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Planning for the future of water and energy supply systems in urban areas requires a thorough assessment of associated risks. In this study, monthly water and energy demand data from 2011 to 2022 in an arid city was used to predict the corresponding demands from 2023 to 2032 using the seasonal auto-regressive integrated moving average (SARIMA) method. The aim is to estimate future water and energy supply risks both individually and jointly, using cumulative distribution functions (CDFs) derived from historical data. The main focus is to calculate the combined risk of water and energy, referred to as the water–energy nexus (WEN) risk. Based on the interdependent relationship between water and energy, the Copula function was utilized to model the bivariate distribution between these two variables. Pearson correlation analysis indicated a strong correlation between water and energy supplies. Among the distributions fitted to the data, the log-normal and gamma distributions were the best fit for water supply and energy supply systems, respectively, with the lowest Akaike information criterion (AIC) values. The Gumbel Copula, with a parameter of 1.66, was identified as the most suitable for modeling the joint distribution, yielding the lowest AIC value. The results indicate that the risks associated with energy supply, water supply, and their joint dependency could exceed 0.8% in the future, highlighting a potentially critical situation for the city. The trend analysis revealed that forecasted water and energy demands and their corresponding risks and the WEN risk are expected to have a significant upward trend in the future. Finally, local authorities need to explore alternative sources to supply water and energy in the future to address the ever-growing water and energy demands. [ABSTRACT FROM AUTHOR]

Although globalization is widely credited for unprecedented global economic growth, it also bears responsibility for climate change. Today, the primary emphasis of contemporary research is to find ways that help to reduce climate change adversaries without compromising the globalization-led economic development agenda. Thus, this study takes the case of 12 emerging economies selected based on their emissions profile and vulnerability to climate change. Covering annual data from 1972 to 2018, this study accounts for globalization's economic, social, and political aspects and empirically tests their relationship with carbon footprints and energy demand in the panel countries. The results confirm the long-run equilibrium relationship among all the underlying variables and find that economic globalization significantly contributes to energy demand and carbon emissions. However, such an effect can be overcome through social globalization. Whereas political globalization reduces the energy demand but spurs carbon emissions. Our results rule out the ant-globalization narrative and support pro-globalization chronicles for sustainable development policy agenda in emerging economies. We suggest that the emerging countries that are mainly facing the decision dilemma to choose between sustainable economic growth and a sustainable environment, are better off if they equally integrate socially and politically with the global economy. Moreover, the model is checked for robustness using FMOLS and found validated. It warrants that the results are appropriate for policy control use. [ABSTRACT FROM AUTHOR]

In an effort to mitigate anthropogenic climate impacts the U.S. has established ambitious Nationally Determined Contribution (NDC) targets, aiming to reduce greenhouse gas emissions by 50% before 2030 and achieving net-zero emissions by 2050. Enhanced rock weathering (ERW)—the artificial enhancement of chemical weathering of rocks to accelerate atmospheric CO2 capture—is now widely seen as a potentially promising carbon dioxide removal (CDR) strategy that could help to achieve U.S. climate goals. Grinding rocks to smaller particle size, which can help to facilitate more rapid and efficient CO2 removal, is the most energy-demanding and cost-intensive step in the ERW life cycle. As a result, accurate life cycle analysis of ERW requires regional constraints on the factors influencing the energetic and economic demands of feedstock grinding for ERW. Here, we perform a state-level geospatial analysis to quantify how carbon footprints, costs, and energy demands vary among regions of the coterminous U.S. in relation to particle size and regional electricity mix. We find that CO2 emissions from the grinding process are regionally variable but relatively small compared to the CDR potential of ERW, with national averages ranging between ~5–35 kgCO2 trock−1 for modal particle sizes between ~10–100  μm. The energy cost for feedstock grinding also varies regionally but is relatively small, with national average costs for grinding of roughly 0.95–5.81 $ trock−1 using grid mix power and 1.35–8.26 $ trock−1 (levelized) for solar PV for the same particle size range. Overall energy requirements for grinding are also modest, with the demand for grinding 1 Gt of feedstock representing less than 2% of annual national electricity supply. In addition, both cost and overall energy demand are projected to decline over time. These results suggest that incorporating feedstock grinding into ERW deployment at scale in the coterminous U.S. should generally have only modest impacts on lifecycle emissions, cost-effectiveness, and energy efficiency. [ABSTRACT FROM AUTHOR]

This paper demonstrates how artificial intelligence can be implemented in order to predict the energy needs of daily households using both multilinear regression (MLR) and single linear regression (SLR) methods. As a basic implementation, the SLR makes use of one input variable, which is the total amount of energy generated as an input. The MLR implementation involves multiple input variables being taken from various energy sources, including gas, coal, geothermal, wind, water, biomass, oil, etc. All of these variables are derived from detailed energy production data from the various energy sources. The purpose of this paper is to demonstrate that it is possible to analyze energy demand and supply directly together as a way to produce a more in-depth analysis. By analyzing energy production data from previous periods of time, a prediction of energy demand can be made. Compared to the SLR implementation, the MLR implementation is found to perform better because it is able to achieve a smaller error value. Furthermore, the forecasting pattern is carried out sequentially based on a periodic pattern, so this paper calls this method the recurrence multilinear regression (RMLR) method. This paper also creates a pre-clustering using the K-Means algorithm before the energy prediction to improve accuracy. Other models such as exponential GPR, sequential XGBoost, and seq2seq LSTM are used for comparison. The prediction results are evaluated by calculating the MAE, RMSE, MAPE, MAPA, and time execution for all models. The simulation results show that the fastest and best model that obtains the smallest error (3.4%) is the RMLR clustered using a weekly pattern period. [ABSTRACT FROM AUTHOR]

