Mixed Response Strategies

Two important decisions in supply chains and logistics systems design are the supplier selection and order allocation (SS&OA) problem and the vehicle routing problem (VRP). Supply disruption may reduce the capacity of suppliers, and the transportation network disruption may decrease the number of vehicles in the fleet and disrupt some routes. Also, increasing environmental regulations and environmental awareness makes companies pay more attention to green supply chain management (GSCM). In this paper, we integrate green and resilient supplier selection and order allocation decisions with vehicle routing decisions under disruption. We present a multiproduct two-stage risk-averse mixed-integer stochastic linear programming for the green and resilient supplier selection and order allocation integrated with vehicle routing (G&RSS&OA-V) problem. We consider resilient strategies before disruption, including multiple sourcing, supplier fortification, prepositioned inventory at the protected supplier, and contract with third-party logistics providers (3PLs). The objective function is to minimize the total mean-risk cost and the cost of greenhouse emissions. We use conditional value at risk (CVaR) as a risk measure to control the risk of worst-case cost. The most significant decisions of this model are the strategic decisions of determining the optimal suppliers and the operational decisions of vehicles routing under disruption simultaneously. Other decisions include determining which suppliers should be fortified, the amount to be transported to the hybrid manufacturing-distribution (HMD) center through the supplier or prepositioned emergency inventory, and the amount of lost sales. In order to validate the proposed model and its features, several numerical examples along with sensitivity analysis are performed by GAMS software, which shows the efficiency and application of the developed model, and some managerial insights are reported. The results of the sensitivity analysis show that as α increases from 0.1 to 0.9, the mean-CVaR objective function cost increases to 13.2%. As λ increases from 0.1 to 0.9, the mean-CVaR objective function cost increases to 35.6%. The increase of these two risk factors makes the proposed model more risk-averse. As the expected shortage cost increases by 150%, the mean-CVaR objective function cost increases to 36% while the amount of expected shortage decreases by 56%.

Reorganizing Students Acces in University Politehnica of Bucharest Campus and Influences on Car Traffic Externalities

Abstract Transport activities are regarded as one of the greatest contributors to greenhouse gas (GHG) emissions. Even though in other economic sectors the trend is to reduce the greenhouse emissions, the transportation field has been facing a continuous growth with negative effects on the environment. Due to its harmful potential for the inhabitants of large urban areas, a special attention is required as the land use individualities, transport network features and both spatial and temporal distribution of human activities often leads to traffic congestion. Universities represent an important pole of attraction because of their role as centers of teaching, research and employment and the mobility generated by them must be taken into account. This research aimed to both develop an analysis of greenhouse emissions in urban congested areas and present the influence of a new pedestrian bridge between University Politehnica of Bucharest campus and student dormitories. An economic analysis is conducted to emphasize that the new construction will lead to benefits in terms of the reduction of travel time, vehicle operating costs, the level of greenhouse gas emissions, etc.

ADDRESSING CLIMATE CHANGE ADAPTATION THROUGH HUMAN RIGHTS AND PROMOTION OF CLIMATE JUSTICE

Climate change is causing irreversible and widespread disruption that threatens the lives of many people. Undoubtedly, vulnerable nations which are less capable of adapting to the inimical consequences of climate change bear most of the burdens of climate injustices. Moreover, substantial inequities in resources and funding create a vast gap between developed and poor nations’ capabilities in adapting to climate change. Climate justice, a theory associated with justice and fairness for all nations and communities, has in its heart the protection of people’s rights. The significance of human rights in the matter of climate change gives rise to justice concerns in the international community, including the accountability of the developed countries to help the vulnerable nations in adapting to climate change. In this article, climate change will be evaluated from the viewpoint of least developed, developing and environmentally vulnerable nations. Furthermore, this article focuses on insights into climate justice in evaluating the usefulness of acknowledging the human rights claims which are certain to incur, particularly from climate change, through an adaptation-based framework. Though normatively, the developed countries are the highest emitters of greenhouse gases, human rights law obliges the states with the lowest emission of greenhouse gases to take adequate measures within the maximum resources available to minimize the climate change harms within its territory. This article explores the existing liabilities and accountabilities of states relating to social, economic and cultural rights in adapting to the adverse impact of climate change. It also highlights the obstacles to recognising such obligations, including the state’s unwillingness, global injustice and other limitations giving rise to political, economic, and justice concerns of employing human rights in an adaptation-based framework.

A comparative analysis of sustainable construction tools and their applicability to the housing sector in two countries; Burkina Faso and Sri Lanka.

