Input: Claim: Battery technology advancements are crucial for storing renewable energy efficiently. [SEP] Text: for Incorporating a of Mechanical Engineering, University of Wisconsin, Madison, WI 53706 Email: upadhya2@wisc. edu Search for other works by this author on: PubMed 10. 1115/1. 4052983 Published Online: December 14, 2021 0907. Download citation file: Ris (Zotero) toolbar search Menu toolbar search search input Search input auto suggest filter your search All ContentJ. Energy Resour. Technol. A recent increase in the integration of renewable energy systems in existing power grids along with a lack of integrated dispatch models has led to waste in power produced. This paper presents a mixed-integer nonlinear optimization model for hybrid renewable-generator-plus-battery systems, with the objective of maximizing long-term profit. Prior studies have revealed that both high and low state of charge (SOC) of the battery is detrimental to its lifetime and results in reduced battery capacity over time. In addition, increased number of cycles of charge and discharge also causes capacity reduction. This paper models these two factors with a constraint relating capacity loss to the SOC and number of cycles completed by the battery. Loss in capacity is penalized in the objective function of the optimization model, thereby disincentivizing high and low SOCs and frequent cycling. A rolling time horizon optimization approach is used to overcome the computational difficulties of achieving global optimality within a long-term time horizon. By incorporating battery degradation, the model is capable of maximizing the profits from the power dispatch to the grid while also maximizing the life of the battery. This paper exercises the model within a case study using a sample photovoltaic system generation time series that considers multiple battery capacities. The results indicate that the optimal battery lifetime is extended in comparison to conventional models that ignore battery degradation in dispatch decisions. Finally, we analyze the relationship between battery operational decisions and the resultant capacity fade. Issue Section: Energy Storage/Systems Keywords: energy storage, energy systems analysis, optimization and control, simulation Topics: Batteries, Cycles, Optimization 1 energy technology continues to develop and improve, gradually reducing societal dependence on fossil fuels for electricity. The increased usage of renewable technologies has led to the manifestation of the so-called “duck curve” [1,2], which is a graph representing the progression of net electric power demand in the (CAISO) grid balancing area over time.

Output: corroborate


Input: Claim: Only teenagers are affected by mental health issues stemming from social media. [SEP] Text: Our finding on the positive association of BMI with drive for thinness and ineffectiveness suggests that evaluation of BMI can lead to dissatisfaction which may provide an effort to reach thinness (Piko et al. , Citation2022). In addition, those who were more passionately engaged in eating healthy food were also more prone to take physical activity regularly. Previous studies also reported that there was a close association between being physically active and ON tendencies, for example, among female athletes (Rudolph, Citation2018). Overall, different aspects of health behavior often overlap, such as diet control, sport and avoidance of harmful habits and form a concept of healthy lifestyle in which drive for thinness can play a decisive role. By way of definition, ON is based on obsession rather than a controlling behavior (Bratman & Knight, Citation2000; Moroze et al. , Citation2015; Ryman et al. , Citation2019). Not surprisingly, in our study there was a relationship between ON tendency and social media addiction. Other studies also found a higher occurrence of ON among those who overused social media compared to the average population (Turner & Lefevre, Citation2017). Social networking sites can strengthen attitudes toward healthy lifestyle, showing pictures of healthy and ideal (thin) bodies or giving advice on slimming and health practices (Saunders & Eaton, Citation2018). Social media addiction also contains the element of obsession, e. g. feeling an urge to use social media more and more, or trying to cut down on the use of social media without success (Bányai et al. , Citation2017; Piko et al. , Citation2022). All these findings suggest that ON tendency can be viewed as a type of behavioral addiction. These findings suggest that orthorexic tendencies may often go together with other psychological or social problems; in this case, psychological help could be necessary. The negative correlation with proactive coping or the positive association with maturity fears and ineffectiveness support the presence of psychological problems behind the orthorexic symptoms. Previous studies reported ambiguous results regarding self-concept: while in several studies lower self-esteem predicted ON (Bóna et al. , Citation2021; Yılmaz & Dundar, Citation2022), in another study ON was not related to self-esteem (Brytek-Matera et al. , Citation2022).

Output: contrast


Input: Claim: Microclimates can create variations in weather over short distances. [SEP] Text: Distribution of events (in pentads) selected according to the type of intraseasonal variability: 30–70 days (IS1; top) and 10–30 days (IS2; bottom). Events are selected when the magnitudes of the OLR anomalies averaged over the SACZ domain are less than 0. 8 standard deviation (IS1 neg; IS2 neg), greater than 0. 8 standard deviation (IS1 pos; IS2 pos) and between ±0. 3 standard deviation (IS1 null; IS2 null). Numbers of events are displayed for each austral summer (S 85–86; S 86–87; S 87–88; S 88–89). Total number of pentads for each type of intraseasonal variability is indicated in parenthesis. View in gallery Fig. 6. Anomaly correlation between forecasts of Z500 and reanalysis averaged over the SACZ (top) and region (40°S–20°N; 90°–20°W) (bottom). Plots on the left are for IS1 type, while plots on the right are for IS2 type. Dashed, dotted, and solid lines indicate positive, negative, and null cases, respectively. Horizontal axis indicates lead time in days. View in gallery Fig. 7. Root-mean-square (rms) errors between forecasts of Z500 and reanalysis averaged over the SACZ (top) and region (40°S–20°N; 90°–20°W) (bottom). Plots on the left are for IS1 type, while plots on the right are for IS2 type. Dashed, dotted, and solid lines indicate positive, negative, and null cases, respectively. Horizontal axis indicates lead time in days. raw image Fig. 1. Frequency response of Lanczos band-pass filters. The 30–70-day band is denoted intraseasonal type 1 (IS1), while variations of 10–30 days are designated intraseasonal type 2 (IS2). View raw image Fig. 2. Mean geopotential height at 500 hPa (Z500) during austral summer (top). Contour interval is 20 m. Total standard deviation of Z500 (bottom). Contour interval is 5 m. View raw image Fig. 3. Anomaly correlation between Z500 forecasts and verification fields.

Output: pass


Input: Claim: Battery technology advancements are crucial for storing renewable energy efficiently. [SEP] Text: Initially, when the use of renewable energy was almost negligible, the curve (Fig. 1) shows energy demand that is lower during the night and which gradually increases as the day progresses (as seen in year 2012). As the use of renewable energy—mainly solar and wind—has increased, a mid-day dip in the net energy demand on the grid has appeared. This leaves dispatchable electricity production plants with a challenge. Grid operators are increasingly forced to curtail solar power production during midday, since conventional plants (for example, those run by fossil fuels) cannot always be rapidly stopped and started to accommodate the excess solar energy produced. This, in turn, has led to the wastage of solar energy and highlights the need to better integrate variable renewables in the grid planning in order to maximize the use of renewable power generation. Fig. 1View largeDownload slideDuck curve showing electric power demand from years 2012 to 2020 [1]Fig. 1View largeDownload slideDuck curve showing electric power demand from years 2012 to 2020 [1] Close modal One path for accommodating variable renewables is adoption of hybrid systems comprised of a renewable generator—such as wind or PV—alongside a battery storage system which is controlled by a dispatch optimization model [3]. Such systems are of increasing interest commercially, yet are not well understood from a lifetime optimization perspective. In light of this, we develop an optimization model for PV-plus-battery hybrid systems that accounts for degradation of the battery and optimizes its use, thereby extending its life. This is accomplished with a novel, rolling time horizon, optimization model developed using the Pyomo optimization toolkit. A constraint characterizing capacity degradation of the battery with respect to certain key parameters is developed. The model maximizes revenues from electricity sales while also accounting for loss due to battery degradation, ensuring the most cost-effective usage of the battery. Battery degradation is strongly dependent on the number of cycles incurred over time. Battery cycling occurs when a charge or discharge (CD) sequence occurs, and there are several ways to count the number of cycles in a time horizon of interest. This paper compares the accurate, but algorithmically complex, “rainflow algorithm” for counting CD cycles with a more conventional cycle counting method. As described below, this study concludes that the difference in results between the two methods is small at 0. 044% of total capacity over a period of 1 year.

Output: corroborate


Input: Claim: Only teenagers are affected by mental health issues stemming from social media. [SEP] Text: A second strength was that we analyzed the relationship of ON tendency with psychological background variables without pathologizing ON symptoms which can be useful in prevention of eating disorders. However, we should also note some limitations here. The cross-sectional study design does not allow us to infer cause-and effect relationships. Further, due to the specific cultural context, the generalizability of our findings may be limited across cultures. Norms and beliefs among women regarding desired body habitus and body image differ across cultures, and we should take this into account when interpreting research results on disordered eating. Finally, we have applied a scale for measuring social media addiction in general and not addiction to a given social media platform (e. g. Instagram or TikTok); a more specific addiction scale would be a better choice to explore its role in ON. In future studies more psychological variables could be included, e. g. coping strategies, anxiety or depression. While we included only females in our sample, future studies could include males as well in order to map sex differences in predictors of ON tendencies. Future studies should also be focused on coping strategies of those with elevated orthorexic tendency (e. g. stress management, social support, adaptive vs. inadaptive coping). We should also include other psychological variables, such as mental health (anxiety, depression, life satisfaction). ConclusionOverall, our findings suggest that for young women with a special interest in healthy lifestyle, drive for thinness together with regular sporting, ON tendency may be serving as a behavior for achieving their goal. In addition, they are more prone to social media addiction which together with other psychological health problems (such as a feeling of ineffectiveness or maturity fears) may also contribute to ON tendency. We really think that orthorexic tendency cannot be diagnosed as a medical disorder without using diagnostic criteria from the Diagnostic and 5-TR. However, identification of ON tendencies may be used to identify those individuals predisposed to develop eating disorders in certain cases. Namely, ON behavior can act as a risk factor for disordered eating and eating disorders, together with other psychological problems. As a practical implication, it would be really useful to provide more information on social media platform to the public audience on the risks of ON in relation to healthy lifestyle to avoid eating disorders.

