Energy Resources Research Articles

Navigating Climate Change and Sustainability Solutions: Nigeria's Role in International Environmental Law

Over the past three decades, the world has witnessed multidimensional impacts of climate change, thus emphasizing the critical urgency for climate action, and promoting sustainability. Africa is reported to disproportionately suffer the consequences of climate change. These consequences such as flooding; droughts and rising temperatures require the adoption of an environmental foreign policy to foster environmental sustainability and cross-sectoral climate resilience. As Africa’s most populous nation and second-largest oil producer significant to global oil supply, Nigeria occupies a unique and distinct position in the global climate discourse. However, despite credibly being a signatory to international bodies such as the United Nations Framework Convention on Climate Change (UNFCCC), Nigeria needs an effective environmental foreign policy to attain the globally desired sustainability and climate resilience. Therefore, this paper delves into Nigeria’s commitment to international environmental law of key international agreements, such as the Paris Agreement, in response to the threatening climate change crisis. The paper recognizes Nigeria as a leading contributor to greenhouse gas emissions in Africa, driving it to develop some national environmental policies that support renewable energy production and sustainability. However, most of the policies seem ineffective. This is because the country needs help with challenges ranging from political interference, corruption, unrealistic emission targets, overreliance on fossil fuels, and other socioeconomic factors. The study highlights the potential of Nigeria, the giant of Africa, in leading regional climate negotiations by localizing international environmental foreign policy and developing a strengthened legal framework to guide the African continent to sustainable development. The perspective of this paper is to encourage Nigeria to meet its sustainability objectives while fulfilling its international environmental obligations by exploring its abundant renewable energy resources such as solar, wind, and hydropower

Testing the Environmental Kuznets Curve Hypothesis within the Context of Industrial Development through NARDL Model: The Case of France

Energy is one of the necessary and vital factors for the economic development of countries and the increase in living standards. The limited availability of conventional energy resources, in parallel with the rapid increase in energy consumption due to industrialization activities and technological developments, has encouraged countries to change their energy production policies and use alternative energy sources. For this purpose, in the study, the relationship and direction of the relationship between industrial production, CO2 emissions, energy use and economic growth in France are examined with the carrying out of the ADF, PP, KPSS unit root and the NARDL test for the period 1990–2023. The analysis findings reveal that the variables are cointegrated in the long-run and a 1% unit raise in energy consumption would trigger CO2 emissions by 0.46%. The increase in energy consumption in the long term affects both negatively and positively in the long term. While a 1% increase in energy consumption reduces carbon emissions by 0.48%, a 1% decrease causes a 0.35 decrease in carbon emissions. The positive increase in economic growth and the negative increase in energy consumption trigger environmental pollution by causing an increase in carbon dioxide emissions. There is interdependence between economic activities and environmental pollution. Policy makers need to adopt a strategic approach in solving the rapid industrialization phenomenon and the accompanying magnitude of energy consumption, the pressure on natural resources and the environmental pollution problems resulting from them. In this respect, it is an inevitable necessity for countries to continue multilateral negotiations in which the public sector, business world, academia and non-governmental organizations will take part in solving environmental problems.

Consensus-Based Model Predictive Control for Active Power and Voltage Regulation in Active Distribution Networks

In this paper, a consensus-based model predictive control (Cb-MPC) scheme is proposed to control the active power and voltage at all nodes in grid-connected active distribution networks (ADNs) with multiple distributed energy resources (DERs). The proposed design methodology is based on a multiple-input multiple-output (MIMO) model of an ADN which accounts for both the internal and external interactions among the control loops of the DERs. To achieve the control objective, each DER unit is equipped with a controller–observer system. In particular, the observer implements the consensus algorithm to estimate the collective system state by exchanging data only with its neighbors. The scope of the controller is to solve the MPC optimal problem based on its collective state estimate, and, due to the presence of an integral term in the control action, it is robust against any unknown scenarios of the ADN, which are represented by uncertainty in the model parameters. The results of numerical simulations validate the effectiveness of the proposed method in the presence of unknown changes in the operating conditions of the ADN and of communication using a sample and hold function.