The Avoid–Shift–Improve framework has been used since its conception in the 1990s to help decision-makers prioritize action towards environmental sustainability in the transport sector. The core of the framework establishes a clear priority of action among the three main strategies that give it its name, thus highlighting the prominent role transport demand reduction should have within policy discussions. However, although its general formulation allows for a fruitful application to other sectors, the approach and its definitions have rarely been extended beyond transport. In particular, the energy sector could significantly benefit from an application of its methodology since the prioritization of energy demand reduction over energy efficiency would be in line with an optimized path towards decarbonization. This paper outlines a theoretical application of the A-S-I framework to the energy sector that allows the categorization of energy policies in terms of Avoid, Shift, or Improve strategies. Moreover, the paper presents an analysis of several energy policies databases to evaluate to what extent policies are addressing energy demand reduction, shift to less-carbon-intensive energy sources or energy efficiency. The results of the study show that most energy-related policies seem to support improving efficiency in current technology (18–33% of policies, depending on the database that is considered) and shifting towards low-carbon sources (28–48% of policies) more than reducing or altogether avoiding energy demand in the first place (6–22% of policies). Further research is recommended to strengthen the results, especially by evaluating the significance of each policy in terms of committed investment, as well as to understand the main factors that contribute to Avoid-type policies being under-represented in the energy sector. [ABSTRACT FROM AUTHOR]

This study analyses data from natural gas combination boilers across a 6-year timeframe, exploring how demand for space heating and hot water have both changed over time, and highlights the impact of factors such as external temperature and the UK energy price cap. The results show that there has been a significant decrease in annual space heating and hot water demand since 2021. Space heating typically contributes 88% of the total annual gas demand of the boilers, with hot water contributing the other 12%. For the same mean temperature across the fourth quarter (8.5 °C), 2018 had a daily mean energy demand of 50.4 kWh, whereas the 2022 value was 41.4 kWh. This 9.0 kWh (18%) difference of the daily mean for Q4 suggests a shift in consumer demand influenced by other factors such as the energy price cap. This analysis provides additional understanding of how consumer energy demand for heating continues to evolve and invites further studies to be completed on future trends of energy demand for both space heating and hot water. Here, we also highlight the benefit of considering space heating and hot water as separate demands, as this provides additional insights and is something the paper helps to advocate for. [ABSTRACT FROM AUTHOR]

Energy consumption demand has shown successive records during recent months, primarily associated with heat waves in almost all Brazilian states. The effects of climate change induced by global warming and the increasingly frequent occurrence of extreme events, mainly regarding temperature and precipitation, are associated with this increase in demand. In this sense, the impact of meteorological variables on load demand in some substations in the northeast and southeast of Brazil was analyzed, considering the historical series of energy injected into these substations. Fifteen substations were analyzed: three in the state of São Paulo, six in Bahia, three in Pernambuco, and three in Rio Grande do Norte. Initially, essential quality control was carried out on the energy injection data. The SAMeT data sets were used for the variable temperature, and Xavier was used for precipitation and relative humidity to obtain a homogeneous data series. Daily and monthly data were used for a detailed analysis of these variables in energy demand over the northeast and southeast regions of Brazil. Some regions were observed to be sensitive to the maximum temperature variable, while others were sensitive to the average temperature. On the other hand, few cases showed the highest correlation with the precipitation and relative humidity variables, with most cases being considered slight or close to zero. A more refined analysis was based on the type of consumers associated with each substation. These results showed that where consumption is more residential, the highest correlations were associated with maximum temperature in most stations in the northeast and average temperature in the southeast. In regions where consumption is primarily rural, the correlation observed with precipitation and relative humidity was the highest despite being negative. A more detailed analysis shows that rural production is associated with irrigation in these substations, which partly explains consumption, as when rainfall occurs, the demand for irrigation decreases, and thus energy consumption is reduced. [ABSTRACT FROM AUTHOR]

Since the 1964 Niigata and Alaskan earthquakes, which incurred severe liquefaction damage, liquefaction-related design for infrastructures and buildings has been developed exclusively on the principle of force equilibrium. However, the energy concept is increasingly recognized as superior for simplified and robust liquefaction designs because of the uniqueness of energy capacity in soil failures regardless of the differences in earthquake loads. The energy-based liquefaction evaluation method (EBM) has been pursued by many investigators where dissipated energy for liquefaction is focused in place of liquefaction strength defined in the conventional stress-based method (SBM). Furthermore, the EBM enables sound liquefaction-related designs without resorting to sophisticated but highly variable/tricky numerical analyses and contributes as a scale to measure the reliability of those numerical tools. Thus, the EBM, though short of practical use in today's engineering works, should be able to serve as a simplified liquefaction evaluation tool besides the SBM. We reviewed the basic idea as well as the recent developments of the EBM together with the supporting data. We also discussed how to simplify and approximate the energy-based liquefaction behavior to implement robust evaluations in practical problems. The EBM liquefaction evaluation steps were delineated and exemplified by case studies for practicing engineers compared to the SBM. [ABSTRACT FROM AUTHOR]

Enterococcus faecalis is a versatile lactic acid bacterium with a large variety of implications for humans. While some strains of this species are pathobionts being resistant against most of the common antibiotics, other strains are regarded as biological protectants or even probiotics. Accordingly, E. faecalis strains largely differ in the size and content of their accessory genome. In this study, we describe the genome-scale metabolic network reconstruction of E. faecalis ATCC 19433, a non-resistant human-associated strain. A comparison of the genome-scale metabolic model (GSM) of E. faecalis ATCC 19433 with a previously published GSM of the multi-resistant pathobiontic E. faecalis V583 reveals high similarities in the central metabolic abilities of these two human associated strains. This is reflected, e.g., in the identical amino acid auxotrophies. The ATCC 19433 strain, however, has a 14.1 % smaller genome than V583 and lacks the multiple antibiotic resistance genes and genes involved in capsule formation. Based on the measured metabolic fluxes at different growth rates, the energy demand at zero growth was calculated to be about 40 % lower for the ATCC 19433 strain compared to V583. Furthermore, the ATCC 19433 strain seems less prone to the depletion of amino acids utilizable for energy metabolism. This might hint at a lower overall energy demand of the ATCC 19433 strain as compared to V583. ● A genome-scale metabolic model of E. faecalis ATCC 19433 has been developed. ● The model reproduces experimental flux distributions and amino acid auxotrophies. ● A comparison to a model of E. faecalis strain V583 indicates a lower energy demand of ATCC 19433. [ABSTRACT FROM AUTHOR]