Output: contrast


Input: Claim: Microclimates can create variations in weather over short distances. [SEP] Text: Skill is computed considering all days from the four austral summers during the DERF period. Skill is shown for 1, 4, 6, 8, and 11 days lead time. Correlations greater than 0. 50 are indicated by dark shading. View raw image Fig. 4. (a) Mean outgoing longwave radiation (OLR) during austral summer (1 November–31 March). Contour interval is 5 W m−2 and shading indicates OLR is less than 220 W m−2. (b) Total OLR standard deviation during austral summer. Contour interval is 2. 5 W m−2 and shading indicates standard deviation greater than 20 W m−2. (c) Standard deviation of OLR anomalies for the IS1 type (30–70 days). Contour interval is 1. 0 W m−2 and shading indicates standard deviation greater than 10 W m−2. (d) Standard deviation of OLR anomalies for the IS2 type (10–30 days). Contour interval is 1. 0 W m−2 and shading indicates standard deviation greater than 15 W m−2. Region indicated with thick solid lines is used to characterize periods of strong (weak) convective activity over the (SACZ). View raw image Fig. 5. Distribution of events (in pentads) selected according to the type of intraseasonal variability: 30–70 days (IS1; top) and 10–30 days (IS2; bottom). Events are selected when the magnitudes of the OLR anomalies averaged over the SACZ domain are less than 0. 8 standard deviation (IS1 neg; IS2 neg), greater than 0. 8 standard deviation (IS1 pos; IS2 pos) and between ±0. 3 standard deviation (IS1 null; IS2 null). Numbers of events are displayed for each austral summer (S 85–86; S 86–87; S 87–88; S 88–89). Total number of pentads for each type of intraseasonal variability is indicated in parenthesis. View raw image Fig. 6. Anomaly correlation between forecasts of Z500 and reanalysis averaged over the SACZ (top) and region (40°S–20°N; 90°–20°W) (bottom). Plots on the left are for IS1 type, while plots on the right are for IS2 type.

Output: pass


Input: Claim: Battery technology advancements are crucial for storing renewable energy efficiently. [SEP] Text: This apparently insignificant difference allows us to approach formulating the optimization model using the conventional cycle counting algorithm, thereby reducing computational time and avoiding the algorithmic difficulties accompanying the rainflow algorithm. We note that the validity of this assumption was only demonstrated for the application under study of utility-scale energy arbitrage. 1. 1 Literature Review. When designing any power system with storage, one area of high investment cost is the battery system [4]. Lithium-ion batteries are emerging as a commonly used grid-scale storage technology due to their favorable energy density, extended cycle life, and capability of operating over a large temperature range [5]. However, with extended usage, the batteries undergo a reduction in capacity and increase in internal resistance [6–9]. Therefore, a large amount of effort is being devoted to developing techniques to understand and reduce the degradation of batteries, thereby extending their lifetime. One of two fundamental approaches are used to understand the degradation of batteries: the theoretical method and the empirical method. The theoretical method explores the internal structure of the battery. Herein, aging of the battery has been associated with side reactions occurring between the electrolyte and the electrodes [10–12]. Degradation due to cycling is correlated to the fatigue of the solid electrolyte interphase layer due to the intercalation and de-intercalation of lithium in the graphite active material [13]. Empirical methods, on the other hand, correlate battery operation parameters such as the depth of discharge (DOD) and state of charge (SOC) of the battery to the degradation of the battery [14–17]. However, operational-level observations do not offer a sufficient amount of information to provide a linkage to the molecular-level degradation of lithium-ion batteries [13,18]. Due to this lack of translatability between the two concepts, the empirical method is often used when designing optimization models for power systems. For the purpose of this paper, the empirical basis is adopted, and several key regression parameters are studied and considered in the formulation of the optimization model. Battery degradation is characterized using two primary categories: calendar aging and cyclic aging. Calendar aging describes loss in battery capacity over time due to any process that is independent of the act of charging or discharging the battery.

Output: corroborate


Input: Claim: Battery technology advancements are crucial for storing renewable energy efficiently. [SEP] Text: , Arvesen, A. , Suh, S. ,. , Vega, M. I. , and Shi, L. , 2015, “Integrated Life-of Electricity-of low-Carbon Technologies,” Proc. Natl. Acad. Sci. U. S. A. , 112(20), 6282. 61. Richardson, D. B. , 2013, “and the Electric Grid: A Review of Modeling Approaches, Impacts, and Integration,” Energy Rev. , 19, 254. 62. Akorede, M. F. , Hizam, H. , and Pouresmaeil, E. , 2010, “Resources and Benefits to the Environment,” Energy Rev. , 14(2), 734. 63. Paliwal, P. , Patidar, N. P. , and Nema, R. K. , 2014, “Planning of Distributed Generators: A Review of Technology, Objectives and Techniques,” Energy Rev. , 40, 570. 64. Upendra Roy, B. P. , and Rengarajan, N. , 2017, “of an System for System in Islanding Mode,” ASME J. Energy Resour. Technol. , 139(1), p. 011901. 65. Zhang, G. , Xiao, C. , and Razmjooy, N. , 2022, “Strategy of Hybrid PV/: A Case Study,” Int. J. Ambient Energy, 43(1), 3966. 66. Shin, W. , Han, J. , and Rhee, W. , 2021, “AI-Assistance for of Systems,” Energy, 221, p. 119775. 67. . , Zawbaa, H. M. , Kumar, N. , and Kamel, S. , 2022, “Management of in Smart Grids: A Survey on Strategies, Challenges, Modeling, and Optimization,” Energy Rep. , 8, 2490. 68. Feng, C. , Liu, Y. , and Zhang, J. , 2021, “A on to ” Int. J. Electr. Syst. , 132, p. 107176. 69. Kuo, P.

Output: pass


Input: Claim: Microclimates can create variations in weather over short distances. [SEP] Text: Dashed, dotted, and solid lines indicate positive, negative, and null cases, respectively. Horizontal axis indicates lead time in days. View raw image Fig. 7. Root-mean-square (rms) errors between forecasts of Z500 and reanalysis averaged over the SACZ (top) and region (40°S–20°N; 90°–20°W) (bottom). Plots on the left are for IS1 type, while plots on the right are for IS2 type. Dashed, dotted, and solid lines indicate positive, negative, and null cases, respectively. Horizontal axis indicates lead time in days. 30 Views 0 0 0 Views 235 105 4 PDF Downloads 104 28 4 of Variability on the in Storage over the Tigris–Basin Authors: Li-and Guo-Evaluation of Performance of to Authors:. Yang, Ardeshir Ebtehaj, Sajad Vahedizade, and A 440-of in California from Authors: Ian M. Howard, David W. Stahle, Michael D. Dettinger, Cody Poulsen, F. Martin Ralph, Max C. A. Torbenson, and Quantifying the Role of Variability and from to at in India Authors: Divya Upadhyay, Sudhanshu Dixit, and Rainfall and Flooding from in the Mid-of the Authors: Yibing Su, James A. Smith, and Previous 10. 1175/1520-0493(2000)128<0486:>2. 0. CO;2 Page(s): 486–494 3060. Email: cjones@icess. ucsb. edu author address: Dr. Charles Jones, Institute for (ICESS), University of 93106-3060. Email: cjones@icess. ucsb. edu 1. IntroductionThe climatology of the large-scale circulation over shows intense convective activity during the austral summer, especially over the Amazon basin. Many previous studies have demonstrated that associated with the region of deep cumulus convection and intense precipitation, there is an upper-level anticyclone, the Bolivian high, and a downstream trough over northeast Brazil (Horel et al. 1989; Jones 1990; Lenters and Cook 1999).

Output: pass


Input: Claim: Battery technology advancements are crucial for storing renewable energy efficiently. [SEP] Text: 9View largeDownload slideBattery profile generated for the model and capacity fade of the battery for every cycle charge/discharge of the battery of initial capacity 1,500 kwh Close modal Upon comparing the two models, it is seen that the dispatch model with battery optimization improves battery life. In the following calculations, it is assumed that 80% capacity is the EOL of the battery. The battery degradation for a period of one year for the model with battery optimization is 2. 99%, whereas without battery optimization is 3. 022%. These observed values are similar, and the model including battery lifetime optimization exhibits higher than expected degradation. Figure 10 illustrates that the battery SOC profile for the model with battery lifetime optimization significantly differs from the model without battery lifetime optimization. For example, the battery lifetime optimization model avoids large jumps in SOC in a single half cycle, but to do so, it also increases the total number of cycles that the battery experiences. Prior studies indicate that avoiding high and low SOC’s increase the life of the battery, which is reflected in the results. On the other hand, the increase in number of cycles completed reduces the life of the battery. This could suggest that the number of cycles plays a more significant role in determining the battery life, than suggested in the constraint used to calculate the battery degradation. Further research must be conducted to characterize the relationship of the SOC and number of cycles with the battery capacity. This can then be translated into the battery optimization model to improve the life of the battery. Fig. 10View largeDownload slideComparison of the battery profile generated from the dispatch model with battery optimization and without battery optimizationFig. 10View largeDownload slideComparison of the battery profile generated from the dispatch model with battery optimization and without battery optimization Close modal 4 paper develops a hybrid renewable generator optimization model with integrated battery optimization. This is done using mixed-integer nonlinear optimization on Pyomo. Various parameters affecting the cyclic aging of the battery are studied and it is shown that high and low SOC, as well increased number of cycles caused degradation of the battery. Therefore, an equation characterizing the capacity degradation of the battery in terms of these parameters (SOC and number of cycles) is developed and used in the battery optimization model. By doing so, the model ensures a more cost-effective use of the battery, thereby extending the expected lifetime of the battery.

Output: corroborate


Input: Claim: Renewable energy reduces reliance on fossil fuels and helps decrease carbon emissions. [SEP] Text: Although the main carbon source is fossil fuels in the near-term (Supplementary Fig. 8), carbon flow in 2050 considerably varies among our three scenarios. In the 1. 5C-CDR scenario, carbon sources remain largely dependent on fossil fuels, with a nearly equal amount sequestered in underground storage to meet net-zero emissions for the energy sector. The 1. 5C-DEC scenario shows the smallest carbon flows both from the atmosphere and fossil fuels, based on the underlying assumption of decarbonization. In contrast to these two scenarios, 1. 5C-CCU uses carbon from the atmosphere, which is mostly converted into synfuels that eventually return the atmosphere. Consequently, the total carbon uptake of the energy system in 1. 5C-CCU is almost twofold greater than the uptake in 1. 5C-DEC, although its dependence on fossil fuel resources is smallest among the three scenarios. Economic implications Figure 4a compares cumulative additional energy system costs from 2021 to 2050. Under the 1. 5C-CCU scenario, these costs constitute approximately 2. 1% of gross domestic product (GDP); this value is almost twofold greater than the costs in the 1. 5C-DEC and 1. 5C-CDR scenarios (1. 3% and 1. 2%, respectively). The 1. 5C-CCU scenario involves higher carbon prices relative to the other scenarios, exceeding 1,000 US$ t-CO2 − 1 in 2050; the 1. 5C-DEC and 1. 5C-CDR scenarios have carbon prices near 800 and 400 US$ t-CO2 − 1, respectively (Fig. 4b, Supplementary Fig. 9a-b). According to sensitivity scenario analysis, despite the optimistic assumptions concerning key technology options (AdvTech) reduce cumulative energy system costs and the carbon price of the 1. 5C-CCU scenario to approximately 1. 6% of GDP and 800 US$ t-CO2 − 1 in 2050, these costs remain higher than in the 1. 5C-DEC and 1. 5C-CDR scenarios because decreases in the costs of solar and wind power occur in those scenarios. This comparison highlights the economic challenges of the CCU scenario compared with other mitigation options. The mitigation cost increase in the 1. 5C-CCU scenario is associated with the synfuel production cost.

