Potential pathways to reach energy-related CO2 emission peak in China: analysis of different scenarios.

The prevalence of global unilateralism and the shock of COVID-19 brought considerable uncertainty to China's economic development. Consequently, policy selection related to the economy, industry, and technology is expected to significantly impact China's national economic potential and carbon emission mitigation. This study used a bottom-up energy model to assess the future energy consumption and CO2 emission trend before 2035 under three scenarios: a high-investment scenario (HIS), a medium-growth scenario (MGS), and an innovation-driven scenario (IDS). These were also used to predict the energy consumption and CO2 emission trend for the final sectors and calculate each sector's mitigation contribution. The main findings were as follows. Firstly, under HIS, China would achieve its carbon peak in 2030, with 12.0 Gt CO2. Moderately lowering the economic growth rate to support the low-carbon transition of the economy by boosting the development of the low-carbon industry and speeding up the employment of key low-carbon technologies to improve energy efficiency and optimize energy structure in the final sectors, the MGS and the IDS would achieve carbon peak approximately in 2025, with a peak of 10.7 Gt CO2 for the MGS and 10.0 Gt CO2 for the IDS. Several policy recommendations were proposed to meet China's nationally determined contribution targets: instigating more active development goals for each sector to implement the "1+N" policy system, taking measures to accelerate the R&D, boosting the innovation and application of key low-carbon technologies, strengthening economic incentives, forming an endogenous driving force for market-oriented emission reduction, and assessing the climate impacts of new infrastructure projects.

Markers of Chemical and Microbiological Contamination of the Air in the Sport Centers.

This study aimed to assess the markers of chemical and microbiological contamination of the air at sport centers (e.g., the fitness center in Poland) including the determination of particulate matter, CO2, formaldehyde (DustTrak™ DRX Aerosol Monitor; Multi-functional Air Quality Detector), volatile organic compound (VOC) concentration (headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry), the number of microorganisms in the air (culture methods), and microbial biodiversity (high-throughput sequencing on the Illumina platform). Additionally the number of microorganisms and the presence of SARS-CoV-2 (PCR) on the surfaces was determined. Total particle concentration varied between 0.0445 mg m-3 and 0.0841 mg m-3 with the dominance (99.65-99.99%) of the PM2.5 fraction. The CO2 concentration ranged from 800 ppm to 2198 ppm, while the formaldehyde concentration was from 0.005 mg/m3 to 0.049 mg m-3. A total of 84 VOCs were identified in the air collected from the gym. Phenol, D-limonene, toluene, and 2-ethyl-1-hexanol dominated in the air at the tested facilities. The average daily number of bacteria was 7.17 × 102 CFU m-3-1.68 × 103 CFU m-3, while the number of fungi was 3.03 × 103 CFU m-3-7.34 × 103 CFU m-3. In total, 422 genera of bacteria and 408 genera of fungi representing 21 and 11 phyla, respectively, were detected in the gym. The most abundant bacteria and fungi (>1%) that belonged to the second and third groups of health hazards were: Escherichia-Shigella, Corynebacterium, Bacillus, Staphylococcus, Cladosporium, Aspergillus, and Penicillium. In addition, other species that may be allergenic (Epicoccum) or infectious (Acinetobacter, Sphingomonas, Sporobolomyces) were present in the air. Moreover, the SARS-CoV-2 virus was detected on surfaces in the gym. The monitoring proposal for the assessment of the air quality at a sport center includes the following markers: total particle concentration with the PM2.5 fraction, CO2 concentration, VOCs (phenol, toluene, and 2-ethyl-1-hexanol), and the number of bacteria and fungi.

FDI and Wellbeing: A Key Node Analysis for Psychological Health in Response to COVID-19 Using Artificial Intelligence.

Developing countries are primary destinations for FDI from emerging economies following the World Investment Report 2022, including destinations in OECD countries. Based on three theoretical lenses and case analyses, we argue that Chinese outward FDI has impacts on wellbeing in destination countries, and that this is an important issue for psychological health in response to COVID-19. Based on the super-efficiency DEA approach, our study investigated the impact of Chinese outward FDI on wellbeing in OECD countries. We also applied a Tabu search to identify country groups based on the relationship between Chinese outward FDI and wellbeing and we developed a key node analysis of the country groups using an immune algorithm. This research has implications for public administrators in global governance and could help shape FDI policies to improve psychological health of the destination countries in response to COVID-19.

