Explore the vast range of titles available.

The aim of this study is to verify the reliability of NOx emissions measured using Smart Emissions Measurement System (SEMS) equipment in comparison with the NOx emissions measured using certified Portable Emissions Measurement System (PEMS) equipment. The SEMS equipment is simple system, and it is less expensive than the PEMS equipment, as it comprises an On-Board Diagnostics (OBD) signal from the test vehicle and a NOx sensor. The SEMS equipment based on low-cost sensors has an advantage of building big data, but there are insufficient previous studies comparing of NOx emissions with certified the PEMS equipment. Therefore, this study is important in verifying the suitability of the SEMS equipment by comparing the NOx emissions measured by the various test modes and RDE using the two types of equipment. To analyze the correlation between the PEMS and SEMS equipment, the advanced diesel vehicle was equipped with the two types of equipment to simultaneously measure NOx emissions. After installing the equipment on the test vehicle, it was conducted under various test modes in the laboratory and the Real Driving Emission (RDE) test to verify the correlation of NOx emissions measured by the SEMS equipment. The correlation analysis for the NOx emissions measured by the PEMS and SEMS equipment under various test conditions and the RDE test indicated that the slope of the NOx emissions was approximately equal to 1, and the coefficient of determination was 0.9 or higher. Based on these test results, it was concluded that NOx emissions measured by the PEMS and SEMS equipment are highly similar.

A portable emission measurement system (PEMS) is expensive and involves complex experimental processes for performing real driving emission (RDE) tests. An appropriate sensor-based emission measurement system (SEMS) must be developed to conduct the RDE test because SEMS has inexpensive and simple. To analyze the correlation between PEMS and SEMS, they are installed on six diesel vehicles, and a RDE test is performed on them. The applicability of the SEMS to the RDE test is estimated by comparing and analyzing real driving NOx emissions obtained from both the PEMS and SEMS. The results of laboratory and RDE tests confirm that the SEMS is applicable to RDE tests. Various correlation tests between the PEMS and SEMS show that the latter can be effectively applied to RDE tests; however, the NOx sensing limit must be improved.

To achieve low‐carbon sustainable energy development, new technologies such as Internet of Energy (IoE), intelligent systems and Internet of Things (IoT) as well as distributed energy generations via smart grids (SG) are gaining attention. The interoperability between intelligent energy systems, realised through the web, enables automatic consumption optimisation and increases network efficiency and intelligent management. IoE is an intriguing topic in close connection with the IoT, communication systems, SG and electrical mobility that contributes to energy efficiency to achieve zero‐carbon technologies and green environments. Furthermore, nowadays, the widespread growth and utilisation of processors for mining digital currency in homes and small warehouses are some other factors to be considered in terms of electric energy consumption and greenhouse gas emission. However, research on the use of the Internet for evaluating the misallocation of energy and the effect it can have on CO2 emissions is often neglected. In this study, the authors present a detailed overview regarding the evolution of SG in conjunction with the employment of IoE systems as well as the essential components of IoE for decarbonisation. Also, mathematical models with simulation are provided to evaluate the role of IoE in reducing CO2 emission. In this study, we present a detailed overview regarding the evolution of smart grids towards modern Internet energy systems. We present the essential components of Internet of Energy (IoE) for decarbonisation in the future of energy sector. Also, some models and equations are provided to evaluate the effects of IoE and the misallocation of energy on CO2 in different regions.

Agriculture is a pivotal player in the climate change narrative, contributing to greenhouse gas (GHG) emissions while offering potential mitigation solutions. This study delved into agriculture’s climate impact. It comprehensively analysed emissions from diverse agricultural sources, carbon sequestration possibilities, and the repercussions of agricultural emissions on climate and ecosystems. The study began by contextualising the historical and societal importance of agricultural GHG emissions within the broader climate change discourse. It then discussed into GHG emitted from agricultural activities, examining carbon dioxide, methane, and nitrous oxide emissions individually, including their sources and mitigation strategies. This research extended beyond emissions, scrutinising their effects on climate change and potential feedback loops in agricultural systems. It underscored the importance of considering both the positive and negative implications of emissions reduction policies in agriculture. In addition, the review explored various avenues for mitigating agricultural emissions and categorised them as sustainable agricultural practices, improved livestock management, and precision agriculture. Within each category, different subsections explain innovative methods and technologies that promise emissions reduction while enhancing agricultural sustainability. Furthermore, the study addressed carbon sequestration and removal in agriculture, focussing on soil carbon sequestration, afforestation, and reforestation. It highlighted agriculture’s potential not only to reduce emissions, but also to serve as a carbon reservoir, lowering overall GHG impact. The research also scrutinised the multifaceted nature of agriculture, examining the obstacles hindering mitigation strategies, including socioeconomic constraints and regulatory hurdles. This study emphasises the need for equitable and accessible solutions, especially for smallholder farmers. It envisioned the future of agricultural emissions reduction, emphasising the advancements in measurement, climate-smart agricultural technologies, and cross-sectoral collaboration. It highlighted agriculture’s role in achieving sustainability and resilience amid a warming world, advocating collective efforts and innovative approaches. In summary, this comprehensive analysis recognised agriculture’s capacity to mitigate emissions while safeguarding food security, biodiversity, and sustainable development. It presents a compelling vision of agriculture as a driver of a sustainable and resilient future. Graphical abstract