Output: contrast


Input: Claim: Microclimates can create variations in weather over short distances. [SEP] Text: In contrast, the weakening of the SACZ is related to suppressed convection in the central Pacific but enhanced convective activity in the western and eastern Pacific. The different behavior in the predictability during strong versus weak SACZ events deserves additional investigation. The purpose of this note is to further motivate research on the influence of intraseasonal variations in the predictive skill over the SACZ. As is pointed out above, the SACZ exhibits a wide range of intraseasonal variability. Therefore, this study considers the errors in numerical weather forecasts in for two types of variations: 30–70 days and 10–30 days. The former is directly related to the MJO, while the latter is still intraseasonal but not necessarily linked to the 30–60-day oscillation. 2. MethodologyIn this study we use results from the recent (DERF) experiment concluded with the reanalysis version of the Forecast (MRF) model (Schemm et al. 1996; see also Jones et al. 1998). The present DERF experiment includes 50-day forecasts, initialized once a day (0000 UTC) with reanalyses fields, and covering the 5-yr period from 1 January 1985 to 31 December 1989. In the present version of the DERF forecast runs, the sea surface temperature (SST) is damped from its initial observed values to climatology using a 90-day e-folding time. The reanalysis version of the MRF model is identical to the NCEP global operational model of 1996, except the horizontal spectral resolution is reduced to T62 (equivalent to 210 km) (Kalnay et al. 1996). The NCEP–NCAR reanalysis fields at 0000 UTC are used for forecast verification and we limit our discussion to errors in 500-hPa geopotential height (Z500) in the austral summer (1 November–31 March; 1985–86, 1986–87, 1987–88, and 1988–89). Time filtering was applied to verification and forecast fields in order to compute anomalies from the annual cycle, which was determined by averaging the 5 yr of reanalysis and computing the first two harmonics. The annual cycle constructed with the mean and first two harmonics was then subtracted from the time series of Z500. No other filtering is applied to the forecasts and reanalysis.

Output: pass


Input: Claim: Battery technology advancements are crucial for storing renewable energy efficiently. [SEP] Text: The paper also compares the rainflow algorithm to a conventional cycle counting method to determine whether a simpler algorithm is appropriate to analyze the battery profile in the model. We observe that the two cycle counting methods predict a difference of 0. 044% in battery degradation for a time period of one year. This value being negligible, we conclude that the conventional cycle counting method can be adopted in the decision optimization model, thereby eliminating the computational difficulties involved with the rainflow algorithm. This work opens the possibility of a broad range of future work that builds on several different areas. Primarily, the number of time-steps in a horizon must be studied to determine the point of convergence in the objective function. Second, improvements can be made to reduce the computational time of the model for time horizons with larger numbers of time-steps. One of the dominant reasons for increased computational time is the presence of two-dimensional variables. One avenue for reducing the number of decision variables – and thereby the required computational time – is to prune the set (T^) associated with the two-dimensional variables. One method to accomplish this would be to reduce the duration of time over which a half-cycle can occur within a horizon. Rather than searching through every t,t^ combinations to locate a complete half-cycle, smaller windows of time (based on average half-cycle duration) may be searched. In addition to this, more work is being done to better characterize the non-linear nature of the battery degradation relationship. It is proposed that a multiplication factor be introduced to vary the slope of the degradation relationship as the number of cycles of use increase. This multiplier would be updated at every update period, providing a long-term adjustment to the rate of degradation. Finally, efforts can be taken to incorporate temperature into the battery degradation relationship. This requires an additional parameter that defines the degradation of the battery due to temperature at every time step and this could be added to the equation evaluating battery degradation. With the increased usage of renewable energy systems that are accompanied by energy storage, the necessity of battery optimization models has been greatly amplified. The results in this paper establish the effectiveness and the importance of such models in improving the life of the battery and ensuring its efficient use, in addition to maximizing profits. Conflict of are no conflicts of interest. datasets generated and supporting the findings of this 1. Denholm, P. , O’Connell, M. , Brinkman, G.

Output: corroborate


Input: Claim: Renewable energy reduces reliance on fossil fuels and helps decrease carbon emissions. [SEP] Text: This finding emphasizes the advantages of the CCU-based scenario, which could help to avoid drastic changes in demand-side technology, infrastructure, and lifestyle without dependence on CDR and bioenergy. The CCU scenario could reduce the lock-in and stranded asset risks of hydrocarbon-based energy demand technologies and infrastructure (e. g. , internal combustion engine vehicles and natural gas pipelines31–33). Second, despite moderate demand-side technological changes, the CCU scenario requires dramatic transformations in energy supply sectors, including the upscaling of hydrogen generation and non-biomass renewable energy supply mainly from solar and wind power; these transformations generally exceed the ranges of the AR6 scenarios. Moreover, the CCU scenario requires large-scale deployment of DAC, reaching approximately 10 Gt-CO2 per year. Despite the decreases in solar and wind costs in recent years, along with advances in DAC technology, additional technological developments and associated policy supports are needed to support the energy supply transformation in the CCU scenario. Third, the CCU scenario exhibits unique characteristics in terms of carbon flow associated with energy systems. Whereas the representative mitigation pathways in AR6 are generally characterized by decarbonization of energy systems through the reduction of energy demand or use of renewables (i. e. , carbon usage itself is diminished), the CCU scenario depends on hydrocarbon energy carriers; its dependence on fossil fuels and bioenergy is the smallest among the three scenarios. This characteristic may affect the commonly used concept of decarbonization because decarbonization is generally regarded as an emissions reduction approach almost equal to fossil fuel usage, whereas the energy system in our CCU scenario heavily relies on carbon that is released into the atmosphere. Fourth, the CCU scenario involves increases in mitigation costs that almost double energy system costs relative to other scenarios, mainly because of additional investment in synfuel supply. Although technological development and cost decreases in key energy technologies (e. g. , solar, wind, hydrogen, and synfuel production, as well as DAC) could contribute to the reduction of synfuel production costs, direct electrification is generally a more attractive option in terms of cost. The CCU scenario has multiple implications for future climate policies and mitigation scenario assessments. First, the model results indicate that CCU-oriented scenarios are possible solely under specific conditions that include limited availability of demand-side electrification, CDR, and bioenergy.

Output: contrast


Input: Claim: Microclimates can create variations in weather over short distances. [SEP] Text: Intraseasonal convective activity over the SACZ is selected according to the magnitude of OLR anomalies. Pentads of OLR were obtained from NCEP from 1–5 January 1979 through 27–31 December 1996. To obtain intraseasonal anomalies, two Lanczos band-pass filters with 49 weights were applied to the OLR pentads [see Jones et al. (2000) for details on time filtering]. Figure 1 shows the frequency responses of the two band-pass filters: 30–70 days and 10–30 days. The first band is denoted here as intraseasonal type 1 (IS1), while the second is intraseasonal type 2 (IS2). 3. ResultsBefore examining the skill of the MRF model over the SACZ region for each band of intraseasonal variations, it is instructive to discuss the mean summertime Z500 and the standard deviation fields (Fig. 2). The mean Z500 (top) indicates a midtroposphere maximum associated with the Bolivian high and strong horizontal gradients in subtropical and middle latitudes of South America. The total standard deviation in Z500 (bottom) shows, as expected, small magnitudes near the equatorial region and increasing variability toward the midlatitudes of the Southern Hemisphere. Anomaly correlations between Z500 forecasts and verification fields for all days during the four summer seasons were computed for 1–20 days lead time (Fig. 3). The MRF model shows higher skill (correlations greater than 0. 7 for 1 day lead time) in a narrow band over subtropical South America. Note that near the equatorial region the skill is less and progressively breaks down toward higher latitudes as the lead time increases. These results can be compared to the errors in the forecasts for each IS1 and IS2 band discussed next. Figure 4a displays the mean OLR during the austral summer and Fig. 4b shows the total standard deviation (i. e. , variations greater than two pentads and shorter than the seasonal average). Although the lowest values of OLR, indicative of strong tropical convection, are observed over the Amazon basin, the largest variability is found over eastern South America. The standard deviations of OLR anomalies (OLRA) in the 30–70 days and 10–30 days are shown in Figs.

Output: pass


Input: Claim: Renewable energy reduces reliance on fossil fuels and helps decrease carbon emissions. [SEP] Text: -19 PreprintsProtocolsVideosJournalsTools & a 10. 21203/rs. 3. rs-2050486/v1This work is licensed under a CC BY 4. 0 LicenseStatus:PostedVersion 1posted You are reading this latest preprint versionAbstractExisting climate change mitigation scenarios for attaining the 1. 5°C goal generally phase out hydrocarbon use through decarbonization of the energy system to eliminate fossil fuel usage. However, they require rapid changes in energy-demand technologies and human behaviors or extensive reliance on bioenergy for carbon dioxide removal (CDR). Carbon capture and utilization (CCU) presumably could support to alter such energy systems, considering recent technological advancements in direct air capture (DAC) and renewable-based hydrogen. Here, we developed a CCU-based scenario under the 1. 5°C climate goal using a global energy system model. In the scenario where CCU is mainly introduced by limiting demand-side electrification, bioenergy, and -based synthetic fuel reaches 30% of global energy demand to achieve net-zero emissions by 2050. In contrast to existing 1. 5°C scenarios, this CCU scenario is characterized by greater availability of hydrocarbons in energy demand, reaching 280 EJ in 2050, whereas fossil fuel and bioenergy supplies decrease to 60 EJ and 100 EJ, respectively. Despite moderate demand-side electrification, this scenario involves rapid upscaling of renewable energy and DAC, which reach 600 EJ and 10 Gt-CO2 per year by 2050, respectively. While the CCU-based scenario involves multiple challenges on energy supply transformation and associated energy investments, it provides an alternative mitigation option that considers the difficulties in rapid demand-side changes and the risks of bioenergy and CDR. Earth and environmental sciences/Environmental social sciences/Climate-change mitigationEarth and environmental sciences/Environmental social sciences/Climate-change policyEarth and environmental sciences/Environmental social sciences/Socioeconomic scenariosFiguresFigure 1Figure 2Figure 3Figure 4Main TextClimate change mitigation scenarios in the context of the 1. 5°C goal of the require accelerated emissions reductions or depend on large-scale net-negative emissions in the second half of this century1. (AR6)2 of the on (IPCC) presented a wide range of mitigation scenarios and corresponding future energy systems.