Indoor air surveillance and factors associated with respiratory pathogen detection in community settings in Belgium.

Currently, the real-life impact of indoor climate, human behaviour, ventilation and air filtration on respiratory pathogen detection and concentration are poorly understood. This hinders the interpretability of bioaerosol quantification in indoor air to surveil respiratory pathogens and transmission risk. We tested 341 indoor air samples from 21 community settings in Belgium for 29 respiratory pathogens using qPCR. On average, 3.9 pathogens were positive per sample and 85.3% of samples tested positive for at least one. Pathogen detection and concentration varied significantly by pathogen, month, and age group in generalised linear (mixed) models and generalised estimating equations. High CO2 and low natural ventilation were independent risk factors for detection. The odds ratio for detection was 1.09 (95% CI 1.03-1.15) per 100 parts per million (ppm) increase in CO2, and 0.88 (95% CI 0.80-0.97) per stepwise increase in natural ventilation (on a Likert scale). CO2 concentration and portable air filtration were independently associated with pathogen concentration. Each 100ppm increase in CO2 was associated with a qPCR Ct value decrease of 0.08 (95% CI -0.12 to -0.04), and portable air filtration with a 0.58 (95% CI 0.25-0.91) increase. The effects of occupancy, sampling duration, mask wearing, vocalisation, temperature, humidity and mechanical ventilation were not significant. Our results support the importance of ventilation and air filtration to reduce transmission.

Evaluation of indoor environmental quality, personal cumulative exposure dose, and aerosol transmission risk levels inside urban buses in Dalian, China.

The transmission of pollutants in buses has an important impact on personal exposure to airborne particles and spread of the COVID-19 epidemic in enclosed spaces. We conducted the following real-time field measurements inside buses: CO2, airborne particle concentration, temperature, and relative humidity data during peak and off-peak hours in spring and autumn. Correlation analysis was adopted to evaluate the dominant factors influencing CO2 and particle mass concentrations in the vehicle. The cumulative personal exposure dose to particulate matter and reproduction number were calculated for passengers on a one-way trip. The results showed the in-cabin CO2 concentrations, with 22.11% and 21.27% of the total time exceeding 1000 ppm in spring and autumn respectively. In-cabin PM2.5 mass concentration exceeded 35 µm/m3 by 57.35% and 86.42% in spring and autumn, respectively. CO2 concentration and the cumulative number of passengers were approximately linearly correlated in both seasons, with R value up to 0.896. The cumulative number of passengers had the most impact on PM2.5 mass concentration among tested parameters. The cumulative personal exposure dose to PM2.5 during a one-way trip in autumn was up to 43.13 µg. The average reproductive number throughout the one-way trip was 0.26; it was 0.57 under the assumed extreme environment. The results of this study provide an important basic theoretical guidance for the optimization of ventilation system design and operation strategies aimed at reducing multi-pollutant integrated health exposure and airborne particle infection (such as SARS-CoV-2) risks.

Uncertainty quantification of CO2 emissions from China's civil aviation industry to 2050.

To mitigate aviation's carbon emissions of the aviation industry, the following steps are vital: accurately quantifying the carbon emission path by considering uncertainty factors, including transportation demand in the post-COVID-19 pandemic period; identifying gaps between this path and emission reduction targets; and providing mitigation measures. Some mitigation measures that can be employed by China's civil aviation industry include the gradual realization of large-scale production of sustainable aviation fuels and transition to 100% sustainable and low-carbon sources of energy. This study identified the key driving factors of carbon emissions by using the Delphi Method and set scenarios that consider uncertainty, such as aviation development and emission reduction policies. A backpropagation neural network and Monte Carlo simulation were used to quantify the carbon emission path. The study results show that China's civil aviation industry can effectively help the country achieve its carbon peak and carbon neutrality goals. However, to achieve the net-zero carbon emissions goal of global aviation, China needs to reduce its emissions by approximately 82%-91% based on the optimal emission scenario. Thus, under the international net-zero target, China's civil aviation industry will face significant pressure to reduce its emissions. The use of sustainable aviation fuels is the best way to reduce aviation emissions by 2050. Moreover, in addition to the application of sustainable aviation fuel, it will be necessary to develop a new generation of aircraft introducing new materials and upgrading technology, implement additional carbon absorption measures, and make use of carbon trading markets to facilitate China's civil aviation industry's contribution to reduce climate change.