Developing a low-cost wireless energy meter with power quality measurements for smart grid applications represents a significant advance in efficient and accurate electric energy monitoring. In increasingly complex and interconnected electric systems, this device will be essential for a wide range of applications, such as smart grids, by introducing a real-time energy monitoring system. In light of this, smart meters can offer greater opportunities for sustainable and efficient energy use and improve the utilization of energy sources, especially those that are nonrenewable. According to the 2020 International Energy Agency (IEA) report, nonrenewable energy sources represent 65% of the global supply chain. The smart meter developed in this work is based on the ESP32 microcontroller and easily accessible components since it includes a user-friendly development platform that offers a cost-effective solution while ensuring reliable performance. The main objective of developing the smart meters was to enhance the software and simplify the hardware. Unlike traditional meters that calculate electrical parameters by means of complex circuits in hardware, this project performed the calculations directly on the microcontroller. This procedure reduced the complexity of the hardware by simplifying the meter design. Owing to the high-performance processing capability of the microcontroller, efficient and accurate calculations of electrical parameters could be achieved without the need for additional circuits. This software-driven approach with simplified hardware led to benefits, such as reduced production costs, lower energy consumption, and a meter with improved accuracy, as well as updates on flexibility. Furthermore, the integrated wireless connectivity in the microcontroller enables the collected data to be transmitted to remote monitoring systems for later analysis. The innovative feature of this smart meter lies in the fact that it has readily available components, along with the ESP32 chip, which results in a low-cost smart meter with performance that is comparable to other meters available on the market. Moreover, it is has the capacity to incorporate IoT and artificial intelligence applications. The developed smart meter is cost effective and energy efficient, and offers benefits with regard to flexibility, and thus represents an innovative, efficient, and versatile solution for smart grid applications.

Depletion of fossil fuel deposits is the main current issue related to the world’s power generation. Renewable energy sources integrated with energy efficiency represent an effective solution. The electrification of end-use coupled with renewable power generation integration is considered as an important tool to achieve these tasks. However, the current electric power system does not currently have the suitable features to allow this change. Therefore, in the future, it has to allow two-way direction power flows, communication, and automated controls to fully manage the system and customers. The resulting system is defined as the smart grid. This article analyses the smart grid state of play within China, the US, and the EU, assessing the completion state of each smart grid technology and integrated asset. The analysis related to these countries presented here shows that the smart grid overall state of play in China, the US, and the EU are equal to 18%, 15%, and 13%, respectively, unveiling the need related to further efforts and investments in these countries for the full smart grid development.

This systematic review examines the sources of greenhouse gas (GHG) emissions in Africa, investigates mitigation strategies, and projects future emissions. It evaluates comprehensive publications on climate change, global warming, GHG emission sources, and mitigation measures in Africa from 2013 to 2022. Ethiopia and South Africa are prominent nations in this field of study, with South Africa being the major source of GHG emissions. Rising GHG emissions and climate change pose significant ecological problems, necessitating effective mitigation techniques. North Africa has a small negative balance compared to Sub-Saharan Africa. Key drivers of GHG emissions and climate change were Land Use (AFOLU) sectors, accounting for 56% of total emissions in 2016 where currently energy sector count 73% of GHGs in 2022. It leads to biodiversity loss, water scarcity, economic losses, species extinction, ecological changes, and coastal life threats. Africa is responsible for 2–3% of global GHG emissions in last decays and currently increased to 3.6–4%. Agroforestry systems, which combine agriculture and forestry, have proven effective in reducing emissions, with the agroforestry sector predicted to sequester up to 135 metric tonnes of CO2 over the next 20 years. Africa should employ climate-smart agriculture and agroforestry to prevent climate change, lower emissions, and promote a green economy. This aligns with the Paris Agreement's goals of lowering carbon emissions and improving food security, reduce poverty, and environmental damage. This review is intended to aid academics and planners prioritize GHG emissions sources and design effective mitigation measures using African countries' resources and ecosystems.