Output: corroborate


Input: Claim: Renewable energy reduces reliance on fossil fuels and helps decrease carbon emissions. [SEP] Text: Our finding is unsurprising since is one of the swiftest-budding economies in the universe. This economic expansion (which is energy-intensive growth) results in increased energy utilisation due to increased industrial activity, leading to a surge in CO2Es, making one of the leading CO2 dischargers globally. Main sectors in South Africa, such as manufacturing, construction, energy production, and mining, heavily depend on conventional energy sources, such as fossil fuels, for energy, which are responsible for higher CO2Es per unit of economic output. Infrastructure growth damages the environment's quality. Massive investment in infrastructure development, such as constructing roads and buildings, leads to higher carbon emissions in South Africa. Economic growth has increased the transport requirements in since the mobility of goods and people has increased. Airplanes, ships, and vehicles burn fossil fuels and emit CO2. Additionally, South Africa's economic growth-driven consumption patterns are characterized by high energy consumption levels and high carbon emissions. Further, due to the need for more strict environmental regulations and the expansion of export-oriented industries that prioritise production efficiency and competitiveness over environmental considerations, economic growth is continuing unrestrained, leading to higher carbon emissions. South Africa’s high CO2Es are primarily a result of its dependence on coal energy for electricity production and other industrial processes. It has been indicated that, in 2022, produced more than 400 million metric tons of CO2Es from fossil fuel burning and industrial purposes. Even though renewable energy sources have been expanding, swift economic growth has outpaced the transition to cleaner production sources in South Africa, resulting in a net increase in CO2Es. Nevertheless, our discovery contradicts the findings of Mujtaba et al. (2022), Mehmood (2021), and Mehmood and Mansoor (2021), who discovered that economic growth reduces CO2Es. Expectedly, the research outcomes show that fossil energy consumption and CO2Es are positively associated. The implication here is that fossil energy utilisation in is an obstruction to environmental sustainability. South Africa’s energy sources depend heavily on fossil fuels (mainly coal and oil), which causes environmental damage. Additionally, coal-powered electricity manufacturing plants supply electricity to South Africa's energy-intensive heavy industries. As these industries modernise and expand, their demand for fossil energy rises, resulting in higher carbon emissions.

Output: contrast


Input: Claim: Microclimates can create variations in weather over short distances. [SEP] Text: 4c,d respectively, and indicate that intraseasonal variations account for a large part of the total variance (Liebmann et al. 1999). Based on the spatial pattern of the OLRA variability, we defined a base region (solid lines) in which the magnitudes of the OLRA were used to characterize periods of weak (strong) SACZ. The extent of the base region is chosen to represent the mean position of the SACZ. A time series of 30–70-day OLRA spatially averaged in the base region was constructed and periods above (below) 0. 8 (−0. 8) standard deviation during the austral summer were selected. The weak events are denoted IS1 positive (IS1 pos), while the strong are IS1 negative (IS1 neg). In addition, periods of very inactive SACZ were selected when the magnitudes of the 30–70-day OLRA values in the base region were between ±0. 3 standard deviation (IS1 null). Likewise, a time series of 10–30-day OLRA was constructed over the base region. Periods of weak IS2 variability were selected when OLRA are above 0. 8 standard deviation (IS2 pos); strong when OLRA are below −0. 8 standard deviation (IS2 neg); and null events when OLRA are between ±0. 3 standard deviation (IS2 null). The frequency distributions of the selected IS1 and IS2 events per season are plotted in Fig. 5. For the IS1 type (top) there are a total of 27 (IS1 pos), 29 (IS1 neg), and 32 (IS1 null) pentads, which represent 11, 11, and 20 independent events, respectively. Similarly, for IS2 type there are 23 (IS2 pos), 23 (IS2 neg), and 34 (IS2 null) pentads, so that 20, 19, and 22 are independent events. We consider independent events nonconsecutive pentads. The skill of the MRF model in forecasting Z500 was examined for each sample of intraseasonal variability defined above (IS1 and IS2). Two skill measures, anomaly correlation and root-mean-square (rms) error, were computed for samples IS1 pos, IS1 neg, IS1 null, IS2 pos, IS2 neg, and IS2 null.

Output: pass


Input: Claim: Renewable energy reduces reliance on fossil fuels and helps decrease carbon emissions. [SEP] Text: To interpret these scenarios, Pathways (IMPs) were described, which represent multiple pathways to achieve net-zero CO2 emissions. These pathways are generally characterized by decarbonization of energy systems, reduction of energy demand3,4, promotion of renewable energy sources and electrification5 or the offsetting of residual emissions through carbon dioxide removal (CDR) processes (e. g. , bioenergy carbon capture and storage [BECCS] and afforestation)6,7. In addition to the IMPs, various unique low-emission pathways have recently been analyzed, such as CDR-oriented scenarios based on direct air carbon capture and storage (DACCS)8,9 and degrowth scenarios10. Because each transformation pathway generally involves multiple risks associated with sustainability11,12, and rapid changes in energy systems are hindered by feasibility concerns 13,14, there is a need for exploration of a diverse portfolio of net-zero emissions pathways during the development of post-AR6 mitigation scenarios to facilitate the achievement of robust and flexible net-zero energy systems. In this context, carbon capture and utilization (CCU) is a promising alternative to the reduction of residual emissions15–18. Synthetic hydrocarbon fuels (synfuels) derived from carbon-neutral sources (e. g. , direct air capture [DAC], biomass, and low-carbon hydrogen) can provide low-carbon liquefied and gaseous energies. These energy carriers have advantages in terms of energy density relative to other energy carriers; they could be used with existing energy infrastructure and technology. Because of recent cost decreases in solar and wind power19,20 and increasing expectations for the commercialization of DAC21,22 to facilitate large-scale CCU implementation, the potential applications of these carriers have been explored in specific sectors23,24 (e. g. , transport fuels) and regions25,26 using Models. Nevertheless, their economy-wide potential and the challenges associated with achievement of stringent mitigation goals, such as 1. 5°C without overshoot, remain unclear. Here, we performed what-if scenario analysis to explore the potential of implementing CCU in the global energy system using the AIM/Technology (Asia-Model/Technology)27 model, which is a bottom-up global energy system model that accurately represents energy technologies in the energy demand and supply sectors.

Output: corroborate


Input: Claim: Renewable energy reduces reliance on fossil fuels and helps decrease carbon emissions. [SEP] Text: Unexpectedly, technological innovation has a significant positive association with CO2Es. This discovery is congruent with the findings of Alnafisah et al. (2024), Chen et al. (2024), Lee et al. (2024), Mehmood et al. (2024), Rahman and Ferdaous (2024), Rahman et al. (2024), Wang et al. (2024), Chaudhry (2023), Li et al. (2022), Villanthenkodath and Mahalik (2022), Rahman and Alam (2022), Xiaoyang et al. (2022), Dauda et al. (2021), and Khattak et al. (2020). Even though this discovery is against our intuition, it can be explained. is Africa's most technologically advanced, industrialised, and diversified country. Technological innovations in its heavy industrial sector increase industrial activity and production, leading to higher CO2Es. Even though has adopted some technologies to reduce CO2Es, it still relies on coal for electricity production. Technological innovations in coal-powered electricity plants (e. g. , carbon capture and storage) prolong dependence on coal, resulting in sustained CO2Es. In the transport sector, technological innovations (e. g. , the production of fuel-efficient vehicles) have resulted in increased mobility and transportation activity, and in construction and infrastructure development, technological innovations have promoted urbanisation and construction activity, leading to higher energy consumption and CO2Es. is rich in natural resources (e. g. , minerals), which are at the centre of technological innovations. Nevertheless, resource processing and extraction require much energy, resulting in higher CO2Es, mainly if fossil fuels are used. Technological innovations in electrical devices have increased electricity demand. This electricity is mainly generated from fossil fuel-powered electricity generation plants, leading to higher CO2Es. Technological advancement also results in the manifestation of wastes that are harmful to the environment. Our result can be an indication that most patent applications in have nothing to do with clean energy technology or energy-saving technology that can reduce CO2Es. That is to say, less attention is given to eco-friendly innovation. is chasing economic growth but must care more about the environment. Clean innovation is not a vital resolve of the country.

Output: contrast


Input: Claim: Microclimates can create variations in weather over short distances. [SEP] Text: The statistics were then averaged over the SACZ base region indicated in Fig. 4 (solid lines) and over the region defined by (40°S–20°N; 90°–20°W). Figure 6 shows the anomaly correlation averaged over the SACZ domain (top) and over the domain (bottom). The anomaly correlation during IS1 averaged over the SACZ domain (top left) indicates that the correlations start at about the same values (0. 65) for IS1 pos, IS1 neg, and IS1 null for 1-day lead time. Note that these values are averaged over the SACZ domain and the MRF model shows higher skill farther south (see Fig. 3). However, as forecast time progresses, the anomaly correlations for IS1 null are systematically smaller than IS1 pos and IS1 neg. Correlations of 0. 3 persist up to 10 days lead time for IS1 pos and IS1 neg. The anomaly correlations during IS2 averaged over the SACZ (top right) show similar magnitude (0. 65) for 1 day lead time. In contrast, as lead time increases, the anomaly correlations for IS2 pos and IS2 neg are smaller than for IS2 null. In particular, during periods of strong convective activity on 10–30 days (IS2 neg) forecasts are less skillful. The different behavior of the MRF forecasts during IS1 and IS2 periods are still noticeable in the averages over the domain (bottom right and left), although the differences in the magnitudes of the anomaly correlations among positive, negative, and null cases are significantly smaller. However, the differences in the magnitudes of the correlation coefficients do not pass the 95% significance level. The rms errors for the IS1 type averaged over the SACZ region (Fig. 7; top left) show that the MRF model has more skill in predicting Z500 during periods of strong convective activity (IS1 neg) as opposed to periods of suppressed (IS1 pos) or weak (IS1 null) activity. In addition, the growth rate of rms errors during IS1 pos is larger than in other periods. These results are consistent with the results of NPMP. In contrast, the rms errors for the IS2 type seem to exhibit a different behavior (Fig. 7; top right).