Monitoring of Thermal Comfort and Air Quality for Sustainable Energy Management inside Hospitals Based on Online Analytical Processing and the Internet of Things.

There is a need to ensure comfortable conditions for hospital staff and patients from the point of view of thermal comfort and air quality so that they do not affect their performance. We consider the need for hospital employees and patients to enjoy conditions of greater well-being during their stay. This is understood as a comfortable thermal sensation and adequate air quality, depending on the task they are performing. The contribution of this article is the formulation of the fundamentals of a system and platform for monitoring thermal comfort and Indoor Air Quality (IAQ) in hospitals, based on an Internet of Things platform composed of a low-cost sensor node network that is capable of measuring critical variables such as humidity, temperature, and Carbon Dioxide (CO2). As part of the platform, a multidimensional data model with an On-Line Analytical Processing (OLAP) approach is presented that offers query flexibility, data volume reduction, as well as a significant reduction in query response times. The experimental results confirm the suitability of the platform's data model, which facilitates operational and strategic decision making in complex hospitals.

The Influence of Ventilation Measures on the Airborne Risk of Infection in Schools: A Scoping Review.

OBJECTIVES: To review the risk of airborne infections in schools and evaluate the effect of intervention measures reported in field studies. BACKGROUND: Schools are part of a country's critical infrastructure. Good infection prevention measures are essential for reducing the risk of infection in schools as much as possible, since these are places where many individuals spend a great deal of time together every weekday in a small area where airborne pathogens can spread quickly. Appropriate ventilation can reduce the indoor concentration of airborne pathogens and reduce the risk of infection. METHODS: A systematic search of the literature was conducted in the databases Embase, MEDLINE, and ScienceDirect using keywords such as school, classroom, ventilation, carbon dioxide (CO2) concentration, SARS-CoV-2, and airborne transmission. The primary endpoint of the studies selected was the risk of airborne infection or CO2 concentration as a surrogate parameter. Studies were grouped according to the study type. RESULTS: We identified 30 studies that met the inclusion criteria, six of them intervention studies. When specific ventilation strategies were lacking in schools being investigated, CO2 concentrations were often above the recommended maximum values. Improving ventilation lowered the CO2 concentration, resulting in a lower risk of airborne infections. CONCLUSIONS: The ventilation in many schools is not adequate to guarantee good indoor air quality. Ventilation is an important measure for reducing the risk of airborne infections in schools. The most important effect is to reduce the time of residence of pathogens in the classrooms.

Spatiotemporal Evolution of Carbon Emissions According to Major Function-Oriented Zones: A Case Study of Guangdong Province, China.

Studying the spatiotemporal evolution of carbon emissions from the perspective of major function-oriented zones (MFOZs) is crucial for making a carbon reduction policy. However, most previous research has ignored the spatial characteristics and MFOZ influence. Using statistical and spatial analysis tools, we explored the spatiotemporal characteristics of carbon emissions in Guangdong Province from 2001 to 2021. The following results were obtained: (1) Carbon emissions fluctuated from 2020 to 2021 because of COVID-19. (2) Over the last 20 years, the proportion of carbon emissions from urbanization development zones (UDZs) has gradually decreased, whereas those of the main agricultural production zones (MAPZs) and key ecological function zones (KEFZs) have increased. (3) Carbon emissions efficiency differed significantly among the three MFOZs. (4) Carbon emissions from coastal UDZs were increasingly apparent; however, the directional characteristics of MAPZ and KEFZ emissions were not remarkable. (5) Carbon transfer existed among the three kinds of MFOZs, resulting in the economy and carbon emissions being considerably misaligned across Guangdong Province. These results indicated that the MFOZ is noteworthy in revealing how carbon emissions evolved. Furthermore, spatiotemporal characteristics, especially spatial characteristics, can help formulate carbon reduction policies for realizing carbon peak and neutrality goals in Guangdong Province.

Unintended consequences of COVID-19 public policy responses on renewable energy power: evidence from OECD countries in the EU.

