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The present study is motivated by the need to decouple economic growth from environmental degradation given the new wave of chase for higher economic growth trajectories comes with its environmental cost implications, especially among developing blocs like the Emerging 7 (E7) countries. There is a consistent trade-off between economic growth versus environmental quality. Government apparatus are perpetually on the chase for low-carbon emission policies via the pursuit for green economy. To this end, this present study extends the conventional environmental Kuznets curve (EKC) argument by incorporating the role of institution in emerging industrialized economies (E7) and using second-generation panel analysis methods like mean group (MG), augmented mean group (AMG), common correlated effects mean group (CCEMG), and the Dumitrescu and Hurlin causality test for more robust estimates and inferences. To this end, we explore the long-run and causality relationship between economic growth, quadratic form of economic growth, institutional quality, trade flow, investment in energy sector, and financial development in an EKC environment. Empirical analysis established a long-run equilibrium relationship among the outlined variables over the study period. The long-run regression shows the presence of EKC in the E7. Thus, suggesting the preference for GDP growth over environmental quality at the earlier stage of growth curve. Interestingly, investment in energy, trade flow dynamics across the blocs, and financial development dampens the detrimental effect of environmental pollution as we observed negative relationship with the ecological footprint. On the contrary, quality of institution is weak as institutional quality increase (worsen) the quality of environment in the E7 economies. From a policy perspective, this current study proposed the need for more stringent environmental treaties and regulations and promotion of green economy without compromising economic growth. In the conclusion part of the study, more details and specifics about the policy blueprint are presented.

(Abstract) This paper proposes a new power line communication (PLC) architecture for monitoring and remote control of Distributed Generators (DG) and Energy Storage Systems (ESS) connected to low voltage distribution networks. The final aim is to improve the performance of the PLC link in terms of robustness and efficiency in devices addressing. The proposed solution is based on a concentrator, to be installed in secondary substation, and a new PLC bridge, to be linked both to inverters and interface protection systems of DGs or ESSs. In this way, a communication link is obtained between distribution system operator (DSO) and DG or ESS owners. The proposed system is able to provide advanced functions for the remote control of DGs and ESSs inverters, not only in terms of remote disconnection but also in terms of adjusting the inverter operating modes.

Electric vehicles (EVs) are considered a substitute for fossil-fueled vehicles due to rising fossil fuel prices and accompanying environmental concerns, and their use is predicted to increase dramatically shortly. However, the widespread use of EVs and their large-scale integration into the energy system will present several operational and technological hurdles. In the energy industry, an innovative solution known as the EVs smart parking lot (SPL) is introduced to handle EV charging and discharging electricity and energy supply challenges. This paper investigates social equity access and mobile charging stations (MCSs) for EVs, where the owner of MCSs is the EV parking lot. Accordingly, a new self-scheduling model for SPLs is presented in this paper that incorporates scheduling of the MCSs as temporary charging infrastructures while considering social equity access and optimizes SPL energy generation and storage schedule. The main objectives of this research are to (i) develop MCSs accessibility measures and quantify the equity impacts of MCSs locations by modeling prioritized demand based on several indices; (ii) determine the optimal set-points of SPL components (i.e., combined heat and power (CHP), photovoltaic system, electrical and heat-energy storage, and MCSs) to manage electrical peak demand and to maximize the economic benefits of SPLs. Results indicate that the proposed demand prioritization function model can meet the required EV charging demands for prioritized events, and the self-scheduling model for SPLs satisfies the charging demand of the EVs in the SPL location. Also, the social equity access to the EV charging stations is satisfied by analyzing the operation of MCSs around the prioritized demand of the prioritized events and social equity access indices.

Enhanced weathering (EW) is one of the most promising negative emissions technologies urgently needed to limit global warming to at least below 2 °C, a goal recently reaffirmed at the UN Global Climate Change conference (i.e., COP26). EW relies on the accelerated dissolution of crushed silicate rocks applied to soils and is considered a sustainable solution requiring limited technology. While EW has a high theoretical potential of sequestering CO2, research is still needed to provide accurate estimates of carbon (C) sequestration when applying different silicate materials across distinct climates and major soil types in combination with a variety of plants. Here we elaborate on fundamental advances that must be addressed before EW can be extensively adopted. These include identifying the most suitable environmental conditions, improving estimates of field dissolution rates and efficacy of CO2 removal, and identifying alternative sources of silicate materials to meet future EW demands. We conclude with considerations on the necessity of integrated modeling-experimental approaches to better coordinate future field experiments and measurements of CO2 removal, as well as on the importance of seamlessly coordinating EW with cropland and forest management.