Output: pass


Input: Claim: Renewable energy reduces reliance on fossil fuels and helps decrease carbon emissions. [SEP] Text: The model includes synthetic liquid fuels and synthetic methane production technologies such as CCU-based energy carriers, which can replace fossil fuel-based liquids and gases in energy demand sectors. Further details concerning the model structure and technological assumptions are provided in the Methods section. We analyzed three climate change mitigation scenarios to compare the roles and characteristics of CCU in residual emissions. First, as the central scenario in this study, a CCU-oriented scenario (1. 5C-CCU) was designed that avoids rapid technological changes in energy demand sectors (e. g. , moderate electrification). Furthermore, dependence on fossil fuels and bioenergy is reduced in a manner similar to the IMPs established to meet the 1. 5°C goal of AR6. Consequently, hydrocarbon-based energy is provided by alternative sources derived from atmospheric carbon, which is converted into synthetic fuels. This scenario has clear advantages in that it does not require drastic changes in energy demand structure and lifestyle; it uses current energy supply infrastructure without dependence on CDR. In contrast to 1. 5C-CCU, the other two scenarios phase out hydrocarbons from energy systems (regardless of source) or use CDR implementation to offset residual emissions. The second scenario is 1. 5C-DEC (DECarbonization), which involves decarbonization of energy systems to reduce residual emissions; this scenario includes demand-side electrification and the upscaling of solar and wind power without dependence on large-scale CDR or bioenergy, and the underlying assumptions are similar to the IMP-Ren scenario in AR6. The third scenario, 1. 5C-CDR, is characterized by extensive use of CDR options similar to the IMP-Neg scenario in AR6, whereby residual emissions from fossil fuel use persist. The quantification of each scenario was implemented in the AIM/Technology model with multiple energy system conditions, in accordance with the underlying scenario. In the 1. 5C-CCU scenario, considering the assumptions of low dependence on fossil fuels and bioenergy, constraints were imposed on CDR and CCS implementation, as well as bioenergy potential, because reliance on fossil fuels is generally determined by CDR availability28. Furthermore, based on moderate assumptions regarding technological changes in energy demand sectors, the diffusion of non-hydrocarbon-based technologies was limited, including the use of electricity- and hydrogen-based devices (see Methods for details).

Output: corroborate


Input: Claim: Renewable energy reduces reliance on fossil fuels and helps decrease carbon emissions. [SEP] Text: Similarly, Tajudeen (2015) indicated that African economies need more funds to enforce greener technologies and effective regulations to decrease CO2Es. On the other hand, Bekun (2024), Dunyo et al. (2024), Ibrahim et al. (2024), Kharb et al. (2024), Li et al. (2024), Rahman and Ferdaous (2024), Wang et al. (2024), Raihan et al. (2023g), Raihan et al. (2022f), Raihan and Tuspekova (2022k), Khan et al. (2019), Mensah et al. (2018), and Yii and Geetha (2017) discovered that innovation reduces CO2Es. Our assessment shows that globalisation has a positive coefficient, indicating that CO2Es rise as globalisation increases. This indicates that globalisation has harmful environmental consequences in South Africa. Our discovery is supported by Elboghdadly (2024), Gyamfi et al. (2024), Kharb et al. (2024), Nadiri et al. (2024), Teklie and Yağmur (2024), Alam et al. (2023), Sultana et al. (2023), Xu et al. (2023), Rahman and Alam (2022), Wen et al. (2021), Khan et al. (2019), and Shahbaz et al. (2015). Globalisation results in economic growth via trade and investment flow expansion between economies. This economic growth through trade and investment flows can increase CO2Es. International trade results in increased transportation of people and goods across borders. partakes in global supply chains, exporting and importing goods via planes, ships, trains, and trucks. Since fossil fuels are used as the primary energy source, CO2Es are rising. Globalisation increases trade liberalisation and trade-linked activities, and since most services and goods produced in the economy need energy, usually fossil energy, CO2Es increase.

Output: contrast


Input: Claim: Microclimates can create variations in weather over short distances. [SEP] Text: The MRF model has less skill in forecasting Z500 during periods of strong (IS2 neg) and suppressed (IS2 pos) convection than in periods of weak (IS2 null) activity. In particular, the growth rate of rms error during IS2 pos is larger than for IS1 neg. A somewhat similar behavior is observed when the rms errors for IS1 and IS2 are averaged over the domain (Fig. 7; bottom left and right, respectively). 4. Concluding remarksThe summer season in exhibits strong convective activity over the Amazon basin and SACZ, with significant intraseasonal variability found in the eastern part of the continent. The recent study of NPMP showed that intraseasonal variations in the 10–90 days band impact the skill of numerical weather forecasts over the PAN region. In particular, their results pointed out that initial forecast errors during periods of weak convective activity over the SACZ are smaller than for strong events. As is discussed in previous studies, intraseasonal variability over the SACZ is modulated by the MJO as well as by shorter period variations (10–30 days). The preliminary results discussed here indicate that the MRF model has more skill (i. e. , in terms of rms errors) during periods of strong convective activity associated with the MJO as opposed to periods of suppressed or weak activity. In contrast, the MRF model has less skill during periods of strong or suppressed convection associated with 10–30 days variations. Additional investigation in this research area should be useful to understand how remotely forced intraseasonal variability over the SACZ can impact local forecasts. This is especially important since the climatological position of the SACZ coincides with the densest demographic region in South America. 3555. Horel, J. D. , A. N. Hahmann, and J. E. Geisler, 1989: An investigation of the annual cycle of convective activity over the tropical Americas. J. Climate,2, 1388–1403. Jones, C. , 1990: An investigation of low-frequency variability of the large-scale circulation over South America. M. S. thesis, Department of Meteorology, University of Utah, 108 pp. ——, and J. D. Horel, 1991: High circulation and convective activity over (in Portuguese). Rev.

Output: pass


Input: Claim: Renewable energy reduces reliance on fossil fuels and helps decrease carbon emissions. [SEP] Text: Across these three scenarios, the emission pathways were equivalent to the 1. 5°C scenario without overshoot and to the C1 category in the 62, whereby CO2 emissions from energy sectors decreased to nearly net-zero status by 20501,29. We also performed sensitivity scenario analyses, as summarized in 1. Details of the scenario settings are provided in the Methods section. Technological changes in energy demand sectors In the 1. 5C-CCU scenario, CCU-based energy carriers, including synthetic liquid fuels and methane, supply approximately 30% of global energy demand by 2050, compared with approximately 5% and nearly zero in the 1. 5C-DEC and 1. 5C-CDR scenarios, respectively (Fig. 1a, 2). In contrast, the electrification rate is approximately 38% in the 1. 5C-CCU scenario, which is a unique characteristic of the 1. 5C-CCU scenario compared with the 1. 5C-DEC and 1. 5C-CDR scenarios, where electrification is approximately 50%. This unique trend is robust across all sensitivity scenarios of 1. 5C-CCU summarized in Supplementary Fig. 1. Because of the limited electrification level in the 1. 5C-CCU scenario, the share of hydrocarbon-based energies, including fossil fuels, biomass and synfuels, is 54% of the total final energy demand, whereas those shares in the 1. 5C-DEC and 1. 5C-CDR scenarios are only 29% and 41%, respectively. This unique trend is also present in the comparison with AR6 scenarios because hydrocarbon consumption in the 1. 5C-CCU scenario is above the 90th percentile range of category C1 scenarios. Figure 1b summarizes sectoral energy demand composition and the shares of electricity, synfuels, and hydrocarbon-based energies in 2050. Across all scenarios, the transport sector was the greatest consumer of synfuels, which replaced fossil fuel-based liquid fuels. In the 1. 5C-CCU scenario, the share of synfuels reaches nearly 60% of total transport energy demand in 2050. In the industry and buildings sectors, CCU-based energy carriers are mainly supplied as gaseous synthetic methane in the 1. 5C-CCU scenario, where they supply approximately 22% and 13% of sectoral energy demand, respectively. Figure 1c and Supplementary Fig.

Output: corroborate


Input: Claim: All countries can transition to renewable energy without economic impacts. [SEP] Text: -19 PreprintsProtocolsVideosJournalsTools & a - 10. 21203/rs. 3. rs-79754/v1This work is licensed under a CC BY 4. 0 LicenseStatus:PostedVersion 1posted You are reading this latest preprint versionAbstractChina is the world’s largest greenhouse gas (GHG) emitter, but declining wind and solar energy costs present opportunities to transform its electric power sector. In 2017, China launched a national emission trading scheme (ETS). Evidence to date suggests that the ETS mitigates CO2 emissions and promotes renewable energy deployment but constrains economic growth. These studies, however, do not account comprehensively for economic impacts. Ours is the first to account for three multiplier effects—shifting consumption patterns, job growth with elastic labor supply, and higher total factor productivity (TFP)—when modeling accelerated renewable electricity growth with the ETS in China. Results from a detailed economic forecasting model show low renewable energy costs interacting with the ETS to slash GHG emissions while directly stimulating incremental net positive economic growth by 2030, compared with a business-as-usual scenario that assumes slower renewable cost reductions and no ETS. Accounting for the multiplier effects reveals larger potential benefits, including up to 15. 6% of additional GDP growth (over business as usual) by 2030 when shifting consumption patterns, job growth with elastic labor supply, and higher TFP are all considered. These results suggest that China should accelerate its clean energy transition, not only for the air-quality and climate benefits, but also for the broad and positive impact on innovation, employment, and economic growth. 1Figure 2Figure 3Figure 4BackgroundThe People’s Republic of China is the world's largest emitter of greenhouse gases (GHGs), 44% (4. 14 billion metric tons of CO2) of which came from the electric power system in 2017. 1 According to its Contribution to the on Climate Change, the Chinese government pledged to lower CO2 emissions per unit of GDP by 60%–65% relative to 2005 emissions, increasing the share of non-fossil fuels in primary energy consumption to around 20%2 by 2030. Accelerating the replacement of traditional energy with renewable substitutes would help transform the power system and reduce carbon emissions.