Since 2020, governments around the world have implemented many types of public policies in response to the outbreak of COVID-19. These dramatic public policies have substantially changed production and consumption activities, thereby temporarily lowering electricity use and greenhouse gas emissions. This study argues that pandemic-induced public policies have unintentionally slowed the transition to renewable energy use in the EU since the decline in greenhouse gas emissions due to the lockdowns helped countries temporarily reduce their total emissions. We employ a fixed-effect model to investigate the effects of different types of COVID-19 public policy responses on electricity production, consumption, and net imports in 12 OECD countries in the EU, and we mainly focus on the share of electricity production from renewable energy sources. Among several public policy responses, stringent lockdown policies, such as workplace closures, stay-at-home requirements, and restrictions on gathering size, have negative and statistically significant impacts on electricity generation and consumption. Furthermore, countries with stringent lockdown policies are more likely to import electricity from other countries to mitigate the electricity shortages in their domestic markets. Importantly, we find that lockdown events have negative and statistically significant effects on the share of renewables in electricity production while increasing the share of fossil fuels in electricity production. In contrast, economic support policies such as income support, debt relief, and economic stimulus programs help reduce the share of fossil fuels in electricity production and decrease the net import of electricity from other countries. Our results indicate that the public policies implemented in response to the outbreak of COVID-19 have mixed effects on the transition to renewable energy sources in the EU, suggesting that the current decline in greenhouse gas emissions comes from the reduction in electricity use due to lockdown events instead of the adoption of renewable energy use and discouraging the transition to renewable energy sources.

Estimation of Occupancy Using IoT Sensors and a Carbon Dioxide-Based Machine Learning Model with Ventilation System and Differential Pressure Data.

Infectious diseases such as the COVID-19 pandemic have necessitated preventive measures against the spread of indoor infections. There has been increasing interest in indoor air quality (IAQ) management. Air quality can be managed simply by alleviating the source of infection or pollution, but the person within a space can be the source of infection or pollution, thus necessitating an estimation of the exact number of people occupying the space. Generally, management plans for mitigating the spread of infections and maintaining the IAQ, such as ventilation, are based on the number of people occupying the space. In this study, carbon dioxide (CO2)-based machine learning was used to estimate the number of people occupying a space. For machine learning, the CO2 concentration, ventilation system operation status, and indoor-outdoor and indoor-corridor differential pressure data were used. In the random forest (RF) and artificial neural network (ANN) models, where the CO2 concentration and ventilation system operation modes were input, the accuracy was highest at 0.9102 and 0.9180, respectively. When the CO2 concentration and differential pressure data were included, the accuracy was lowest at 0.8916 and 0.8936, respectively. Future differential pressure data will be associated with the change in the CO2 concentration to increase the accuracy of occupancy estimation.

Long-Term Assessment of a Set of CO2 Concentration Sensors in an In-Use Office Building.

The measurement of the CO2 concentration has a wide range of applications. Traditionally, it has been used to assess air quality, with other applications linked to the experimental assessment of occupancy patterns and air renewal rates. More recently, the worldwide dissemination of COVID-19 establishing a relationship between infection risk and the mean CO2 level has abruptly led to the measurement of the CO2 concentration in order to limit the spread of this respiratory disease in the indoor environment. Therefore, the extensive application of this measurement outside of traditional air quality assessment requires an in-depth analysis of the suitability of these sensors for such modern applications. This paper discusses the performance of an array of commercial wall-mounted CO2 sensors, focusing on their application to obtain occupancy patterns and air renovation rates. This study is supported by several long-term test campaigns conducted in an in-use office building located in south-eastern Spain. The results show a spread of 19-101 ppm, with a drift of 28 ppm over 5 years, an offset of 2-301 ppm and fluctuations up to 80 ppm in instantaneous measurements not related to concentration changes. It is proposed that values averaged over 30 min, using a suitable reference value, be used to avoid erroneous results when calibration is not feasible.

CO2 Concentrations and Thermal Comfort Analysis at Onsite and Online Educational Environments.

In building areas with high occupancy, such as classrooms, transmission routes of SARS-CoV-2 are increased when indoor air quality is deficient. Under this scenario, universities have adopted ventilation measures to mitigate contagious environments. However, the lack of adequate equipment or designs in old educational buildings is a barrier to reach minimum requirements. This study aims to quantify the indoor air quality and thermal comfort at universities and compare it to conditions in students' households. In this regard, several classrooms in buildings of the Polytechnic University of Catalonia were monitored for temperature, CO2 concentration and relative humidity. The people who used these classrooms were surveyed about their comfort perceptions. A sample of students was also monitored at their homes where they reported to studying during the exam period. By means of point-in-time surveys, students reported their daily comfort, for comparison with the monitored data. The results show that the recommendations for CO2 concentration, temperature, and relative humidity are not always met in any of the study spaces. These factors are more critical at universities due to the high occupancy. In addition, the surveys highlighted the perception that the environment is better at home than at university.