A new multi-objective-stochastic framework for reconfiguration and wind energy resource allocation in distribution network incorporating improved dandelion optimizer and uncertainty

Improving the reliability and power quality of unbalanced distribution networks is crucial for ensuring consistent and reliable electricity supply. In this research, multi-objective optimization of unbalanced distribution networks reconfiguration integrated with wind turbine allocation (MORWTA) is implemented considering uncertainties of networks load, and also wind power incorporating a stochastic framework. The multi-objective function is defined by the minimization of power loss, voltage sag (VS), total harmonic distortion (THD), voltage unbalance (VU), energy not-supplied (ENS), system average interruption frequency index (SAIFI), system average interruption duration index (SAIDI), and momentary average interruption frequency (MAIFI). A new improved dandelion optimizer (IDO) with adaptive inertia weight is recommended to counteract premature convergence to identify decision variables, including the optimal network configuration through opened switches and the best location and size of wind turbines in the networks. The stochastic problem is modeled using the 2m + 1 point estimate method (PEM) combined with K-means clustering, taking into account the mentioned uncertainties. The proposed stochastic methodology is implemented on three modified 33-bus, and unbalanced 25-, and 37-bus distribution networks. The results demonstrated that the MORWTA enhanced all study objectives in comparison to the base networks. The results also demonstrated that the IDO had superior capability to solve the deterministic- and stochastic-MORWTA in comparison to the conventional DO, grey wolf optimizer (GWO), particle swarm optimization (PSO), and arithmetic optimization algorithm (AOA) in terms of achieving greater objective value. Moreover, the results demonstrated that when the stochastic-MORWTA model is considered, the power loss, VS, THD, VU, ENS, SAIFI, SAIDI, and MAIFI are increased by 18.35%, 9.07%, 10.43%, 12.46%, 11.90%, 9.28%, 12.16% and 14.36%, respectively for 25-bus network, and also these objectives are increased by 12.21%, 10.64%, 12.37%, 9.82%, 14.30%, 12.65%, 12.63% and 13.89%, respectively for 37-bus network compared to the deterministic-MORWTA model, which is related to the defined uncertainty patterns.

Energy-Efficient Algorithm for Data Path Selection in High-Density Wireless Sensor Networks

Relevance. Increasing the number of sensor devices per unit area consequently reduces the physical distance between the devices in the sensor network. Such networks are usually deployed over a large area and the sensor device that wants to transmit a data packet is located far away from the base station. In such a case, the source device is challenged to choose a transmission path that consumes the least amount of energy resources and satisfies the delivery time requirements. The objective of this study is to develop and validate the effectiveness of an empirical algorithm for selecting a transmission path that reduces the energy consumption of high-density wireless sensor networks. Methods of system analysis, analytical modeling, geometry and probability theories are used. Solution. It is assumed that the sensor network is deployed in a limited area and is a set of devices that are connected to each other informationally and energetically. When building data transmission routes, any sensor devices can be used as repeaters. At the same time, the increase in the number of repeaters leads to an increase in the time of data delivery. Novelty. It is assumed that the sensor network is deployed in a limited area and is a set of devices that are connected to each other informationally and energetically. Any sensor devices may be used as repeaters when constructing data transmission routes.Significance (theoretical). Dependences of power consumption level on various system parameters affecting the processes of functioning of high-density wireless sensor networks have been obtained. Significance (practical). The proposed empirical algorithm for selecting a rational data transmission route in a wireless sensor network allows us to determine, among all alternatives, the route to the coordinator that requires the least power. The effectiveness of the proposed empirical power-saving algorithm is confirmed by simulation modeling.