The adoption of solar systems has witnessed a remarkable growth rate in recent years, driven by increasing awareness of renewable energy and declining costs of solar technology. Solar systems offer several advantages, including abundant energy source, reduced carbon emissions, and potential cost savings. However, they also face challenges such as intermittency, limited energy storage capacity, and grid integration issues. By incorporating hydrogen in smart grids, these drawbacks can be addressed as hydrogen can serve as a means of energy storage, allowing excess solar energy to be stored as hydrogen and utilized during periods of low solar generation. Hydrogen-incorporated smart grids thus provide a complementary solution to enhance the reliability, stability, and scalability of solar systems, facilitating their integration into the broader energy landscape. Consequently, this chapter aims to provide a comprehensive review of green hydrogen-integrated sector-coupled smart grids and presents prospects for future advancements. The background and significance of hydrogen integration within smart grid systems are established. The fundamentals of hydrogen integration, including its role as an energy carrier and its integration within smart grid systems, are explored. The concept of sector coupling in smart grids is examined, emphasizing the interconnection of different energy sectors and the importance of achieving energy system integration. Existing green hydrogen-incorporated smart grid projects are reviewed, and experiences gathered from successful implementations are analyzed. Technological advancements, such as emerging green hydrogen production and storage technologies, are discussed along with smart grid control and management systems for efficient green hydrogen utilization. Economic and environmental considerations are evaluated, encompassing cost analysis, evaluation of environmental impacts, and identification of economic incentives. Future prospects and research directions are explored, aiming to identify key challenges, address gaps, and highlight areas for further investigation. Overall, through this comprehensive review and exploration of future prospects, a deeper understanding of hydrogen-integrated sector-coupled smart grids and their potential for advancing sustainable energy systems can be achieved.

The technological development of preparing bio-oil from low-temperature hydrothermal conversion of agricultural and forestry waste has positive significance for alleviating the shortage of oil energy supply and reducing environmental pollution. This paper selects typical oxides (Al2O3, CeO2, MgO, SiO2, TiO2, and ZnO) as catalysts to set up a low-temperature (220 °C) hydrothermal conversion process of cotton stalk containing pretreatment processes including chopping. For moderate amplification estimation, lab-scale experimental data is used as a benchmark for calculation, and the functional unit for this study is set to be a 1 kg bio-oil product. The results suggest that the cerium dioxide-involved process with the highest bio-oil yield and highest synthetic consumption, and the silica-involved process with the lowest bio-oil yield, caused the highest environmental impact, resulting in greenhouse gas (GHG) emissions of 67.729 kg CO2e/kg and 60.001 kg CO2e/kg, respectively. It indicates that catalysts need to consider the balance between synthetic consumption and catalytic performance. Magnifying lab-scale data to an industrial scale using scale-up frameworks introduces a low model uncertainty, as the practical value had little effect on the overall evaluation results. However, existing equipment data should be used to reduce the uncertainty of the model itself. The environmental sustainability of bio-oil production by low-temperature hydrothermal liquefaction still needs to be improved, especially by catalyst recovery and bio-oil yield improvement.

The focus of this study is to analyse and compare the predictive capabilities of univariate and multivariate methods of forecasting the global horizontal irradiance (GHI) for an hour ahead. The forecasting problem is addressed using supervised machine learning methods. In order to simplify the model, a feature selection algorithm is used to identify the highly correlated features. The forecasting is performed by utilizing popular machine learning algorithms viz., random forest (RF), K-nearest neighbors regression (KNN), support vector machine (SVM) and artificial neural networks (ANN). The paper evaluates and contrasts the effectiveness of these models for this application. Additionally, the study examines how the forecasting models' performance varies throughout the year and across seasons.