Carbon footprint of the 2021 and 2022 AAPOS annual meetings.

The COVID-19 pandemic necessitated a virtual annual meeting of the American Association for Pediatric Ophthalmology and Strabismus (AAPOS) in 2021, thus eliminating carbon emissions from travel to and from the planned meeting venue in Boston, Massachusetts. We found that the reduced carbon footprint of the virtual meeting saved 1,282 metric tonnes of CO2 emissions compared with estimated CO2 emissions for travel if the meeting had taken place in person, or 880 metric tonnes relative to the projected emissions associated with the in-person 2022 annual meeting in Scottsdale, Arizona. An entirely virtual or hybrid AAPOS meeting would reduce its environmental footprint and increase the opportunity for national and international participation and education.

An empirical study on correlation among poverty, inclusive finance, and CO2 emissions in China.

This paper explores the nonlinear relationship between poverty and CO2 emissions based on the panel data of 30 provinces in China from 2005 to 2019. In this study, the autoregressive distributed lag (ARDL) model is first used. Findings confirm that poverty has a negative impact on CO2 emissions in the short run and a positive impact in the long run, while both effects of inclusive finance on CO2 emissions are negative. In order to explore the reasons for the change in the coefficient of poverty, we introduce a moderating effect (ME) model and a dynamic panel threshold (DPT) model. The result shows that the negative effect of poverty on CO2 emissions diminishes with the moderation of inclusive finance. When inclusive finance crosses the threshold value (IFI = 0.2696), the impact of poverty on CO2 emissions will change from negative to positive gradually, which verifies the applicability of the "Poverty-CO2 Paradox" in China and provides an empirical basis for breaking the "Poverty-CO2 Paradox." Consequently, deepening poverty reduction and pushing the region's inclusive finance to the threshold level are proposed as effective ways to promote CO2 emission reduction.

Importance of ventilation and occupancy to Mycobacterium tuberculosis transmission rates in congregate settings.

BACKGROUND: Ventilation rates are a key determinant of the transmission rate of Mycobacterium tuberculosis and other airborne infections. Targeting infection prevention and control (IPC) interventions at locations where ventilation rates are low and occupancy high could be a highly effective intervention strategy. Despite this, few data are available on ventilation rates and occupancy in congregate locations in high tuberculosis burden settings. METHODS: We collected carbon dioxide concentration and occupancy data in congregate locations and public transport on 88 occasions, in Cape Town, South Africa. For each location, we estimated ventilation rates and the relative rate of infection, accounting for ventilation rates and occupancy. RESULTS: We show that the estimated potential transmission rate in congregate settings and public transport varies greatly between different settings. Overall, in the community we studied, estimated infection risk was higher in minibus taxis and trains than in salons, bars, and shops. Despite good levels of ventilation, infection risk could be high in the clinic due to high occupancy levels. CONCLUSION: Public transport in particular may be promising targets for infection prevention and control interventions in this setting, both to reduce Mtb transmission, but also to reduce the transmission of other airborne pathogens such as measles and SARS-CoV-2.

Spatial-Temporal Characteristics and Driving Factors of the Eco-Efficiency of Tourist Hotels in China.

At present, COVID-19 is seriously affecting the economic development of the hotel industry, and at the same time, the world is vigorously calling for "carbon emission mitigation". Under these two factors, tourist hotels are in urgent need of effective tools to balance economic and social contributions with ecological and environmental impacts. Therefore, this paper takes Chinese tourist hotels as the research object and constructs a research framework for Chinese tourist hotels by constructing a Super-SBM Non-Oriented model. We measured the economic efficiency and eco-efficiency of Chinese tourist hotels from 2000 to 2019; explored spatial-temporal evolution patterns of their income, carbon emissions, eco-efficiency, and economic efficiency through spatial hotspot analysis and center of gravity analysis; and identified the spatial agglomeration characteristics of such hotels through the econometric panel Tobit model to identify the different driving factors inside and outside the tourist hotel system. The following results were obtained: (1) the eco-efficiency of China's tourist hotels is higher than the economic efficiency, which is in line with the overall Kuznets curve theory, but the income and carbon emissions have not yet been decoupled; (2) most of China's tourist hotels are crudely developed with much room for improving the economic efficiency, and most of the provincial and regional tourist hotels are at a low-income level, but the carbon emissions are still on the increase; and (3) income, labor, carbon emissions, waste emissions, and water consumption are the internal drivers of China's tourist hotels, while industrial structure, urbanization rate, energy efficiency, and information technology are the external drivers of China's tourist hotels. The research results provide a clear path for the reduction in carbon emissions and the improvement of the eco-efficiency of Chinese tourist hotels. Under the backdrop of global climate change and the post-COVID-19 era, the research framework and conclusions provide references for countries with new economies similar to China and countries that need to quickly restore the hotel industry.