An Evolutionary Game Model of Market Participants and Government in Carbon Trading Markets with Virtual Power Plant Strategies

The utilization of conventional energy sources commonly leads to heightened energy consumption and the generation of specific forms of environmental pollution. As an innovative power management and dispatch system, virtual power plants (VPPs) have the potential to significantly enhance the flexibility and stability of power systems, while supporting carbon reduction targets by integrating distributed energy resources (DERs), energy management systems (EMSs), and energy storage systems (ESSs), which have attracted much attention in the power industry in recent years. Consequently, it can effectively address the variability and management challenges introduced by renewable energy. Furthermore, optimizing power market dispatch and user-side power management plays a pivotal role in promoting the transition of the energy industry towards sustainable development. The current study highlights the unresolved issue of strategic decision-making among market participants, such as energy companies, generation companies, and power distribution companies, despite the potentially significant benefits of VPPs. These entities must carefully evaluate the costs and benefits associated with adopting a VPP. Additionally, governments face the complex task of assessing the feasibility and effectiveness of providing subsidies to incentivize VPP adoption. Previous research has not adequately explored the long-term evolution of these decisions in a dynamic market environment, leading to a lack of adequate understanding of optimal strategies for market participants and regulators. This paper addresses this critical research gap by introducing an innovative bilateral evolutionary game model that integrates VPP and carbon trading markets. By utilizing the model, simulation experiments are carried out to compare different strategic decisions and analyze the stability and long-term evolution of these strategies. Research findings indicate that the adoption of VPP technology by market participants, in conjunction with government policies, results in an average 90% increase in market participants’ earnings, while government revenues see a 35% rise. This approach provides an alternative method for understanding the dynamic interactions between market participants and government policy, offering both theoretical and practical insights. The findings significantly contribute to the literature by proposing a robust framework for integrating VPPs into electricity markets, while offering valuable guidance to policymakers and market participants in developing effective strategies to support the sustainable energy transition. The application of this model has not only enhanced the understanding of market dynamics in theory, but also provided quantitative support for strategic decisions under different market conditions in practice.

Analisis Preferensi Bahan Bakar Minyak Subsidi dan Non Subsidi Di Purwokerto

The need for fuel oil for the transportation sector increases every year, while non-renewable energy reserves are increasingly depleting. Subsidized fuel oil is seen as not on target, the heavy burden of fuel oil subsidies limits fiscal space so that APBN allocations for other development sectors are limited. The increase in fuel prices is one of the considerations in choosing fuel. This research aims to analyze the factors that influence fuel oil preferences in North Purwokerto. The sample in this study amounted to 96 respondents who were calculated using the Lemeshow formula. This research uses logistic regression analysis. The results of the logistic regression analysis show that vehicle age, engine capacity, income, gender, and volume of fuel use have a significant effect on preferences for subsidized and non-subsidized fuel oil in North Purwokerto. Meanwhile, type of vehicle, number of family dependents, type of work, regional origin and social status do not have a significant effect on preferences for subsidized and non-subsidized fuel oil in North Purwokerto. The implication of this research is the need to increase consumer knowledge about the use of fuel oil that is adjusted to vehicle conditions. The government can make policies so that subsidized fuel can be received by people who are entitled to need it.

Mega-scale solar-wind complementarity assessment for large-scale hydrogen production and storage (H2PS) in Algeria: A techno-economic analysis

Green hydrogen (GH2) is produced using renewable energy resources (RERs) such as solar photovoltaic (PV) and wind energy. However, relying solely on a single source, H2 production systems may encounter challenges due to the intermittent nature, time-of-day variability, and seasonal changes associated with these energies. This paper addresses the assessment of mega-scale solar-wind complementarity and the economic viability of large-scale H2 production and storage in Algeria, considering various climatic conditions. A techno-economic feasibility analysis is conducted by examining various factors, including resource complementarity, electricity and hydrogen productivity, solar and wind contributions, capacity factors (CFs), total net present cost (TNPC), the levelized cost of hydrogen (LCOH) production, and storage (LCOS). The findings reveal that among the studied locations (Tamanrasset, M'sila, Hassi R'mel, and Adrar), Tindouf ranked first in H2 production, with the highest amount at 11,267 kg and the lowest LCOH at 2.999 $/kg. Wind energy accounted for 69% (3,846,922 kWh) of the total (5,572,571 kWh), while solar energy contributed 31% (1,738,171 kWh). Economically, the LCOH-TNPC for the various locations ranged from 2.99 $/kg-77,042,696 $ to 3.70 $/kg-99,000,488 $. The total electricity-hydrogen generated is estimated at 25,840.251 MWh-529,395 kg. Wind power makes the major contribution, accounting for 63%–70%, compared to the PV system's share, ranging from 31% to 37%. Compared to relying on a single source, leveraging solar-wind complementarity presents opportunities for more consistent energy utilization and reliable hydrogen production. The obtained results are essential for decision-makers and policymakers in Algeria, indicating that the country can successfully achieve energy diversification and transition for future sustainability, and become a major player in the international hydrogen market.