Output: contrast


Input: Claim: Microclimates can create variations in weather over short distances. [SEP] Text: Brasileira de Meteorologia,5-1, 379–387. ——, D. E. Waliser, J. E. Schemm, and W. K. Lau, 2000: Prediction skill of the Madden and in dynamical extended range weather forecasts. Climate Dyn. , in press. Kalnay, E. , and Coauthors, 1996: NCEP/NCAR 40-Project. Bull. Amer. Meteor. Soc. ,77, 437–471. Lenters, J. D. , and K. H. Cook, 1999: Summertime precipitation variability over South America: Role of the large-scale circulation. Mon. Wea. Rev. ,127, 409–431. Liebmann, B. , G. N. Kiladis, J. A. Marengo, T. Ambrizzi, J. D. Glick, 1999: Submonthly convective variability over and the Convergence Zone. J. Climate,12, 1877–1891. Madden, R. A. , and P. R. Julian, 1994: Observations of the 40–50 day tropical oscillation: A review. Mon. Wea. Rev. ,122, 814–837. Nogues-Paegle J. , K. Mo, and J. Paegle, 1998: Predictability of the NCEP/NCAR reanalysis model during austral summer. Mon. Wea. Rev. ,126, 3135–3152. Schemm, J. E. , H. Van den Dool, and S. Saha, 1996: A Multi-at NCEP. Preprints 11th Conf. on. Meteor. Soc. , 47–49. Size Fig. 1. Frequency response of Lanczos band-pass filters. The 30–70-day band is denoted intraseasonal type 1 (IS1), while variations of 10–30 days are designated intraseasonal type 2 (IS2). Citation: Review 128, 2; 10. 1175/1520-0493(2000)128<0486:>2. 0. CO;2Download FigureDownload figure as PowerPoint slide Size Fig. 2. Mean geopotential height at 500 hPa (Z500) during austral summer (top).

Output: pass


Input: Claim: Renewable energy reduces reliance on fossil fuels and helps decrease carbon emissions. [SEP] Text: 2 illustrate the diffusion rates of non-hydrocarbon-based technologies in terms of existing stock capacity. In all energy demand sectors, technological change is moderate in the 1. 5C-CCU scenario, particularly relative to the 1. 5C-DEC scenario, which requires drastic technological change to support rapid electrification. Energy supply and transformation associated with synfuel production Figure 2a shows a secondary energy flow diagram associated with synfuel production in 2050. In the 1. 5C-CCU scenario, electricity generation increases to nearly 700 EJ per year by 2050 to meet the energy demand for synfuel production, as well as the energy demands for direct electrification and hydrogen. Substantial energy losses occur between secondary and final energy conversion processes in the 1. 5C-CCU scenario. The main factor that drives such losses is electrolysis in the synfuel production process, which requires approximately 40% of generated electricity, along with other energy transformation losses, including transmission, storage losses, and curtailment (Supplementary Fig. 3). In the 1. 5C-CCU scenario, electricity consumption for DAC uses approximately 10% of the total electricity generated; CO2 capture via DAC reaches approximately 10 Gt-CO2 per year by 2050, mainly because of CCU demands (Fig. 3a). Considering the requirement for low-carbon electricity, the 1. 5C-CCU scenario poses multiple challenges to energy supply sectors despite moderate technological changes in energy demand sectors. First, the 1. 5C-CCU scenario is characterized by large-scale hydrogen production, reaching approximately 250 EJ per year by 2050, which includes hydrogen for synfuel production and the direct use of hydrogen, as well as hydrogen in the form of ammonia (Fig. 2b, Supplementary Fig. 4). In contrast, hydrogen production reaches approximately 40 EJ and 100 EJ per year by 2050 in the 1. 5C-CDR and 1. 5C-DEC scenarios, respectively; these values are similar to or greater than range in the AR6 scenario. Second, the 1. 5C-CCU scenario includes massive non-biomass renewable energy supply that mainly depends on solar and wind, which exceed 600 EJ per year by 2050 in the 1. 5C-CCU scenario (Fig. 2c, Supplementary Fig. 5); fossil fuel and biomass supplies are limited to approximately 60 EJ and 100 EJ per year, respectively (Supplementary Fig.

Output: corroborate


Input: Claim: All countries can transition to renewable energy without economic impacts. [SEP] Text: China has already demonstrated rapid growth in renewable electricity generation, which increased from 736 TWh (17% of total electricity generation) to 1,931 TWh (26%) between 2010 and 2019. 3 Renewable energy costs have experienced an unprecedented decline. Over the past decade, the global weighted-average levelized cost of electricity has fallen by 82% for photovoltaics (PV), 38% for onshore wind, and 84% for battery storage. 4-7 Even without accounting for the social cost of carbon, all-inclusive costs of electricity from new PV and onshore wind are now below the marginal operating costs of an increasing number of existing coal-fired powerplants. 5,6 In market economies, this crossover cost trend will support autonomous power system transformation, reduce fossil fuel dependence and related environmental impacts,8-10 and reduce wholesale and retail electricity costs. 11 As a result, new opportunities will arise for cutting consumer and manufacturer energy costs, improving human welfare, and boosting economic growth. The computable general equilibrium (CGE) model is an important tool for studying these macroeconomic impacts. The progress of energy technology in the CGE model is generally characterized by homogeneous autonomous energy efficiency improvement (AEEI) across the economy12,13 or average AEEIs for different energy-consuming sectors,14,15 but neither approach distinguishes technological improvement for different energy sources. Huang et al. (2019) use an exogenously specified mix of fossil and renewable energy as a baseline to describe the process of renewable energy substitution along China’s future low-carbon development pathway. 16 However, the exogenous generation mix does not capture the recent cost crossover—with the cost of renewables dropping below the cost of coal power—nor does it evaluate the full economic impacts of technology improvement. Therefore, there is a need for studies that consider both the plummeting renewable energy costs and the associated economic and social impacts. Carbon emission trading scheme (ETS) is among the most potentially impactful approaches to driving the renewable transition. In 2011, China launched a pilot ETS covering seven leading provinces and cities including and Hubei. By the middle of 2019, the cumulative trading volume of the pilot ETS covered 330 million metric tons of CO2 equivalent emissions, with a cumulative transaction value of 7. 11 billion RMB. 17 2017, China officially launched its national ETS, and electric power was the first industry included in the system.

Output: contrast


Input: Claim: Microclimates can create variations in weather over short distances. [SEP] Text: Contour interval is 20 m. Total standard deviation of Z500 (bottom). Contour interval is 5 m. Citation: Review 128, 2; 10. 1175/1520-0493(2000)128<0486:>2. 0. CO;2Download FigureDownload figure as PowerPoint slide Size Fig. 3. Anomaly correlation between Z500 forecasts and verification fields. Skill is computed considering all days from the four austral summers during the DERF period. Skill is shown for 1, 4, 6, 8, and 11 days lead time. Correlations greater than 0. 50 are indicated by dark shading. Citation: Review 128, 2; 10. 1175/1520-0493(2000)128<0486:>2. 0. CO;2Download FigureDownload figure as PowerPoint slide Size Fig. 4. (a) Mean outgoing longwave radiation (OLR) during austral summer (1 November–31 March). Contour interval is 5 W m−2 and shading indicates OLR is less than 220 W m−2. (b) Total OLR standard deviation during austral summer. Contour interval is 2. 5 W m−2 and shading indicates standard deviation greater than 20 W m−2. (c) Standard deviation of OLR anomalies for the IS1 type (30–70 days). Contour interval is 1. 0 W m−2 and shading indicates standard deviation greater than 10 W m−2. (d) Standard deviation of OLR anomalies for the IS2 type (10–30 days). Contour interval is 1. 0 W m−2 and shading indicates standard deviation greater than 15 W m−2. Region indicated with thick solid lines is used to characterize periods of strong (weak) convective activity over the (SACZ). Citation: Review 128, 2; 10. 1175/1520-0493(2000)128<0486:>2. 0. CO;2Download FigureDownload figure as PowerPoint slide Size Fig. 5. Distribution of events (in pentads) selected according to the type of intraseasonal variability: 30–70 days (IS1; top) and 10–30 days (IS2; bottom). Events are selected when the magnitudes of the OLR anomalies averaged over the SACZ domain are less than 0.

Output: pass


Input: Claim: Renewable energy reduces reliance on fossil fuels and helps decrease carbon emissions. [SEP] Text: 6). The 1. 5C-CCU scenario is characterized by substantial availability of hydrocarbon-based energy for energy demand sectors, along with low dependence on fossil fuels. Conventional mitigation scenarios show a clear correlation of primary energy supply from fossil fuels and biomass with the consumption of hydrocarbon-based energy carriers (Fig. 2d). All AR6 scenarios, including IMPs, 1. 5C-DEC, and 1. 5C-CDR, show similar trends because hydrocarbon-based final energy carriers are generally supplied by fossil fuels or biomass. In the 1. 5C-CCU scenario, hydrocarbon-based energy usage in energy demand sectors reaches approximately 280 EJ per year, despite the reduction of fossil fuel and biomass primary energy supply to approximately 160 EJ by 2050, because DAC is a major carbon source for the energy system in this scenario. Carbon sources and utilization in energy systems The 1. 5C-CCU scenario requires CO2 capture from the atmosphere via DAC, reaching approximately 10 Gt-CO2 per year by 2050; the 1. 5C-DEC requires approximately 1 Gt-CO2 and the AR6 scenarios require < 2 Gt-CO2 (Fig. 3, Supplementary Fig. 7). In contrast to the extensive reliance on DAC, the implementation of CDR and CCS is lower in the 1. 5C-CCU scenario than in the 1. 5C-DEC, 1. 5C-CDR, and AR6 scenarios. CDR implementation is limited to 1 Gt-CO2 per year in the 1. 5C-CCU scenario, whereas it reaches 2. 3 and 6. 5 Gt-CO2 per year by 2050 in the 1. 5C-DEC and 1. 5C-CDR scenarios, respectively (Fig. 3b). CCS implementation, specifically geological storage, reaches approximately 3. 4 Gt-CO2 per year by 2050 in the 1. 5C-CCU scenario, even when CCS requirements for industrial process emissions are included; this value is lower than the values in the 1. 5C-DEC and 1. 5C-CDR scenarios, as well as the values in most AR6 scenarios (Fig. 3c). Figure 3d is a diagram of carbon flow in 2050 associated with energy systems; it includes DACCS, which is normally considered outside of the energy system.