Sustainable energy sources are continually advocated as the globe strives to transition from non-renewable forms. Energy security risks associated with the gradual depletion of petroleum resources and the related climate change effects require remedies. Nations have enacted laws stipulating biofuel blending mandates to reverse these adverse effects. Using the Low Emissions Analysis Platform (LEAP) software, this study assessed the potential long-term impact on energy demand and CO2 emissions from implementing B10 (10% biodiesel and 90% conventional diesel blend) in Uganda's petroleum sub-sector was assessed. Four scenarios were analysed from 2019 to 2050: baseline (100% fossil diesel), B10, B20 (20% biodiesel and 80% conventional diesel blend), and electric-powered transport, were analysed for 2019 to 2050. The analysis revealed that replacing fossil diesel with B10 reduces fossil-derived energy demand by 3.52% and projects an 8.38% reduction in CO2 emissions compared to the baseline scenario. The B20 and electric vehicle scenarios offer an even greater reduction. Specifically, the B20 scenario shows an 8.46% decrease, and the electric scenario shows a 9.98% decrease in fossil-derived energy demand. These reductions are attributed to the lower proportion of fossil fuels in biodiesel blends and the higher energy efficiency of electric vehicles. B10 use as a substitute fuel for diesel-powered vehicles is expected to mitigate transport sector emissions in Uganda with minimal impact on final energy demand. B20 and electrification scenarios with lower final energy demand and higher environmental impact reductions are more advantageous relative to the B10 and baseline scenarios. Future analyses should establish the optimal renewable fuel and vehicular technologies mix for a net zero scenario by 2050 for Uganda's transport sector. [ABSTRACT FROM AUTHOR]

The country's energy demand is experiencing rapid growth, and fossil fuels remain the primary resources to meet this increasing need. However, global warming poses a significant challenge for governments and industries worldwide. To address this issue, utilizing methane as a fossil fuel and implementing CO2 capture and sequestration can prove to be effective mitigation strategies. This paper presents an innovative idea to utilize unmineable coal seams in the Singrauli coal field for CO2 sequestration while extracting associated CBM to offset the cost of the sequestration process. The findings reveal that the field has great potential for CO2 sequestration. The estimated average maximum sorption capacities of CO2 and CH4 in the coal seams under in situ conditions of the basin are 39.50 m3/t and 11.15 m3/t, respectively. The total maximum sorption capacities of CO2 and CH4 in the potential coal seams are estimated to be 10,758.00 Mm3 and 3,215.00 Mm3, respectively. These results indicate that the unmineable coal seams in the Singrauli coalfield hold potential for CO2 sequestration and the extraction of available CBM from the coal seams. [ABSTRACT FROM AUTHOR]

Balancing the ATP: NADPH demand from plant metabolism with supply from photosynthesis is essential for preventing photodamage and operating efficiently, so understanding its drivers is important for integrating metabolism with the light reactions of photosynthesis and for bioengineering efforts that may radically change this demand. It is often assumed that the C3 cycle and photorespiration consume the largest amount of ATP and reductant in illuminated leaves and as a result mostly determine the ATP: NADPH demand. However, the quantitative extent to which other energy consuming metabolic processes contribute in large ways to overall ATP: NADPH demand remains unknown. Here, we used the metabolic flux networks of numerous recently published isotopically non-stationary metabolic flux analyses (INST-MFA) to evaluate flux through the C3 cycle, photorespiration, the oxidative pentose phosphate pathway, the tricarboxylic acid cycle, and starch/sucrose synthesis and characterize broad trends in the demand of energy across different pathways and compartments as well as in the overall ATP:NADPH demand. These data sets include a variety of species including Arabidopsis thaliana, Nicotiana tabacum, and Camelina sativa as well as varying environmental factors including high/low light, day length, and photorespiratory levels. Examining these datasets in aggregate reveals that ultimately the bulk of the energy flux occurred in the C3 cycle and photorespiration, however, the energy demand from these pathways did not determine the ATP: NADPH demand alone. Instead, a notable contribution was revealed from starch and sucrose synthesis which might counterbalance photorespiratory demand and result in fewer adjustments in mechanisms which balance the ATP deficit. [ABSTRACT FROM AUTHOR]

Zambia has recently faced prolonged load shedding due to reduced hydroelectricity generation caused by unfavourable rainfall patterns. To tackle this issue and satisfy increasing energy demands, the government aims to explore alternative energy sources like wind energy. However, the uncertainty surrounding the economic feasibility of implementing wind power projects in Zambia poses a significant challenge. This study evaluates the economic feasibility of eight proposed wind farm sites using net present value (NPV), simple payback period (SPP), internal rate of return (IRR), and levelized cost of electricity (LCOE). By examining energy yield analysis (EYA), wind speed, and financial indicators, the study identifies the most economically viable wind farm site(s). The results reveal that the Lusaka wind farm is the most economical, with an energy yield analysis of 386 GWh, wind speed of 8 m/s, NPV of USD 316 million, SPP of 2.9 years, IRR of 82%, and LCOE of 0.182 USD/kWh. An economic sensitivity analysis, varying the average electricity tariff, also points to Lusaka as the most financially viable site. Consequently, policymakers are advised to develop cost-reflective feed-in tariff (FiT) schemes and power purchase agreements to establish electricity tariffs that encourage investment from independent power producers in Zambia.