METHODOLOGICAL APPROACH TO ASSESSING THE IMPACT OF ENERGY EFFICIENCY ON THE FINANCIAL PERFORMANCE OF A GAS COMPANY

Currently, the use of energy-saving technologies that reduce costs and improve the production process is the most important factor in the development of the gas industry. The relevance of the research topic is due to the fact that in order to resolve issues related to reducing energy costs in the gas sector of the Russian economy, optimal use of non-renewable energy resources, and reducing technological losses, an integrated approach to assessing energy efficiency is required, as well as a methodology for assessing the impact of energy efficiency indicators on the financial performance of a company. The purpose of this article is to develop a methodological approach to assessing the impact of the level of energy efficiency on the financial performance of a gas company. The research methodology is based on correlation and regression analysis tools. As part of the study, a system of energy efficiency indicators for a gas company's activities has been formed and, using correlation and regression analysis tools, a methodology for assessing the impact of the level of energy efficiency on the financial performance of a gas company is proposed. An algorithm for correlation and regression analysis is presented, which makes it possible to formalize a step-by-step assessment of the impact of the level of energy efficiency on the financial performance of a gas company. The proposed methodological approach allows us to identify correlation-regression dependencies between the company’s key performance indicators (sales revenue and net profit) and the proposed energy efficiency indicators, determine the most significant indicators that affect the company’s performance indicators and determine the equation for the relationship between the company’s performance and energy efficiency indicators. Testing of the proposed approach using the example of the gas company PJSC Gazprom made it possible to quantify how changes in the energy intensity of gas production and the energy intensity of gas transportation will affect changes in sales revenue and net profit of the company. It has been established that the energy efficiency indicator such as the specific energy intensity of gas transportation has the greatest impact on the financial performance of the company. Regression equations have been established to describe the nature of the relationship between energy efficiency indicators and sales revenue and net profit of a gas company.

6G edge-networks and multi-UAV knowledge fusion for urban autonomous vehicles

The advent of 6G wireless networks has the potential to unlock diverse applications of scalable autonomy. By advantageously coupling the individual and aggregated attributes of diverse multi-UAV fleets, a range of high-value applications such as logistics, enhanced disaster response, urban navigation, and surveillance can be significantly improved. However, enabling effective communication for knowledge fusion necessitates the intrinsic optimization of performance metrics like energy consumption, resource allocation, latency, and computational overheads to enhance autonomous efficiency. Furthermore, designing robust security features is essential to safeguarding privacy, control, and operational integrity. This paper explores a novel collaborative knowledge-sharing (KS) framework that leverages 6G and edge-computing capabilities to facilitate the cooperative training of decentralized machine learning models among multiple UAVs, without the need to transmit raw data. This framework aims to enhance the learning experience and operational efficiency of autonomous vehicles. The DECKS (distributed edge-based collaborative knowledge-sharing) architecture enables Federated Learning (FL) within UAV networks, allowing local models to be trained and shared among neighboring UAVs for creating global models. This promotes intelligent knowledge aggregation without a central entity, enhancing collaborative capabilities among autonomous vehicles. The DECKS architecture efficiently extracts and distributes collaborative shared experience to ground vehicles through edge and direct inference, reducing energy consumption, latency, and computational overhead. Our simulation analysis demonstrates that the DECKS architecture has the potential to reduce energy consumption by 70% in sensorless vehicles and improve autonomous vehicle learning performance by 15% compared to centralized approaches in a distributed environment. This improvement is achieved by comparing the efficiency of systems with and without aggregated knowledge, as well as with a centralized system.