Output: corroborate


Input: Claim: All countries can transition to renewable energy without economic impacts. [SEP] Text: 18 To date, the research literature suggests that China’s ETS mitigates CO2 emissions and promotes renewable energy deployment but hinders the economy—although there is significant disagreement about the magnitude and direction of net economic impacts. Chen et al. (2020) find that a 1% carbon emission reduction would reduce GDP by 0. 06%,19 whereas Zhang et al. (2018) indicate that the ETS could reduce carbon intensity by 20% without GDP loss. 20 Most studies show a negative net impact on the economy due to the ETS, ranging from 0. 08% to 5. 61% of real GDP12,21 and depending on carbon pricing,22 the allowance allocation mechanism,15,23-25 revenue redistribution methods,16,26 government fines,27 and coverage considered. 13,14,28 The price-directed nature of the ETS still confers efficiency gains compared with mandated emission caps. 29,30 However, few studies focus on the comprehensive impact of renewable technology promotion and the ETS in the context of the renewable energy cost crossover. Further, the existing literature does not account for three critical multiplier effects that could magnify the impacts of accelerating China’s renewable electricity transition through technology improvement and the ETS. First, households can be expected to divert the electricity cost savings that result from rapidly declining renewable energy costs toward other expenditures, promoting broad-based economic growth from the demand side. Second, because China generally has a higher elasticity of labor supply compared with developed countries, this more accommodating labor supply will amplify economic gains due to the expenditure stimulus, particularly in the long term. Third, increasing renewable energy use may increase regional total factor productivity (TFP), supporting the anecdotal narrative of a virtuous productivity cycle linking renewable energy deployment to other technological innovation; although the exact mechanisms are difficult to disentangle, studies in diverse settings suggest a positive linkage. 31-33 Tugcu (2013) estimates long-run (short-run) growth elasticities are -2. 1 (-1. 7) and 0. 8 (0. 7) for fossil and renewable consumption, respectively. 34 Yan et al. (2020) study the relationship between renewable energy technology innovations and China’s green productivity growth, finding a significant effect in provinces where income is above a certain threshold.

Output: contrast


Input: Claim: Microclimates can create variations in weather over short distances. [SEP] Text: 8 standard deviation (IS1 neg; IS2 neg), greater than 0. 8 standard deviation (IS1 pos; IS2 pos) and between ±0. 3 standard deviation (IS1 null; IS2 null). Numbers of events are displayed for each austral summer (S 85–86; S 86–87; S 87–88; S 88–89). Total number of pentads for each type of intraseasonal variability is indicated in parenthesis. Citation: Review 128, 2; 10. 1175/1520-0493(2000)128<0486:>2. 0. CO;2Download FigureDownload figure as PowerPoint slide Size Fig. 6. Anomaly correlation between forecasts of Z500 and reanalysis averaged over the SACZ (top) and region (40°S–20°N; 90°–20°W) (bottom). Plots on the left are for IS1 type, while plots on the right are for IS2 type. Dashed, dotted, and solid lines indicate positive, negative, and null cases, respectively. Horizontal axis indicates lead time in days. Citation: Review 128, 2; 10. 1175/1520-0493(2000)128<0486:>2. 0. CO;2Download FigureDownload figure as PowerPoint slide Size Fig. 7. Root-mean-square (rms) errors between forecasts of Z500 and reanalysis averaged over the SACZ (top) and region (40°S–20°N; 90°–20°W) (bottom). Plots on the left are for IS1 type, while plots on the right are for IS2 type. Dashed, dotted, and solid lines indicate positive, negative, and null cases, respectively. Horizontal axis indicates lead time in days. Citation: Review 128, 2; 10. 1175/1520-0493(2000)128<0486:>2. 0. CO;2Download FigureDownload figure as PowerPoint slide this content Copy this link, or click below to email it to a friend Email this content or copy the link directly: The link was not copied. Your current browser may not support copying via this button. Link copied successfullyCopy link Share on facebook Share on linkedin Share on twitter Abstract1. Introduction2. Methodology3. Results4. Concluding remarks 3555. Horel, J.

Output: pass


Input: Claim: Renewable energy reduces reliance on fossil fuels and helps decrease carbon emissions. [SEP] Text: Although the mean production cost of electricity gradually decreases to approximately 15 US$ GJ− 1 in 2050 because of decreases in renewable costs, the cost of synfuels is approximately fourfold greater than the cost of electricity; it reaches approximately 60 US$ GJ− 1 by 2050 (Fig. 4c). According to the synfuel cost composition shown in the right panel of Fig. 4c, increases in synfuel cost are mainly driven by the hydrogen supply cost, which constitutes approximately two-thirds of synfuel costs because of the energy losses illustrated in Fig. 2a. Even under the AdvTech assumption, synfuel production cost exceeds 40 US$ GJ− 1 in 2050; thus, it is much more costly than hydrogen and electricity. The 1. 5C-CCU scenario shows unique characteristics in terms of sectoral energy investment, although all scenarios decrease investments in other energy supply, including fossil fuel extraction, relative to the no-policy scenario (Fig. 4d). In the 1. 5C-CCU scenario, investments in energy demand sectors in 2041–2050 are lower than in 1. 5C-DEC because of moderate energy demand technological changes; however, total energy investment reaches approximately 7 trillion US$ per year because of the upscaling of electricity, hydrogen, and CO2 capture via DAC. The regional investments illustrated in Supplementary Fig. 9c reveal similar trends among regions, although those trends are stronger in the OECD and Asian regions under 1. 5C-CCU than under other scenarios because of their high demand for synfuels (Supplementary Fig. 10). DiscussionWe assessed three illustrative scenarios for achieving net-zero CO2 emissions. In contrast to the 1. 5C-DEC, 1. 5C-CDR, and AR6 scenarios, where residual emissions are generally reduced through energy system decarbonization or CDR, the 1. 5C-CCU scenario exhibits distinct characteristics, which are summarized in Table 1. Here, we discuss these characteristics in greater detail. First, the CCU scenario makes hydrocarbon-based energy carriers available to energy demand sectors; this has the advantage of increasing energy density while lowering dependence on fossil fuels and bioenergy. Because most synfuels are derived from renewable-based hydrogen and carbon captured via DAC in this scenario, synfuels can be considered carbon-neutral hydrocarbons that reduce residual CO2 emissions in the energy sector.

Output: corroborate


Input: Claim: All countries can transition to renewable energy without economic impacts. [SEP] Text: 35 Ours is the first study to account for the economic impacts of shifting consumption patterns, job growth in the context of elastic labor supply, and higher TFP when simulating accelerated renewable electricity growth with the ETS in China. We employ a dynamic recursive CGE model with a diverse portfolio of electric power technologies to evaluate scenarios designed for illuminating each of the three multiplier effects. Those scenarios are described in the next section, followed by our results and conclusions. section details our analytical approach. We evaluate six scenarios (Table 1). In the business as usual (BAU) scenario, the productivity of renewables (wind, solar, and electricity storage) is assumed to follow historical trends (He et al. 2020),11 with sustained but moderate cost reductions into the future. In the low-cost renewable scenario (R), more rapid productivity growth in renewables continues in alignment with recent indications, simulating how lower renewable energy costs provide direct stimulus to the economy. In the carbon constraint scenario (C50), total emissions from the power sector by 2030 are limited to 50% below the 2015 level by instituting an ETS for this sector. Table 1: productivity improvements in renewables R Rapid productivity improvements in renewables C50 R + ETS limiting CO2 emissions from the power sector to 50% of the 2015 emissions level Keynes C50 + shifting expenditure from energy savings EMP Keynes + more accommodating labor market + energy productivity spillovers (1% TFP growth) Our other three scenarios examine the multiplier economic impacts of renewable innovation and the ETS. scenario builds on the C50 scenario to elucidate the effects of demand-side aggregate stimulus resulting from energy cost savings due to renewable technology innovation. Previous economic assessments of renewable energy have focused on three component impacts: investment in technology production, technology purchasing, and installation. Technology production (e. g. , building and operating a solar panel factory) represents so-called “shovel ready” investment and is usually an unambiguous economic stimulus. Technology purchase and installation costs can have mixed economic effects, depending on the opportunity cost or alternative return to capital. In other words, technology adoption will stimulate the economy if it increases productivity, reduces resource costs, or both. If it reduces productivity or increases resource costs, it will be detrimental to growth. The shifting consumption patterns in the Keynes scenario begin with lower-cost electricity saving money for households and enterprises.

Output: contrast


Input: Claim: Microclimates can create variations in weather over short distances. [SEP] Text: D. , A. N. Hahmann, and J. E. Geisler, 1989: An investigation of the annual cycle of convective activity over the tropical Americas. J. Climate,2, 1388–1403. Jones, C. , 1990: An investigation of low-frequency variability of the large-scale circulation over South America. M. S. thesis, Department of Meteorology, University of Utah, 108 pp. ——, and J. D. Horel, 1991: High circulation and convective activity over (in Portuguese). Rev. Brasileira de Meteorologia,5-1, 379–387. ——, D. E. Waliser, J. E. Schemm, and W. K. Lau, 2000: Prediction skill of the Madden and in dynamical extended range weather forecasts. Climate Dyn. , in press. Kalnay, E. , and Coauthors, 1996: NCEP/NCAR 40-Project. Bull. Amer. Meteor. Soc. ,77, 437–471. Lenters, J. D. , and K. H. Cook, 1999: Summertime precipitation variability over South America: Role of the large-scale circulation. Mon. Wea. Rev. ,127, 409–431. Liebmann, B. , G. N. Kiladis, J. A. Marengo, T. Ambrizzi, J. D. Glick, 1999: Submonthly convective variability over and the Convergence Zone. J. Climate,12, 1877–1891. Madden, R. A. , and P. R. Julian, 1994: Observations of the 40–50 day tropical oscillation: A review. Mon. Wea. Rev. ,122, 814–837. Nogues-Paegle J. , K. Mo, and J. Paegle, 1998: Predictability of the NCEP/NCAR reanalysis model during austral summer. Mon. Wea. Rev. ,126, 3135–3152. Schemm, J. E. , H. Van den Dool, and S. Saha, 1996: A Multi-at NCEP. Preprints 11th Conf. on. Meteor. Soc. , 47–49. in gallery Fig. 1.

Output: pass


Input: Claim: Renewable energy reduces reliance on fossil fuels and helps decrease carbon emissions. [SEP] Text: The influence of economic growth, fossil and renewable energy, technological innovation, and globalisation on carbon dioxide emissions in 1, Asif Raihan2, Helper Zhou1 & …Mabutho Sibanda1 Show authors 451 all metrics AbstractCarbon dioxide (CO2), whose atmospheric volume has been increasing, is the principal greenhouse gas (GHG) that causes global warming and climate change. Climate change from increasing greenhouse gases (GHGs) has broad health and environmental influences. Human-emitted GHGs constitute a significant cause of global warming. The main objective of this 2020. Squares (DOLS) estimator was implemented to examine the relationship between the constituents. The study results indicate that economic growth, fossil energy use, technological innovation, and globalisation boost CO2Es, whereas renewable energy consumption reduces CO2Es. The output of the canonical cointegrating regression and fully modified least squares estimators also supports the output of the DOLS estimator. We concluded that, in South Africa, a rise in economic growth, fossil energy use, technological innovation, and globalisation results in an upsurge in CO2Es. In contrast, an increase in renewable energy consumption leads to a reduction in CO2Es. This research work contributes to the discourse on CO2Es in the context. The study recommends that policymakers in should design policies that foster the formation of a low-carbon country, the consumption of renewable energy, and the implementation of environment-friendly technological innovations that reduce the use of fossil energy. Highlights • This study assessed the impact of economic growth, technological innovation, fossil energy use, renewable energy consumption, and globalisation on CO2Es in South Africa. • The analysis revealed that economic growth, fossil energy use, technological innovation, and globalisation boost CO2Es, whereas renewable energy consumption reduces CO2Es. • There is a need to incorporate policies that promote the development of a low-carbon economy. Similar content being viewed by others The role of energy mix and financial development in greenhouse gas (GHG) emissions’ reduction: evidence from ten leading CO2 emitting countries 1950. GHGs originate from a multiplicity of human actions, such as fossil fuel burning for energy and heat, forest clearing, crop fertilising, livestock rearing, and the manufacturing of some types of commodities (EPA 2024; Raihan 2023a; Mensah et al. 2018).