Abstract Grid integration constraints are limiting the deployment potential of renewable energy sources in Malta. Large penetrations of photovoltaics in the low voltage (LV) distribution network pose a significant risk to grid stability due to their inherent intermittency and are known to cause overvoltages and reverse power flows. The authors evaluate how self‐consumption strategies with distributed battery energy storage systems can contribute to the voltage regulation in LV networks and the reduction of reverse power flows. The batteries are controlled to absorb the reverse power flow at the dwellings' point of common coupling, before this is injected into the LV network. Simulations show that uncoordinated strategies are not suitable to address the distribution network challenges during reverse power flows and evening peak demands. On the other hand, self‐consumption coordinated by a time‐varying feed‐in tariff (FiT) can provide higher profitability to the prosumers while providing added benefits to the utility. The net‐billing profitability for the prosumers in a self‐consumption scenario with time‐varying FiT is transformed from the downward trend of the uncoordinated scenario to an upward trend against the increasing values of storage capacity.

In developing countries, electrical power distribution networks are often inadequate, particularly in small health centers. As a result, the electrical energy supplied by the grid is frequently interrupted. The productivity and quality of service delivered by these health centers to the people who live in these areas are severely affected by this issue. This issue can be resolved by incorporating battery storage systems along with renewable energy sources into the distribution system. The direct delivery of energy to customers is greatly aided by these renewable energy supplies. Partially, the grid supports such a system on a limited scale to guarantee the continuity of the energy supply. This study tried to resolve the problem due to these frequent power outages and its economic expenditures. To address the illustrated challenges, we tried to renovate the diesel generator with a solar and battery energy supply. The PVsyst software shows the average global solar radiation in the selected zone is5.84kmh/m2/day$$ 5.84\ \mathrm{kmh}/{\mathrm{m}}^2/\mathrm{day} $$. The annual energy demand for Gedeo health centers in 2023 is 3.32 MWH and the proposed PV‐battery hybrid system has a 10.95 MWH capacity. Moreover, when we utilize a diesel generator the Capital cost (CC), Net present cost (NPC), levelized cost of energy (LCOE), and payback period are 12 452.25$, 13 369.12$, 0.1$, and 10.7 years respectively. The economic assessment result of the proposed system is 4083$, 4727$, 0.059$, and 3.8 years consecutively. In southern Ethiopia, the annual emission from diesel generators alone, excluding the emission from the vehicles is close to 692 tons. Consequently, from the empirical economic assessment the installed solar energy is 90% more beneficial than the existing system. [ABSTRACT FROM AUTHOR]

During the gestation and lactation period, the energy demand in pregnant and lactating bitches is elevated. Non‐esterified fatty acids (NEFAs) are utilized either directly from the fed diet or from body fat storage. High NEFA concentration in the blood plasma leads to an increased risk for diseases. Therefore, measuring blood NEFA concentrations may be an indicator for a period of scarcity. The aim of this study is to explore if serum NEFA concentrations in healthy bitches change during gestation and lactation. Healthy pregnant and lactating bitches were sampled on three appointed dates around parturition. NEFA values were examined with a multiparameter clinical chemistry analyser. All statistical analyses were performed using R. Overall, 38 bitches were enrolled in the study. Twenty‐one bitches were sampled on all three appointed dates. The median NEFA concentration antepartum was 0.73 mmol/L (IQR: 0.59, 1.01); during peak lactation, it was 0.57 mmol/L (IQR: 0.44, 0.82); and around weaning, it was 0.58 mmol/L (IQR: 0.46, 0.73). NEFA concentrations rose slightly with litter size in late gestation. Body condition score had no influence on observed NEFA values. We conclude that NEFA concentrations widely remain within reference ranges in well‐fed pregnant and lactating bitches. Nevertheless, they may be a valuable parameter to assess the actual metabolic status of malnourished pregnant and lactating bitches. [ABSTRACT FROM AUTHOR]

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The technological advancement in all countries leads to massive energy demand. The energy trading companies struggle daily to meet their customers’ power demands. For a good quality, disturbance-free, and reliable power supply, one must balance electricity generation and consumption at the grid level. There is a profound change in distribution networks due to the intervention of renewable energy generation and grid interactions. Renewable energy sources like solar and wind depend on environmental factors and are subject to unpredictable variations. Earlier, energy distribution companies faced a significant challenge in demand forecasting since it is often unpredictable. With the prediction of the ever-varying power from renewable sources, the power generation and distribution agencies are facing a challenge in supply-side predictions. Several forecasting techniques have evolved, and machine learning techniques like the model predictive controller are suitable for arduous tasks like predicting weather-dependent power generation in advance. This paper employs a Model Predictive Controller (MPC) to predict the solar array’s power. The proposed method also includes a system identification algorithm, which helps acquire, format, validate, and identify the pattern based on the raw data obtained from a PV system. Autocorrelation and cross-correlation value between input and predicted output 0.02 and 0.15. The model predictive controller helps to recognize the future response of the corresponding PV plant over a specific prediction horizon. The error variation of the predicted values from the actual values for the proposed system is 0.8. The performance analysis of the developed model is compared with the former existing techniques, and the role and aptness of the proposed system in smart grid digitization is also discussed. [ABSTRACT FROM AUTHOR]