Social life cycle assessment of green hydrogen production: Evaluating a projected Portuguese industrial production plant

The increase in industrial production and energy consumption has led to excessive exploitation of non-renewable resources, resulting in serious environmental problems such as greenhouse gas emissions. In response, there's a growing investment in renewable energies such as hydroelectric, wind, and solar power. However, these sources are unable to fully meet demand, leading to imbalances between consumption and production. An emerging solution to this challenge is green hydrogen, produced from clean sources, reducing dependence on fossil fuels and mitigating greenhouse gas emissions. The S-LCA methodology presented in the UNEP/SETAC Guidelines for the Social Life Cycle Assessment is applied to the production of green hydrogen via the electrolytic separation of water using a proton exchange electrolyser. The process involves the extraction and processing of raw materials from the electrolyser, BOP and reverse osmosis system, the manufacture of the systems and the production of green hydrogen. The data from each stage is inventoried and entered into the PSILCA v.3.1 and SHDB 2022FV5 databases, integrated into the SimaPro software version 9.3.0.2, enabling a complete analysis of the social impacts associated with the production of green hydrogen. The data was evaluated considering 4 stakeholder categories: workers, value chain actors, society and local community. The results indicate that the extraction and processing of raw materials for the electrolyser was the primary stage responsible for the social impacts in both databases. However, the electrolyser manufacturing stage was the main contributor to the indicators “weekly working hours per employee” and “union density” in the PSILCA database. Nafion® and Iridium were identified as the major contributors among components in both databases. The study highlights the significant role played by countries like China and South Africa in social impacts, particularly in the extraction and processing of raw materials. Despite this, Portugal emerged as the largest contributor to five out of fourteen indicators in the PSILCA database, while its contributions in the SHDB database were less than 7 %. Moreover, a comparison between the two databases revealed that PSILCA exhibited a greater distribution of results across various stages, components, and countries assessed, whereas SHDB showed more centralized results. The observed discrepancies between the results obtained from different databases can be attributed to three main factors: the input-output database utilized in each S-LCA tool, the assumed risk levels for each indicator, and the equivalence between indicators and subcategories. This exploratory study offers valuable insights for guiding strategic decisions regarding the social component of sustainability, providing a detailed understanding of the social impacts associated with the specific case of green hydrogen production in a planned hub in Portugal.

Assessment of grid electricity systems using the life-cycle carbon-emission model

The previous study developed a life-cycle carbon emissions (LCCE) algorithm in MS Excel. Despite improvements, a comprehensive approach is needed to conduct LCCE inventory analysis using current methods. This study diverges from existing research by assessing life-cycle carbon emissions inventory data of power generation and transmission systems on studied grids, considering component lifespans, recycling pollutants, and retirement rates. The life-cycle carbon emissions inventory analysis results improve understanding of power system environmental performance, aligning with stakeholders' objectives. This study aims to enhance the environmental performance of electric power systems in Kenya, Rwanda, and Tanzania by evaluating the life-cycle carbon emissions of power generation and transmission within their national grids. The selected grids are the right participants for the study, of non-renewable and renewable grid electricity generation mixes, due to their different environmental features, potential power trade, upcoming grid interconnection, and power transmission practises at various scales. The study applied a life cycle assessment method and simulated the learning patterns using RStudio. The data (emission factors and activity) has been collected from the reports (scientific and technical) and national utility actors. The presented results show that only Kenyan generation and transmission systems have the lifetime decarbonization performance relationship between renewable energy sources dominated power systems and non-renewable energy sources dominated power systems. A major challenge of this study has been the scarcity of primary data, leading to reliance on some secondary and external sources. Therefore, future research should consider the use of more internal and primary data sources, and the use of the most current data, including new technologies adopted from cradle-to-grave of the systems. This study's findings inform better system designs, policies, and plans for improved environmental performance in electrical power systems.

