Electricity generation is a key contributor to global emissions of greenhouse gases (GHG), NOx and SO2 and their related environmental impact. A critical review of 167 case studies involving the life cycle assessment (LCA) of electricity generation based on hard coal, lignite, natural gas, oil, nuclear, biomass, hydroelectric, solar photovoltaic (PV) and wind was carried out to identify ranges of emission data for GHG, NOx and SO2 related to individual technologies. It was shown that GHG emissions could not be used as a single indicator to represent the environmental performance of a system or technology. Emission data were evaluated with respect to three life cycle phases (fuel provision, plant operation, and infrastructure). Direct emissions from plant operation represented the majority of the life cycle emissions for fossil fuel technologies, whereas fuel provision represented the largest contribution for biomass technologies (71% for GHG, 54% for NOx and 61% for SO2) and nuclear power (60% for GHG, 82% for NOx and 92% for SO2); infrastructures provided the highest impact for renewables. These data indicated that all three phases should be included for completeness and to avoid problem shifting. The most critical methodological aspects in relation to LCA studies were identified as follows: definition of the functional unit, the LCA method employed (e.g., IOA, PCA and hybrid), the emission allocation principle and/or system boundary expansion. The most important technological aspects were identified as follows: the energy recovery efficiency and the flue gas cleaning system for fossil fuel technologies; the electricity mix used during both the manufacturing and the construction phases for nuclear and renewable technologies; and the type, quality and origin of feedstock, as well as the amount and type of co-products, for biomass-based systems. This review demonstrates that the variability of existing LCA results for electricity generation can give rise to conflicting decisions regarding the environmental consequences of implementing new technologies.
Lian Farhadian, Samira Amiri Khoshkar Vandani, Hai-Feng Ji
Dielectric barrier discharge (DBD) plasma provides a solvent-free and energy-efficient approach for the in situ polymerization of styrene on cotton textiles. Traditional methods for polystyrene (PS) coating often require elevated temperatures, chemical initiators, or organic solvents, conditions that are incompatible with porous, heat-sensitive substrates such as cotton. In this work, we demonstrate that DBD plasma can initiate and sustain styrene polymerization directly on cotton fibers under ambient conditions. FT-IR spectroscopy confirms the consumption of the vinyl C=C bond and the formation of atactic, amorphous polystyrene. Thermogravimetric analysis indicates that the cotton coated with DBD polymerized PS exhibits enhanced thermal stability compared to cotton coated with commercial PS. Additionally, UV aging tests confirm that the plasma-deposited coating maintains its hydrophobicity after exposure to light. Together, these findings highlight DBD plasma as a sustainable and effective approach for producing hydrophobic, thermally robust, and UV-stable textile coatings without the need for solvents, initiators, or harsh processing conditions.
The Sustainable Development Goals (SDGs) represent a fundamental global commitment to addressing a wide range of socio-economic and environmental challenges. A key component of these goals is the commitment to ocean sustainability, encapsulated in the concept of the blue economy. The blue economy, emerging in an era characterized by intricate dynamics and openness to transformation, is influenced by various determinants. This study utilizes panel data analysis and the pooled least squares method to investigate the factors influencing the share of the blue economy in the archipelagic provinces of Indonesia from 2012 to 2021. With its vast maritime territory and numerous islands, Indonesia provides a highly relevant context for examining these dynamics. The empirical results indicate that information and communication technology (ICT), fisheries capture, and aquaculture production positively impact the blue economy’s share. Conversely, trade openness and electricity consumption exhibit a negative relationship with the blue economy’s share. Moreover, the analysis reveals that investment does not have a significant effect on the blue economy’s share. These findings underscore the critical importance of developing robust infrastructure and implementing stringent regulatory oversight on fishery product trade to enhance sustainable growth within the blue economy framework.
<p>We assigned the side-chain resonances of the Crimean–Congo hemorrhagic fever virus (CCHFV) glycoprotein n (Gn) cytosolic domain that is 69 amino acids long to complete the backbone resonances previously published by Estrada et al. (2011). The process was facilitated by three factors. First, sample preparation using cell-free protein synthesis (CFPS) was completed in less than 2 d and allowed for correct zinc finger formation by adding zinc ions to the reaction. Second, access to NMR platforms with standardized pulse sequences allowed for data acquisition in 18 d. Third, data analysis using the online platform NMRtist allowed sequential resonance assignments to be made in a day, and assignments were verified and finalized in less than a week. Our work thus provides an example of how NMR assignments, including side chains, of small and well-behaved proteins can be approached in a rapid routine, at protein concentrations of 150 <span class="inline-formula">µ</span>M.</p>
This paper introduces a novel carbon emission prediction method based on tracking control, leveraging historical CO2 emission prediction errors and feed-forward integration of electricity consumption data to enhance forecasting accuracy and minimize lag. Comparative analysis with pre-trained models such as LSTM and ARDL using Python showcases the proposed method's substantial reduction in prediction errors compared to singular reliance on electricity data, while also significantly reducing computational time in contrast to LSTM models. The findings establish a valuable reference for policymakers and researchers in refining carbon emission prediction methodologies and formulating effective carbon reduction policies.
There is a large number of combined heat and power units in northern China, and due to the limit of heating demand, the operating mode of setting electricity by heat of combined heat and power units has seriously took over the consumption space of other energy, resulting in severe wind power curtailment and rationing situation in some areas, so this paper studies the deep peak regulation bidding strategy problem considering multiple uncertainties on virtual power plants, and established a two-staged optimization model of virtual power plant to maximize the net revenue, then introduced the Shapely value method with correction coefficient redistribute the peak regulation revenue. The simulation results showed that the two-stage bidding model can not only improve the market competitiveness of the virtual power plant, but also promote the consumption of renewable energy and reduce the market peak regulation service cost. Meanwhile, the improved apportion method can effectively guarantee the enthusiasm of all kinds of units to participate in the deep peak regulation market.