Background: Repeated sprint ability (RSA) is defined as the ability to recover and maintain maximal effort during repeated sprints, recognised as a crucial performance component in team sports. The exercise mode used to test RSA may influence performance and the contributions of different energy systems. The primary aim of this study is to address the critical gap between traditional cycling-based anaerobic tests, such as the Wingate test, and the practical, sport-specific demands of running in field-based team sports. Methods: This study involved 32 professional soccer players (age: 21.2 ± 1.3 years; height: 177.8 ± 4.3 cm; and mass: 71.3 ± 6.4 kg). They performed cycling- and running-based repeated sprint tests, with similar total sprint numbers, durations, and recovery times, on different days. Contributions from adenosine triphosphate-phosphocreatine (ATP-PCr), glycolytic, and oxidative systems were estimated through body weight, oxygen uptake (VO2), blood lactate (BLa), and the fast component of excess post-exercise oxygen consumption (EPOC). The VO2 levels and heart rate (HR) were monitored during the rest (10 min), exercise, and recovery (15 min) phases in a breath-by-breath mode using a portable gas exchange system. BLa was measured before (at rest) and 1, 3, 5, 7, and 10 min after the running and cycling tests using a handheld portable analyser. A mono-exponential model estimated the ATP-PCr system contribution, calculated using the fast component of EPOC following the final sprint and the sum of the VO2-time integral during rest intervals. Results: The cycling tests demonstrated significantly higher values for the peak power (PP), mean power (MP), and rate of perceived exertion (RPE) (p < 0.05), while the heart rate peak and blood lactate responses were similar across all modalities. The fatigue index was notably higher in the running tests (p < 0.05). Furthermore, the running tests showed greater contributions in both the percentage and absolute terms from the adenosine triphosphate-phosphocreatine (ATP-PCr) system (p < 0.01), total energy demand (p < 0.05), and total energy expenditure (TEE) (p < 0.01). Notably, the running tests resulted in an increased phosphocreatine breakdown (p < 0.05) and rapid phosphocreatine replenishment (p < 0.01). A simple linear regression analysis highlighted a significant determination coefficient between these performance variables and the contributions of the energy systems, affirming the robustness of the results. The correlation heatmaps further illustrated these relationships, with higher correlations for the PP and MP across modalities (0.41), emphasising the moderate association between cycling and running tests in these metrics. Conclusions: This study elucidated the similarities and differences in energy system contributions and performance outcomes between a cycling and a running repeated sprint protocol, with a comparable total sprint time and work–rest ratio. The findings reveal that a running repeated sprint test elicits a higher energy demand and a higher contribution from the PCr energy system compared to cycling. Performance variables were not associated between running and cycling tests, suggesting those tests cannot be used interchangeably. [ABSTRACT FROM AUTHOR]

The primary aim of this paper is to identify the factors that have influenced changes in the level of energy-related greenhouse gas emissions (GHG) over time. Using the additive and multiplicative LMDI decomposition method, the observed changes are analyzed with regard to four factors: pollution coefficient, energy intensity, economic structure, and economic activity. Results in the period of interest (2007–2017) from Tunisia show that economic activity constitutes the main driver of increasing GHG emissions. Structural change has a positive but relatively modest effect on the program. The effect of energy intensity negatively contributes to the observed increase of GHG emissions. Against this backdrop, Tunisia should continue to reduce its dependence on energy from traditional sources in favor of renewable energy sources to limit future GHG emissions, while also improving the efficiency of energy use and energy conservation. Following the discussion of results, this study concludes with select policy recommendations. [ABSTRACT FROM AUTHOR]

In Ecuador, the growth of the transportation sector has significantly increased greenhouse gas emissions. According to experts, this sector currently contributes to 49.8% of total greenhouse gas emissions in this country. This poses significant challenges for environmental sustainability, emphasizing the urgent need for effective strategies to mitigate these emissions and promote environmentally friendly practices. Therefore, this study focuses on developing a dynamic simulation of energy scenarios for the year 2035 in the transportation sector, with the goal of transitioning to sustainable mobility, as fossil fuels are the main pollutants in the country. This study proposes system dynamics models using VENSIM 6.0b software to estimate the total energy demand and pollutant emissions in the transportation sector. The results suggest that if Ecuador aligns with global mobility trends and implements public policies promoting the use of electric vehicles, total CO2 emissions could potentially decrease from 50,161,432 kilobarrels of oil equivalent (kBOE) to 20,589,720 kBOE by the year 2035. [ABSTRACT FROM AUTHOR]

Restructuring energy supply and demand is one of the essential measures to realize the carbon emissions reduction target. To explore the impact of the restructuring of energy supply and demand on fossil fuel demand under the carbon emissions reduction target, takes China as an example, obtains a data set, and utilizes the elasticity analysis, incremental contribution method, weighted moving average method and scenario analysis to forecast the structure of energy demand and the consumption of fossil fuels. The study results show that the projected values of China's total energy consumption demand in 2030 and 2035 will be 6300.19-6419.82 million tons of standard coal and 6955.92-7175.29 million tons of traditional coal, respectively. The shares of coal, oil, and natural gas in total energy consumption in 2030 will be 45.68-46.35%, 17.95-18.27%, and 10.71-10.89% respectively; by 2035 the energy structure will be further optimized, and the shares of coal, oil, and natural gas in the total energy consumption will be 39.71-40.53%, 18.07-18.56% and 11.86-12.15%, respectively. Further forecasts of gasoline, kerosene, diesel, and fuel oil consumption in 2030 and 2035 are analyzed in this study. [ABSTRACT FROM AUTHOR]