Decomposition of residential electricity-related CO2 emissions in China, a spatial-temporal study.

The COVID-19 pandemic brings a surge in household electricity consumption, thereby enabling extensive research interest on residential carbon emissions as one of the hot topics in carbon reduction. However, research on spatial-temporal driving forces for the increase of residential CO2 emissions between regions still remains unknown in terms of emissions mitigation in post-pandemic era. Therefore, we studied the residential CO2 emissions from the electricity consumption of China during the period 1997-2019. Afterward, the regional specified production emission factors, combining with electricity use pattern, living standard and household size, were modelled to reveal the spatial-temporal driving forces at national and provincial scales. We observed that the national residential electricity-related CO2 increased from 1997 to 2013, before fluctuating to a peak in 2019. Guangdong, Shandong and Jiangsu, from East China were the top emitters with 27% of the national scale. The decomposition results showed that the income improvement was the primary driving force behind the emission increase in most provinces, while the household size and production emission effects were the main negative effects. For the spatial decomposition, differences in the total households between regions further widen the gaps of total emissions. At the provincial scale of temporal decomposition, eastern developed regions exhibited the most significant decrease in production emissions. In contrast, electricity intensity effect showed negative emission influences in the east and central regions, and positive in north-eastern and western China. The research identified the different incremental patterns of residential electricity-related CO2 emissions in various Chinese provinces, thereby providing scientific ways to save energy and reduce emissions.

The role of tourism in service sector employment: Do market capital, financial development and trade also play a role?

Workers' living standards have recently deteriorated in the service sector throughout the world, although a few decades ago, service was among the fastest growing sectors in industrialised nations. However, in recent years, in service sectors tourism especially has been drying up. This paper examines the symmetric and asymmetric effects of tourism, market capital, financial development, and trade on service sector employment in Australia from the period 1991-2019. The results of the cointegration tests, notably the ARDL and NARDL bound tests, reveal that the variables are related in the long run. The positive effect of tourist arrival on service sector employment in Australia is confirmed by long-run estimates from both ARDL and NARDL approaches. Similarly, both approaches also confirm the long-run positive relation of financial development. However, while ARDL shows long-run negative and positive associations of market capital and trade, respectively, the opposite is found in the case of the NARDL approach. As a result, policy proposals like planning and initiating tools for ensuring consistent international arrivals and easing of entry requirements have been recommended by this study to assist Australia in enhancing service sector employment, thus promoting economic development.

Forecasting of energy consumption by G20 countries using an adjacent accumulation grey model.

This paper studies an adjacent accumulation discrete grey model to improve the prediction of the grey model and enhance the utilization of new data. The impact of COVID-19 on the global economy is also discussed. Two cases are discussed to prove the stability of the adjacent accumulation discrete grey model, which helped the studied model attain higher forecasting accuracy. Using the adjacent accumulation discrete grey model, non-renewable energy consumption in G20 countries from 2022 to 2026 is predicted based on their consumption data from 2011 to 2021. It is proven that the adjacent accumulation exhibits sufficient accuracy and precision. Forecasting results obtained in this paper show that energy consumption of all the non-renewable sources other than coal has an increasing trend during the forecasting period, with the USA, Russia, and China being the biggest consumers. Natural gas is the most consumed non-renewable energy source between 2022 and 2026, whereas hydroelectricity is the least consumed. The USA is the biggest consumer of Nuclear energy among the G20 countries, whereas Argentina consumed only 0.1 Exajoules of nuclear energy, placing it at the end of nuclear energy consumers.