Energy management of the residential smart microgrid with optimal planning of the energy resources and demand side

The study specifically aimed to model the optimal operation of these appliances based on their usage patterns, rather than relying on the capacity of demand flexibility in demand response (DR) and energy pricing. The modeling operation of the appliances is done using two-layer energy optimization. In this optimization, energy consumption by appliances is reshaped via DR and load shifting in first-layer optimization. Then, minimizing the consumption costs and consumers' discomfort in the second layer is formulated with consideration of the optimized consumption from the first layer. The lp-metric method is employed to solve the proposed optimization in the GAMS software. Finally, the efficiency of the two-layer optimization is confirmed using testing proposed case studies in the numerical simulation.

Sustainable hybrid energy system’s reliability optimization by solving RRAP-CM with integration of metaheuristic approaches

PurposeThis research introduces an innovation strategy aimed at bolstering the reliability of a renewable energy resource, which is hybrid energy systems, through the application of a metaheuristic algorithm. The growing need for sustainable energy solutions underscores the importance of integrating various energy sources effectively. Concentrating on the intermittent characteristics of renewable sources, this study seeks to create a highly reliable hybrid energy system by combining photovoltaic (PV) and wind power.Design/methodology/approachTo obtain efficient renewable energy resources, system designers aim to enhance the system’s reliability. Generally, for this purpose, the reliability redundancy allocation problem (RRAP) method is utilized. The authors have also introduced a new methodology, named Reliability Redundancy Allocation Problem with Component Mixing (RRAP-CM), for optimizing systems’ reliability. This method incorporates heterogeneous components to create a nonlinear mixed-integer mathematical model, classified as NP-hard problems. We employ specially crafted metaheuristic algorithms as optimization strategies to address these challenges and boost the overall system performance.FindingsThe study introduces six newly designed metaheuristic algorithms. Solve the optimization problem. When comparing results between the traditional RRAP method and the innovative RRAP-CM method, enhanced reliability is achieved through the blending of diverse components. The use of metaheuristic algorithms proves advantageous in identifying optimal configurations, ensuring resource efficiency and maximizing energy output in a hybrid energy system.Research limitations/implicationsThe study’s findings have significant social implications because they contribute to the renewable energy field. The proposed methodologies offer a flexible and reliable mechanism for enhancing the efficiency of hybrid energy systems. By addressing the intermittent nature of renewable sources, this research promotes the design of highly reliable sustainable energy solutions, potentially influencing global efforts towards a more environmentally friendly and reliable energy landscape.Practical implicationsThe research provides practical insights by delivering a comprehensive analysis of a hybrid energy system incorporating both PV and wind components. Also, the use of metaheuristic algorithms aids in identifying optimal configurations, promoting resource efficiency and maximizing reliability. These practical insights contribute to advancing sustainable energy solutions and designing efficient, reliable hybrid energy systems.Originality/valueThis work is original as it combines the RRAP-CM methodology with six new robust metaheuristics, involving the integration of diverse components to enhance system reliability. The formulation of a nonlinear mixed-integer mathematical model adds complexity, categorizing it as an NP-hard problem. We have developed six new metaheuristic algorithms. Designed specifically for optimization in hybrid energy systems, this further highlights the uniqueness of this approach to research.

Sustainable energy harvesting from medical waste: Utilizing discarded ointment tubes in triboelectric nanogenerators

Medical waste management is a common global issue that causes environmental pollution and poses significant health risks. The present study introduces a sustainable approach to addressing this issue by utilizing discarded medical ointment tubes to fabricate Triboelectric Nanogenerators (TENGs) for energy harvesting applications. The discarded metallic ointment tubes with epoxy resin coating inside simplify the TENG fabrication. The fabricated metallic ointment tube-based TENG produces a maximum output voltage of ∼ 405 V and a current of ∼ 82 µA, with a power density of 658 mW/m2. Further, TENG was utilized to power 480 LEDs and portable electronic devices with the help of an energy management system. The present work highlights the potential of transforming medical waste into sustainable energy resources.