As society develops and urbanization accelerates, energy requirement and environmental emissions in the road transport field have expanded in Shenyang, China. It is necessary to look at its future energy needs and environmental emission trends. In this work, we have used the LEAP model to account for the energy requirement and environmental emissions from 2017 to 2030 under five scenarios for the road transport sector in Shenyang. Additionally, the intake fraction approach was applied to estimate the health effects and financial damages of NOX, SO2, PM10, and PM2.5 emissions. The results indicate that the energy required in 2030 will reach 8.18-10.65 Mtce in various scenarios. By 2030, under the business as usual (BAU) scenario, the emissions of CO2, CO, SO2, NOX, PM10, and PM2.5 will be 31089.51, 1794.4, 20.59, 205.88, 6.6, and 6.5 kt, respectively. Regarding health benefits, financial damage caused byNOX, SO2, PM10, andPM2.5 emissions will reach RMB 8.594, 0.117, 1.013, and 1.566 billion in 2030 under the BAU scenario. Moreover, the development of energy-efficient and new energy automobiles is the best means of reducing energy demand and environmental emissions; thus, this approach should be paid special attention when developing future energy efficiency and emission abatement policies for road transport. [ABSTRACT FROM AUTHOR]

The effect of knife mill variables on comminute beech chips with 7.8 wt % in moisture was experimentally studied to define models that predict characteristic particle size and energy requirement based on knife mill variables. The experimental data showed that particle size decreases with the rotor's increasing rotational peripheral speed and decreasing screen sieve size. Studying their mutual relations, an original empirical model was developed to predict particle size based on screen sieve size and rotor speed. The energy requirement of 1.47–12.62 kWh t−1 was reached to reach D50 particle sizes 0.48–1.58 mm. Regarding the brittle behaviour of beech chips, the Rittinger theory was applied to predict the energy requirement of size reduction with the Rittinger constant CRD50 = 13.0 kWh mm t−1 for D50 in size. [ABSTRACT FROM AUTHOR]

The present study aims to describe the potential sources of energy in Peru with the purpose of implementing them to achieve a sustainable system, taking advantage of the natural resources in the Peruvian land. To achieve this, three alternative scenarios have been defined and analyzed using the LEAP (Long-range Energy Alternatives Planning) software [Software Version: 2020.1.112]. The scenarios are as follows: the first one, the Business-as-Usual scenario, is based on normal trends according to historical data and referencing projections made by Peruvian state entities; the second one is focused on Energy Efficiency, the highlighted characteristic is taking into consideration the efficient conditions in transmission and distribution of electric energy; and the third one, centered on Geothermal Energy, focused on the development of this type of energy source and prioritizing it. The primary purpose of this analysis is to identify the advantages and disadvantages inherent in each scenario in order to obtain the best out of each one. In this way, the intention is to propose solutions based on Peru's national reality or possible uses of the country's energy potential to supply its energy demand. Currently, Peru's energy demand relies on fossil fuels, hydraulic, and thermal energy. However, there is the possibility of transforming this system into a sustainable one by strengthening existing and growing energy sources such as solar and wind energy and new technologies for hydraulic and thermal energy, in addition to considering geothermal energy as the main energy source in the third scenario. The new system mentioned satisfactorily indicates that the CO2 equivalent emissions decrease significantly in the third scenario, with a 15.8% reduction compared to the first scenario and a 9.7% reduction in comparison to the second. On the other hand, the second scenario shows a 5.6% decrease in CO2 emissions compared to the first, resulting from improvements in technology and energy efficiency without requiring significant modifications or considerable investments, as in the third scenario. [ABSTRACT FROM AUTHOR]

AbstractThis paper presents the first empirical evaluation of Climate Perks, an employee benefit scheme designed to reduce air travel demand by offering additional paid annual leave to those travelling without flying for vacation purposes. In-depth interviews and surveys were undertaken with employers that offer Climate Perks and travellers who have claimed journey days. The findings show the value of positive action to enable sustainable consumption in contributing to catalysing and mainstreaming low-carbon travel, drawing out key themes around recruiting travellers to surface travel, sharing knowledge and know-how, recrafting temporal experiences and learning-by doing to overcome infrastructural complexities of surface travel in a mobility system presently dominated by air travel. The paper also discusses how the boundaries around personal action on climate change are shifting as employers recognise the contribution of work to the formation of personal routines. This paper thereby contributes to understanding air travel demand and explores the potential for reducing this emissions intensive practice. [ABSTRACT FROM AUTHOR]

With the global pursuit of renewable energy and carbon neutrality, hydrogen-based microgrids have also become an important area of research, as ensuring proper design and operation is essential to achieve optimal performance from hybrid systems. This paper proposes a distributed control strategy based on multiagent self-triggered model predictive control (ST-MPC), with the aim of achieving demand-side control of hydrogen-based microgrid systems. This architecture considers a hybrid energy storage system with renewable energy as the main power source, supplemented by fuel cells based on electrolytic hydrogen. The primary objective of this architecture is aiming at the supply and demand balance problem under the supply and demand relationship of microgrid, the service life of hydrogen-based microgrid energy storage equipment can be increased on the basis of realizing demand-side control of hydrogen energy microgrid system. To accomplish this, model predictive controllers are implemented within a self-triggered framework that dynamically adjusts the counting period. The simulation results demonstrate that the ST-MPC architecture significantly reduces the frequency of control action changes while maintaining an acceptable level of set-point tracking. These findings highlight the viability of the proposed solution for microgrids equipped with multiple types of electrochemical storage, which contributes to improved sustainability and efficiency in renewable-based microgrid systems. [ABSTRACT FROM AUTHOR]

BACKGROUND: One of the biggest challenges facing the soft fruit sector is to propagate disease free strawberry plants in a timely manner, financially viable approach and in an eco-friendly fashion. OBJECTIVE: This study sets out to investigate the efficacy of growing everbearing Fragaria×ananassa 'Malling Ace' runners at different propagation dates and under Different LED lighting regimes using vertical farming units and glasshouse (as control). METHODS: To evaluate the effects of the treatment factors, some of the main horticultural features including rooting success rate, rooting speed, root and shoot biomass production, chlorophyll content and bud emergence were measured and statistically analysed. In addition, this study aimed to investigate the energy efficiency of a self-sufficient Direct Current (DC) based vertical farming unit, versus a vertical farming unit using the Alternate Current (AC) electricity provided by national grid electricity. RESULTS: light, tipping date, and the interaction between light and tipping date, significantly influenced shoot biomass, total crown diameter, and chlorophyll content in the leaves of 'Malling Ace'. In terms of energy demand, the DC unit had a higher level of energy efficiency over AC unit with 23% less energy demand. CONCLUSION: In general, this trial demonstrated vertical farming as a potentially sustainable method and efficient tool in propagating strawberry plants. [ABSTRACT FROM AUTHOR]