A Smart Community (SC) is an essential part of the Internet of Energy (IoE), which helps to integrate Electric Vehicles (EVs) and distributed renewable energy sources in a smart grid. As a result of the potential privacy and security challenges in the distributed energy system, it is becoming a great problem to optimally schedule EVs’ charging with different energy consumption patterns and perform reliable energy trading in the SC. In this paper, a blockchain-based privacy-preserving energy trading system for 5G-deployed SC is proposed. The proposed system is divided into two components: EVs and residential prosumers. In this system, a reputation-based distributed matching algorithm for EVs and a Reward-based Starvation Free Energy Allocation Policy (RSFEAP) for residential homes are presented. A short-term load forecasting model for EVs’ charging using multiple linear regression is proposed to plan and manage the intermittent charging behavior of EVs. In the proposed system, identity-based encryption and homomorphic encryption techniques are integrated to protect the privacy of transactions and users, respectively. The performance of the proposed system for EVs’ component is evaluated using convergence duration, forecasting accuracy, and executional and transactional costs as performance metrics. For the residential prosumers’ component, the performance is evaluated using reward index, type of transactions, energy contributed, average convergence time, and the number of iterations as performance metrics. The simulation results for EVs’ charging forecasting gives an accuracy of 99.25%. For the EVs matching algorithm, the proposed privacy-preserving algorithm converges faster than the bichromatic mutual nearest neighbor algorithm. For RSFEAP, the number of iterations for 50 prosumers is 8, which is smaller than the benchmark. Its convergence duration is also 10 times less than the benchmark scheme. Moreover, security and privacy analyses are presented. Finally, we carry out security vulnerability analysis of smart contracts to ensure that the proposed smart contracts are secure and bug-free against the common vulnerabilities’ attacks. The results show that the smart contracts are secure against both internal and external attacks.

Climate change and food security are two of humanity’s greatest challenges and are highly interlinked. On the one hand, climate change puts pressure on food security. On the other hand, farming significantly contributes to anthropogenic greenhouse gas emissions. This calls for climate-smart agriculture—agriculture that helps to mitigate and adapt to climate change. Climate-smart agriculture measures are diverse and include emission reductions, sink enhancements, and fossil fuel offsets for mitigation. Adaptation measures include technological advancements, adaptive farming practices, and financial management. Here, we review the potentials and trade-offs of climate-smart agricultural measures by producers and consumers. Our two main findings are as follows: (1) The benefits of measures are often site-dependent and differ according to agricultural practices (e.g., fertilizer use), environmental conditions (e.g., carbon sequestration potential), or the production and consumption of specific products (e.g., rice and meat). (2) Climate-smart agricultural measures on the supply side are likely to be insufficient or ineffective if not accompanied by changes in consumer behavior, as climate-smart agriculture will affect the supply of agricultural commodities and require changes on the demand side in response. Such linkages between demand and supply require simultaneous policy and market incentives. It, therefore, requires interdisciplinary cooperation to meet the twin challenge of climate change and food security. The link to consumer behavior is often neglected in research but regarded as an essential component of climate-smart agriculture. We argue for not solely focusing research and implementation on one-sided measures but designing good, site-specific combinations of both demand- and supply-side measures to use the potential of agriculture more effectively to mitigate and adapt to climate change.

This paper sheds light on the importance of evaluating climate justice concerns when forging climate-neutral strategies at the city level. Climate justice can be a useful policy lever to develop measures that promote simultaneously greenhouse gas emissions reductions and their social justice dimension, thus reducing the risk of adverse impacts. As a result, evaluating policymakers’ awareness of (i) recognition (ii) distributive (iii) procedural, and (iv) intergenerational issues about the transition to climate neutrality might help identify where to intervene to ensure that decisions towards more sustainable urban futures are born justly and equitably. This study uses data from the European Mission on 100 Climate Neutral and Smart Cities by 2030 and a principal component analysis to build an index of climate justice awareness. It then identifies control factors behind different levels of climate justice awareness. The empirical analysis suggests that the more cities are engaged in climate efforts, the more they implement these efforts considering also the social justice dimension. It also reveals that the geographical location and the relationship with higher levels of governance contribute to shape the heterogeneity in a just-considerate climate action by virtue of different governance structures, historical legacies, and economic, cultural, and political characteristics. Overall, the analysis unveils that the availability of governmental support in capacity building and financial advisory services, and the breadth of the city’s legal powers across different fields of action are positively related to justice awareness. Conversely, the perception of favourable geo-climatic conditions is negatively correlated. These relationships can be read as assistance needs that cities perceive in their pathway to just climate neutrality and highlight where future efforts in research and policy-making should focus in the following years to pave the way to a just transition.