DEVELOPMENT OF SCIENTIFIC FOUNDATIONS AND METHOD OF AUTONOMOUS POWER AND GAS SUPPLY OF REMOTE CONSUMERS FROM GAS DISTRIBUTION NETWORKS

The development and implementation of independent power supply systems with a high degree of autonomy is becoming an urgent task for the development of remote and inaccessible regions of the country, both due to the lack of energy capacities and accessible networks in newly developed areas, and the high cost of allocating separate power lines for remote and compact settlements. The increase in the scale of central networks is associated with high capital investments and long project implementation time, which creates inconvenience for consumers and the development of small and medium-sized enterprises. At the same time, unlike power supply, gasification issues, which are actively being resolved in all regions of the country, make it possible to connect new consumers of natural gas several times faster and cheaper, which is due to the high manufacturability and speed of construction of polyethylene gas pipelines of distribution networks, provided that gas pipelines pass in the region. In addition to lower capital and operating costs, the process of transporting energy resources through pipelines in the form of a primary source — gas — is characterized by significantly lower losses compared to water and electricity. At the same time, network gas in combination with trigeneration technologies and cryogenic hydrocarbon storage can be used not only as boiler fuel, but also as motor fuel, a source for generating electricity, heat and even cold with a replenished autonomous reserve. Thus, as an alternative solution to traditional power systems, it is proposed to use hybrid power systems — systems based on trigeneration plants with gas piston installations, turbine and screw machines, as well as cryogenic gas storage facilities that can provide the population with electricity, heat, cold and motor fuel. The article analyzed the state and development of the fuel and energy complex in Russia, conducted a study of the needs for heat, gas and electricity for an average compact settlement with small and medium-sized enterprises. Analysis and selection of equipment parameters for uninterrupted autonomous power supply of the economy was carried out.

Green investment and its transformative impact on energy use in agriculture: A holistic approach towards carbon dioxide emissions

The extensive use of non-renewable energy sources in agriculture and associated unsustainable practices have contributed greatly to climate change issues. Africa's lack of green initiatives has exacerbated environmental pollution in the last decade. In that regard, this study investigates the transformative impact of green investment on carbon emissions in agriculture fueled by dependency on non-renewable energy use in the sector. It utilizes advanced econometric techniques, including dynamic ordinary least squares, fully modified ordinary least squares, and the causality tests to conduct a rigorous analysis. The findings reveal a concerning rise in agricultural pollution via crop and livestock production. However, green investment is found to have a positive impact on carbon dioxide emissions, hence a potent tool for significantly reducing carbon emissions. The causality analysis unveils a bidirectional causal relationship between green investment and agricultural pollution and a unidirectional causality between green investment and carbon emissions, shedding light on the complex interplay between these variables. Also, non-renewable energy is found to increase carbon emissions in the region. The outcomes of this research contribute valuable insights and lay a foundation for future research and policymakers to enhance the future of agricultural research and sustainability. It also offers a roadmap for developing effective policies that balance the immediate needs of the agricultural sector with the imperative of long-term environmental sustainability. Finally, it has a global significance on the relevance of green initiatives to boost sustainable agriculture and reduce pollution.

A Novel Transformer-CNN Approach for Predicting Soil Properties from LUCAS Vis-NIR Spectral Data