The fast growth of the population is leading to an ever-increasing trend in energy consumption these days. In this regard, the construction industry is among the biggest consumers. Considering that most buildings are residential, energy optimization is essential, especially during the initial design phase. A practical way of building design with less energy demand is passive design methods. The importance of this issue is more visible in residential buildings in cold climates, which have the greatest temperature fluctuations. This study aims to investigate the energy demand of different passive strategies applied to a residential building in the cold climate of Tabriz, Iran and to select the most efficient design factor. The methodology is a combination of qualitative and quantitative methods. As the first step, considering the theoretical framework of the research, passive systems and the factors affecting building envelope thermal performance are determined. In the next step, EnergyPlus is used to analyze the application of the passive systems in the baseline model. All states are simulated separately in terms of the amount of heating and cooling energy demand and the results are presented in compareable graphs. In the third step, the strategy that has the greatest impact on reducing energy demand in cold-climate buildings is identified, and the most efficient alternative is presented through the analysis of different scenarios compared to the baseline model. This research reveals that heat loss through the envelope accounts for most of the energy demand, and thermal insulation plays an important role in reducing that loss. Also, different scenarios (materials, thickness and location) of thermal insulation were investigated. It shows that the optimal mode of thermal insulation in residential buildings in cold climates is to use polyurethane insulation with a thickness of 7 cm in the outer part of the building wall. [ABSTRACT FROM AUTHOR]

The harvesting of microalgae biomass cultivated during different processes is still identified as the main driver of biomass production cost. Particularly in the field of wastewater treatment, an energy-efficient and reliable harvesting or separation method is needed to remove microalgae biomass from the wastewater after nutrient assimilation. In this study, the suitability of pile cloth media filtration (PCMF) for microalgae harvesting during wastewater treatment is investigated. A mini plate PCMF was operated over 18 months with three different pile cloth media as part of a pilot-scale wastewater treatment plant incorporating a microalgae treatment step for phosphorus and nitrogen removal. During this time, the removal rates and achievable total suspended solids (TSS) concentration in the effluent were recorded. Differences between the three pile cloth media were noticeable, with TSS concentrations ranging from 9.7 mg·L−1 to 17.7 mg·L−1. The pilot-scale data were used to determine the dimensions of a large-scale PCMF and to estimate its energy demand. This resulted in theoretical energy demands of 7 to 8 Wh·m−3 or 37 Wh·kg−1 TSS, considerably lower than the energy demand of other harvesting technologies. [ABSTRACT FROM AUTHOR]

The article is devoted to the study of the future demand for energy in transport in the context of the transition to a low-carbon development trajectory in accordance with the adopted "Strategy for the long-term development of the Russian Federation with low greenhouse gas emissions until 2050." The research is based on scenario forecasting and mathematical modeling methods. The results of a forecast assessment of the demand for motor fuel and electricity in passenger transport in Russia as a whole and in federal districts until 2050 have been presented, taking into account: 1) transition of the economy to a low-carbon development model; 2) change in the structure of passenger turnover in favor of less carbon-intensive modes of transport; 3) increase in the energy efficiency and environmental safety of vehicles. The possible consequences of large-scale development of electric transport in Russia in the long term have been given. [ABSTRACT FROM AUTHOR]

About 20% of the world's CO2 emissions originate from transport. Many countries are committed to decarbonizing their transport sector. Singapore pledged to electrify a whole host of its land transportation fleet, which includes private cars, public buses, ride-hail vehicles, and motorcycles. This paper proposes a simple empirical framework to estimate the future energy demand after 100% electrification has been realized for nine selected road transport vehicle sub-classes and to calculate the carbon emission reduction potential based on various scenarios. The present energy demand for each vehicle sub-class is first calculated based on parameters like petrol and diesel consumption, heat value and density of petrol and diesel, population of vehicle type, and average mileage per vehicle sub-class. Several scenarios are presented, and an analysis is carried out to derive a range of emission factors which are used to estimate the carbon emission reduction potential. Relative to the present day, the future energy demand estimates reveal an overall reduction of 73.60%. Full electrification and a "clean" power generation mix could lead to an emission reduction as high as 93.64% across all vehicles sub-classes, with private cars having the highest reduction potential. [ABSTRACT FROM AUTHOR]

The increasing industrial development and energy demand have necessitated the maximization of available energy use and the deployment of renewable resources. Effective energy management, optimal modeling, and efficient planning are essential to transform the power system into a high-efficiency, optimal model. This study focuses on the initial step of modeling smart buildings (SBs) equipped with non-responsive devices and renewable photovoltaic sources. A comprehensive energy management (EM) plan is formulated for these buildings, incorporating the KNX protocol for solar energy system management. Batteries are integrated into the building model to store energy during periods of low consumption and serve as generators during peak load conditions, with the primary goal of minimizing power system losses and related costs. To address the complexity of this model, whale optimization algorithm (WOA) is employed for optimization. Optimal candidate buses are selected for interconnected building management based on a suggested sensitivity analysis to minimize losses. Cost performance is then assessed, considering energy production and sales. The findings indicate that substantial control of operating costs can be achieved through strategic management of battery charging and discharging, as well as the utilization of photovoltaic units. The proposed model is evaluated across various scenarios using a test system comprising 30 modified system, demonstrating its effectiveness in enhancing energy efficiency and management.