The buildings and construction sector is responsible for nearly 40% of the total greenhouse emissions (GHG). Considering 50% of the building stock that will exist in 2050 is yet to be built and most of it will be devoted to housing; the sector is a determinant and transformative force to strengthen sustainability, reducing CO2 emissions and environmental degradation worldwide. Most of the increase in construction and housing is set to occur in developing countries and mainly in cities in Asia and Africa. This global picture places new housing programs in the rapidly urbanising regions as potential agents of sustainable transformation, with positive outcomes for both communities and the environment. Investing in sustainable housing has significant and real value in reducing emissions, confronting climate change, and generating better planned, inclusive, and sustainable cities. The holistic benefit achieved with the implementation of carbon neutral and carbon negative technologies is often scattered, and an integrated view of it would be a key tool to support the development of sustainable housing programmes. Considering that technologies to decarbonize and render the construction sector more sustainable have already been developed, there is a need to contrast their applicability to different countries and contexts in order to verify their functionality and identify gaps for improvement. The recent decade has witnessed a significant improvement at the global level with regards to the application of the concept of sustainability to the built environment, this being demonstrated by the multiple sustainability ratings and frameworks being developed to certify building performance. Their adoption has been critically important in most regions in the so-called Global North, where countries have started enforcing them at a normative level. While these tools’ accuracy and comprehensiveness could be disputed, their importance in promoting a systematic standardisation of the adoption of sustainability measures in the built environment is endorsed. Nevertheless, the diffusion of such tools and frameworks across rapidly urbanising middle and low-income countries has been so far extremely limited. There are myriad reasons why this is the case: tools based for high-income country contexts, their complexity, the need for accurate data and specific capacity for their adoption and diffusion, the lack of contextual relevance with regards to the specific market, culture and behavioural dynamics, and more. The following paper aims at demonstrating the value of shifting toward sustainable building practices by a comparative analysis of existing global tools and certifications and their applicability to low and middle-income countries undergoing a rapid urbanisation process. It proposes a three-phased multi-stakeholders methodology. The outcome of these three phases is combined, providing a more appropriate definition of effective and operative guidelines and tools for sustainable housing in rapidly urbanising middle and lowincome regions.

Non-Metal-Doped Porous Carbon Nitride Nanostructures for Photocatalytic Green Hydrogen Production.

Photocatalytic green hydrogen (H2) production through water electrolysis is deemed as green, efficient, and renewable fuel or energy carrier due to its great energy density and zero greenhouse emissions. However, developing efficient and low-cost noble-metal-free photocatalysts remains one of the daunting challenges in low-cost H2 production. Porous graphitic carbon nitride (gCN) nanostructures have drawn broad multidisciplinary attention as metal-free photocatalysts in the arena of H2 production and other environmental remediation. This is due to their impressive catalytic/photocatalytic properties (i.e., high surface area, narrow bandgap, and visible light absorption), unique physicochemical durability, tunable electronic properties, and feasibility to synthesize in high yield from inexpensive and earth-abundant resources. The physicochemical and photocatalytic properties of porous gCNs can be easily optimized via the integration of earth-abundant heteroatoms. Although there are various reviews on porous gCN-based photocatalysts for various applications, to the best of our knowledge, there are no reviews on heteroatom-doped porous gCN nanostructures for the photocatalytic H2 evolution reaction (HER). It is essential to provide timely updates in this research area to highlight the research related to fabrication of novel gCNs for large-scale applications and address the current barriers in this field. This review emphasizes a panorama of recent advances in the rational design of heteroatom (i.e., P, O, S, N, and B)-doped porous gCN nanostructures including mono, binary, and ternary dopants for photocatalytic HERs and their optimized parameters. This is in addition to H2 energy storage, non-metal configuration, HER fundamental, mechanism, and calculations. This review is expected to inspire a new research entryway to the fabrication of porous gCN-based photocatalysts with ameliorated activity and durability for practical H2 production.

Modeling piezoelectricity’s impact on environmental parameters with real-life applications

With a rapid increase in the global human population in recent years, the Earth is in dire need of alternative energy sources. The objective of this paper is to assess the feasibility of implementing piezoelectricity as a primary source of renewable energy. Specifically, this paper looks at its consequent impact if introduced in 3 fundamental areas of implementation: bus tires, train tracks and railway stations. Each of these areas provide input elements at scale to generate electricity and are simple to implement, maintain and measure through government support. This paper limits the data to the city of Cairo, Egypt. Being low cost as well, the three areas come close to an ideal situation from an economic standpoint. Predictive algorithms and existing data are used to evaluate the consequent change in global green-house emissions and other environmental parameters, had piezoelectricity been previously implemented. The results of the equations show that there would be improved environmental conditions - thus, a net positive impact - that switching to piezoelectricity would have had on the atmosphere and environment. The predictions stay true, provided that the efficiency of the piezoelectric crystal remains constant- an outcome which serves to boost output and lower greenhouse emissions

Influence of Injection Pressure on the Dual-Fuel Mode in CI Engines Fueled with Blends of Ethanol and Tamanu Biodiesel