Soil, a non-renewable resource, requires continuous monitoring to prevent degradation and support sustainable agriculture. Visible-near-infrared (Vis-NIR) spectroscopy is a rapid and cost-effective method for predicting soil properties. While traditional machine learning methods are commonly used for modeling Vis-NIR spectral data, large datasets may benefit more from advanced deep learning techniques. In this study, based on the large soil spectral library LUCAS, we aimed to enhance regression model performance in soil property estimation by combining Transformer and convolutional neural network (CNN) techniques to predict 11 soil properties (clay, silt, pH in CaCl2, pH in H2O, CEC, OC, CaCO3, N, P, and K). The Transformer-CNN model accurately predicted most soil properties, outperforming other methods (partial least squares regression (PLSR), random forest regression (RFR), support vector machine regression (SVR), Long Short-Term Memory (LSTM), ResNet18) with a 10–24 percentage point improvement in the coefficient of determination (R2). The Transformer-CNN model excelled in predicting pH in CaCl2, pH in H2O, OC, CaCO3, and N (R2 = 0.94–0.96, RPD > 3) and performed well for clay, sand, CEC, P, and K (R2 = 0.77–0.85, 2 < RPD < 3). This study demonstrates the potential of Transformer-CNN in enhancing soil property prediction, although future work should aim to optimize computational efficiency and explore a wider range of applications to ensure its utility in different agricultural settings.

The Development of Metal-Free Porous Organic Polymers for Sustainable Carbon Dioxide Photoreduction.

A viable tactic to effectively address the climate crisis is the production of renewable fuels via photocatalytic reactions using solar energy and available resources like carbon dioxide (CO2) and water. Organic polymer material-based photocatalytic materials are thought to be one way to convert solar energy into valuable chemicals and other solar fuels. The use of porous organic polymers (POPs) for CO2 fixation and capture and sequestration to produce beneficial compounds to reduce global warming is still receiving a lot of interest. Visible light-responsive organic photopolymers that are functionally designed and include a large number of heteroatoms and an extended π-conjugation allow for the generation of photogenerated charge carriers, improved absorption of visible light, increased charge separation, and decreased charge recombination during photocatalysis. Due to their rigid structure, high surface area, flexible pore size, permanent porosity, and adaptability of the backbone for the intended purpose, POPs have drawn more and more attention. These qualities have been shown to be highly advantageous for numerous sustainable applications. POPs may be broadly categorized as crystalline or amorphous according to how much long-range order they possess. In terms of performance, conducting POPs outperform inorganic semiconductors and typical organic dyes. They are light-harvesting materials with remarkable optical characteristics, photostability, cheap cost, and low cytotoxicity. Through cocatalyst loading and morphological tweaking, this review presents optimization options for POPs preparation techniques. We provide an analysis of the ways in which the preparative techniques will affect the materials' physicochemical characteristics and, consequently, their catalytic activity. An inventory of experimental methods is provided for characterizing POPs' optical, morphological, electrochemical, and catalytic characteristics. The focus of this review is to thoroughly investigate the photochemistry of these polymeric organic photocatalysts with an emphasis on understanding the processes of internal charge generation and transport within POPs. The review covers several types of amorphous POP materials, including those based on conjugated microporous polymers (CMPs), inherent microporosity polymers, hyper-crosslinked polymers, and porous aromatic frameworks. Additionally, common synthetic approaches for these materials are briefly discussed.

The role of environmental tax in guiding global climate policies to mitigate climate changes in European region

AbstractThe purpose of this study is to perform a practical inquiry into the influence of environmental tax laws on the execution of climate‐related financial policies. Our research will assess the effectiveness of environmental tax measures in 23 European countries from 2011 to 2020. The panel‐corrected standard error (PCSE) model and the feasible generalized least squares (FGLS) model are used in the empirical examination of the link between environmental tax laws and the implementation of climate‐related financial measures. This study is based on panel data with cross‐sectional dependence. The results of our estimation highlight the need to improve policy effectiveness by using all four ecological tax indicators. These include total environmental tax revenue, energy tax revenue, pollution and resource tax revenue, and transportation tax revenue. Furthermore, we provide actual evidence clarifying the process by which the implementation of environmental tax policies improves the efficacy of climate‐related financial policies in the short term as well as the long term. According to the findings, a third of environmental tax policy indicators have a long‐term effect on the implementation of climate‐related financial measures, with no short‐term effects seen. Our findings are critical for economists and policy‐makers who support the environmental tax as an effective tool to promote a country's climate policy implementations.