How can people imagine ways of achieving desirable energy futures in cities, oriented towards sufficiency? Building on the notion of sufficiency understood as avoiding demand while meeting human needs, this paper discusses the results of seven participatory workshops (n = 154 participants) held in Switzerland where new imaginaries around the future in cities were discussed. The results demonstrate that people can reflect on how living, consuming and working in cities could achieve the double dividend of meeting needs while using less energy. Based on the notion of practices-as-satisfiers, the participants collectively discussed synergic satisfiers or changes to practices were found to lead to energy savings and the satisfaction of multiple needs. However, for this to be possible, practices need to be thought of as part of systems: several changes would need to take place at once. Certain practices, such as reducing work time, are prefigurative of others. Participants reflected on how desirable imaginaries could be planned for, moving from individual change to proposed collective changes, considering their interrelated and prefigurative nature. The findings show how similar forms of public participation can lead to policy-relevant insights for planning, adopting and implementing sufficiency measures in cities. Policy relevance While efficiency and renewable energy measures are central in many national energy strategies, sufficiency policies—involving reductions in energy demand together with wellbeing—are emerging as a key component in the energy transition. The results of participatory workshops conducted with Swiss citizens provide insights into how to organize towards sufficiency in the future. First, policy development must consider sufficiency as part of the goal, and reflect on the means of reaching this goal. Second, people should be included in the debate, in that they can reflect on collective changes that must take place today to achieve desirable sufficiency futures. The results presented around synergic, interrelated and prefigurative nature of systems of practices and on changes needed are useful to inform debates on what could come out of such collective discussions, namely interrelated measures imagined at the collective level towards sufficiency.

Pakistan is faces significant challenges in meeting its energy demand and consumption needs for consumers. This country's energy production from primary sources such as petroleum and natural gasses is incompetent in fuel-use and hence unable to meet feasibility cost. With an increasing population, Pakistan's energy consumption per capita has been steadily rising. This behaviour is leading to critical energy issues, especially in remote rural areas. This trend in rising energy costs and demand factors are similar to those in the energy markets in the South and South-East Asia. The primary energy sources in Asia continent, including fossil fuels, are insufficient supply to meet this growing demand in production and thus resulting in frequent electricity blackouts. Consequently, renewable energy sources such as solar photovoltaic (PV) and wind power have substantially started to produce energy and to provide a huge portion of Pakistan's daily energy needs apart in conventional energy currently. However, these sources are not yet as reliable, conventional energy bases have a challenge for sustainable energy production. As a result, renewable energy factors nonetheless initial started have effectively stabilized energy consumption, particularly for green electricity with net-zero carbon emissions. The aim of this study is to evaluate the feasibility and cost-effectiveness of integrating a microgrid hybrid system with combined (solar PV/wind power) renewable energy as well as conventional fossil fuel generators. This evaluation focuses on predicting energy production and its costs using Hybrid Optimization of Multiple Energy Resources (HOMER) software, and to enhance the electricity standards at NUST (National University for Sciences and Technology), Pakistan. The proposed methodology of microgrid hybrid system, when evaluated using HOMER software, shows a significant improvement in energy stability and cost efficiency. Moreover, this proposed system can reduce reliance on fossil fuels by a substantial percentage, enhances the predictability of energy production, and optimizes its energy consumption. These can achieve better performance metrics in terms of reliability, cost, and environmental impact; feasible solution for Pakistan and the developing countries. This proposed methodology offers a novel approach by integrating renewable energy sources with conventional generators to create a balanced and efficiency factor by microgrid system. This hybrid system goals as an investigation is to optimize this energy production, reduce carbon emissions, and provide a more stable and cost-effective energy supply.

The energy and industrial sectors' most recent extraordinarily quick expansion has resulted in a substantial rise in stationary CO2 sources. As a result, many questions have been raised concerning how to stop global warming and implement methods to mitigate its effects by 2050 to create a low-carbon and sustainable future. Over the last three decades, India's CO2 emissions have increased at a growth rate of 3.1 % mainly consumption of fossil fuels. In this global and Indian situation, the best method, besides renewable energy sources, is Carbon capture, utilization, and storage (CCUS), which will make it possible to achieve net-zero emissions targets within this century. Given this, a brief review and discussion that aims to study India's development in CCUS deployment and its importance for sustainable development were conducted. The review highlights the current energy scenario of India and the world, the CO2 increase trend, and its consequences globally. Furthermore, a brief discussion on the present status of several areas of carbon capture, utilization, and storage (CCUS) technologies have been done. To clearly define the maturity of each important component of the CCUS system with a commercialization direction route, we focused on determining the technological readiness level (TRL) from India's perspective. Further, we identified important data of different sizes from the biggest R&D schemes, policies, regulations and initiatives taken by the government across the country. Lastly, our study seeks to provide a guide for the successful implementation of CCUS in India.

Abstract Developing comprehensive scenarios for the shipping sector has been a challenge for the Integrated Assessment Model (IAMs) community, influencing how attainable decarbonization is in the sector, and for Earth System Models (ESMs), impacting the climate contribution of shipping emissions. Here we present an approach to develop spatially explicit energy demand projections for shipping in alignment with the Shared Socioeconomic Pathways framework and IAMs projections of global fossil fuel demand. Our results show that shipping could require between 14 and 20 EJ by 2050, corresponding to a 3% and 44% increase from 2018 for the SSP1-1.9 and SSP3-7.0 scenarios. Furthermore, the energy projections we present in this publication can be combined with different fuel mixes to derive emission inventories for climate modeling and, thus, improve our understanding of the various challenges in mitigating emissions for shipping. Through that, we aim to present a framework to incorporate detailed spatial shipping inventories and increase transparency for the scientific community.