The acceleration of global warming is primarily attributable to nonrenewable energy sources such as conventional fossil fuels. The primary source of energy for the automobile sector is petroleum products. Petroleum fuel is depleting daily, and its use produces a significant amount of greenhouse emissions. Biofuels would be a viable alternative to petroleum fuels, but a redesign of the engine would be required for complete substitution. The use of CNG in SI engines is not new, but it has not yet been implemented in CI engines. This is due to the fuel having a greater octane rating. The sole use of CNG in a CI engine results in knocking and excessive vibration. This study utilizes CNG under dual-fuel conditions when delivered through the intake manifold. In a dual-fuel mode, compressed natural gas (CNG) is utilized as the secondary fuel and a blend of 90% tamanu methyl ester and 10% ethanol (TMEE10) is used as the primary fuel. The injection pressure (IP) of the primary fuel changes between 200 and 240 bar, while the CNG induction rate is kept constant at 0.17 kg/h. The main combustion process is governed by the injection pressure of the pilot fuel. It could be affecting factors such as the vaporization characteristics of the fuel, the homogeneity of the mixture, and the ignition delay. Originally, tamanu methyl ester (TME) and diesel were used as base fuels in the investigation. As a result of its inherent oxygen content, TME emits more NOx than diesel. The addition of 10% ethanol to TME (TMEE10) marginally reduces NOx emissions in a CI mode because of its high latent heat of vaporization characteristics. Under peak load conditions, NOx emissions of TMEE10 are 6.2% lower than those of neat TME in the CI mode. Furthermore, the experiment was conducted using TMEE10 as the primary fuel and CNG as the secondary fuel. In the dual-fuel mode, the TMEE10 blend showed higher combustion, resulting in an increase in performance and a significant decrease in emission characteristics. As a result of the CNG’s high-energy value and rapid burning rate, the brake thermal efficiency (BTE) of TMEE10 improves to 29.09% compared to 27.09% for neat TME. In the dual-fuel mode of TMEE10 with 20.2% CNG energy sharing, the greatest reduction in fuel consumption was 2.9%. TMEE10 with CNG induction emits 7.8%, 12.5%, and 15.5% less HC, CO, and smoke, respectively, than TME operation.

Sustainable ammonia recovery from anaerobic digestion effluent through pretreating the feed by biomass ash

Ammonia recovery from anaerobic digestion (AD) effluent by vacuum membrane distillation (VMD) is a sustainable and low-carbon footprint strategy for nitrogen fertilizer production and waste treatment. However, due to the good buffering performance of AD effluent, lots of alkali chemicals are consumed to adjust the pH value, thereby increasing the treatment cost. To reduce the consumption of alkali chemicals, biomass ash (BA) is proposed as the alternative to replace the alkali chemical for pretreating the AD effluent before ammonia recovery in this study. Compared with the case using NaOH for pH adjustment, the ammonia separation factor increases by 40 % for BA addition case. Meanwhile, the 30-h VMD experiment for AD effluent treatment is conducted to testify to the stability. Additionally, the high germination index (GI) value (0.84) illustrates the potential of ammonia-removed AD effluent in agricultural applications. Furthermore, the greenhouse emission of ammonia recovery from AD effluent pretreated with BA is only 2.06 kg-CO2 eq/kg, which decreases by 51 % compared with that of the Haber-Bosch process. Finally, BA addition adopted for AD effluent pretreatment has economic superiority to NaOH addition. Inferably, using BA to pretreat the AD effluent can be acted as a high-efficacy and sustainable candidate strategy for ammonia recovery.

Asymmetric effect of agriculture value added on CO2 emission: Does globalization and energy consumption matter for pakistan

Globalization has resulted in several technical advancements, including the ability to connect people all over the world and drive the economies with higher agricultural output. With agricultural productivity expanding quickly, the negative impact of globalization on environmental degradation is being disregarded. Rapid agricultural expansion and globalization have resulted in significant increases in energy consumption and CO2 emissions. The primary purpose of this research is to assess the role of Pakistan’s massive agriculture industry in encouraging or discouraging CO2 emissions under Globalization scenario. Therefore, we applied Non-linear Autoregressive Distributive Lag Nonlinear Autoregressive Distributed Lag model from 1971 to 2021. Our results showed that in presence of globalization, agricultural production shows asymmetries in case of positive and negative shocks. A positive shock in Agricultural production increased the CO2 emissions while negative shock in agricultural production decreased CO2 emissions. Furthermore, GDP, energy consumption and economic globalization have positive association with economic globalization while on the other hand, surprisingly trade and urbanization in the presence of globalization have negative association with CO2 emissions. Environmental deterioration due to greenhouse emissions causes climatic variation in the economy and several mitigation strategies are required on sustainable basis in Pakistan. So, our study recommends that farmers of Pakistan should adopt organic farming this will help to reduce CO2 emissions.