Output: corroborate


Input: Claim: All countries can transition to renewable energy without economic impacts. [SEP] Text: These savings are diverted toward other expenditures, mostly domestic services that employ workers from all skill levels and demographics and that are non-tradable, meaning these new jobs cannot be outsourced. Because more than half of China's aggregate domestic demand is from household consumption and 70% of this goes to services, about half of energy cost savings diverted to other expenditures can be expected to go to this employment category—the most labor intensive and skill diverse in the economy. In contrast, the carbon fuel supply chain is among the least employment intensive; for example, the carbon fuel supply chain produces only 1%–10% as many jobs per unit of revenue compared with the service sector, differences far too large to be offset by wage inequality (Figure 1). Simply put, about two thirds of the money saved on cheaper electricity is spent on services, stimulating much stronger and more diverse domestic job growth. The EMP scenario builds on the Keynes scenario to examine the role of labor supply in economic adjustment. Based on China’s dynamism over most of the last two decades, the BAU, R, C50, and Keynes scenarios assume relatively “full employment,” which would limit the economy’s supply response to positive or negative stimulus. China’s reform period has seen dramatic economic growth and large-scale labor mobilization, but over the last decade growth and rates of job creation have moderated (particularly in the post-COVID economy). To reflect this, the EMP scenario assumes a low but nonzero elasticity of aggregate labor supply (0. 25), describing a more accommodating labor market. Finally, the PROD scenario builds on the EMP scenario to exemplify renewable energy productivity spillovers, assuming that accelerated renewable deployment confers an average of 1% higher TFP growth on the economy. This value is not a forecast; it merely illustrates the growth potential of induced innovation. ResultsAs expected, fossil fuel generation becomes increasingly small and renewable generation increasingly large as the renewable-promoting assumptions in our scenarios become increasingly aggressive (Figure 2). In the BAU scenario (moderate productivity improvement in renewables), coal’s share of generation declines from 65. 9% in 2017 to 51. 3% in 2030, while solar’s share increases by 8. 6 percentage points and wind’s share increases by 7. 6 percentage points. In the R scenario (more rapid productivity growth and larger renewable cost reductions), solar’s share rises by 18.

Output: contrast


Input: Claim: Microclimates can create variations in weather over short distances. [SEP] Text: Frequency response of Lanczos band-pass filters. The 30–70-day band is denoted intraseasonal type 1 (IS1), while variations of 10–30 days are designated intraseasonal type 2 (IS2). View in gallery Fig. 2. Mean geopotential height at 500 hPa (Z500) during austral summer (top). Contour interval is 20 m. Total standard deviation of Z500 (bottom). Contour interval is 5 m. View in gallery Fig. 3. Anomaly correlation between Z500 forecasts and verification fields. Skill is computed considering all days from the four austral summers during the DERF period. Skill is shown for 1, 4, 6, 8, and 11 days lead time. Correlations greater than 0. 50 are indicated by dark shading. View in gallery Fig. 4. (a) Mean outgoing longwave radiation (OLR) during austral summer (1 November–31 March). Contour interval is 5 W m−2 and shading indicates OLR is less than 220 W m−2. (b) Total OLR standard deviation during austral summer. Contour interval is 2. 5 W m−2 and shading indicates standard deviation greater than 20 W m−2. (c) Standard deviation of OLR anomalies for the IS1 type (30–70 days). Contour interval is 1. 0 W m−2 and shading indicates standard deviation greater than 10 W m−2. (d) Standard deviation of OLR anomalies for the IS2 type (10–30 days). Contour interval is 1. 0 W m−2 and shading indicates standard deviation greater than 15 W m−2. Region indicated with thick solid lines is used to characterize periods of strong (weak) convective activity over the (SACZ). View in gallery Fig. 5. Distribution of events (in pentads) selected according to the type of intraseasonal variability: 30–70 days (IS1; top) and 10–30 days (IS2; bottom). Events are selected when the magnitudes of the OLR anomalies averaged over the SACZ domain are less than 0. 8 standard deviation (IS1 neg; IS2 neg), greater than 0. 8 standard deviation (IS1 pos; IS2 pos) and between ±0. 3 standard deviation (IS1 null; IS2 null).

Output: pass


Input: Claim: Renewable energy reduces reliance on fossil fuels and helps decrease carbon emissions. [SEP] Text: Since climate change and global warming are connected to a plethora of threats to the environment, human life, and development (Raihan et al. 2023a; Samour et al. 2022), reducing CO2Es and alleviating global warming are essential for human well-being. Raihan (2023b) articulated that to develop a progressive, sustainable, and well-off community where no person is left behind, the universal society should try to create a carbon-neutral future. Nations formulated Goals (SDGs) for 2030 that highlight the importance of reasonably priced and clean energy, technological innovation, sustainable management of forests, sustainable and comprehensive economic progress, and universal teamwork for sustainable development to deal with climate change (SDGs 7, 8, 9, 13, 15, and 17). Agreement aims to fortify the universal response to the climate change problem by keeping a universal temperature increase this century well under 2 degrees Celsius (ºC) above pre-industrial levels and by engaging in activities to restrict the temperature rise even to 1. 5ºC. Moreover, the enforces the on Climate Change. It requires industrialised and transition economies to regulate and decrease emissions of GHGs in conformity with the approved specified goals. This research contributes to the discourse on CO2Es by assessing the impact of economic growth, technological innovation, renewable energy utilisation, fossil energy use, and globalisation on CO2Es in South Africa. These factors are chosen because they each significantly shape South Africa's carbon emissions profile. is one of the fastest-expanding emerging economies in the world (Kwakwa et al. 2023; Ekwueme et al. 2021). Its GDP has amplified from US$ 159 billion to US$ 352 billion from 1980 to 2021 (2023). Industrialisation, which is typically associated with high levels of CO2Es due to the burning of fossil fuels, is what drives economic growth in South Africa. is one of the most carbon-intensive countries and is the 14th main CO2 discharger in the world (Salahuddin et al. 2019). Salahuddin et al. (2019) further posited that contributes the most significant level of CO2Es in Sub-(SSA). In the same vein, Ndoricimpa (2017) postulated that is responsible for 42. 8% of the total emissions in Africa. Samour et al.

Output: corroborate


Input: Claim: All countries can transition to renewable energy without economic impacts. [SEP] Text: 7 percentage points compared with the BAU scenario in 2030, and wind’s share rises by 0. 5 percentage points, with coal’s share dropping below half. The rapid reduction in technology costs, especially solar costs, directly drives transformation of the power sector from fossil energy to renewable energy. In the C50 scenario (R scenario + ETS limiting power-sector CO2 emissions to 50% of 2015 levels), the substitution from fossil fuel to renewables in the power sector is further accelerated by the ETS policy. Solar and wind account for 54. 4% of generation in 2030, and coal falls to 21. 1%. In the Keynes, EMP, and PROD scenarios, changes in consumption patterns, labor supply, and productivity spillovers have relatively small impacts on the generation mix compared with the rapid renewable cost reductions and ETS implementation in the C50 scenario; thus, their electricity generation mixes are similar to the mix in the C50 scenario. CO2 power-sector CO2 emissions trends in each scenario correspond with the generation trends (Figure 3). In the BAU scenario, the moderate renewable productivity improvement decreases electricity prices, which increases electricity demand. Because coal generation remains the largest component, this rebound effect results in higher emissions. In the R scenario, total electricity demand is 2. 6% higher than in the BAU scenario owing to lower electricity prices. However, higher renewable penetration more than offsets this effect, and total annual carbon emissions are 4. 47 Gt in 2030, slightly larger than in 2017 but 23% lower than in the BAU scenario. The remaining scenarios show how transforming the power sector via renewables can reduce CO2 emissions while meeting electricity demand growth. In the C50, Keynes, EMP, and PROD scenarios, power-sector CO2 emissions are reduced by the cap of 50% below 2015 levels, at 1. 92 Gt in 2030. Compared with our BAU scenario, all of our other scenarios grow China’s economy (Tables 2 and 3). In the R scenario, sustained renewable cost improvements alone more than compensate for the cost of a significant renewable buildout, adding over 607 billion RMB or 0. 4% to China’s real GDP by 2030. Even the C50 scenario, with its aggressive CO2 mitigation strategies, results in positive growth owing to low-cost renewables.

Output: contrast


Input: Claim: Microclimates can create variations in weather over short distances. [SEP] Text: Numbers of events are displayed for each austral summer (S 85–86; S 86–87; S 87–88; S 88–89). Total number of pentads for each type of intraseasonal variability is indicated in parenthesis. View in gallery Fig. 6. Anomaly correlation between forecasts of Z500 and reanalysis averaged over the SACZ (top) and region (40°S–20°N; 90°–20°W) (bottom). Plots on the left are for IS1 type, while plots on the right are for IS2 type. Dashed, dotted, and solid lines indicate positive, negative, and null cases, respectively. Horizontal axis indicates lead time in days. View in gallery Fig. 7. Root-mean-square (rms) errors between forecasts of Z500 and reanalysis averaged over the SACZ (top) and region (40°S–20°N; 90°–20°W) (bottom). Plots on the left are for IS1 type, while plots on the right are for IS2 type. Dashed, dotted, and solid lines indicate positive, negative, and null cases, respectively. Horizontal axis indicates lead time in days. raw image Fig. 1. Frequency response of Lanczos band-pass filters. The 30–70-day band is denoted intraseasonal type 1 (IS1), while variations of 10–30 days are designated intraseasonal type 2 (IS2). View raw image Fig. 2. Mean geopotential height at 500 hPa (Z500) during austral summer (top). Contour interval is 20 m. Total standard deviation of Z500 (bottom). Contour interval is 5 m. View raw image Fig. 3. Anomaly correlation between Z500 forecasts and verification fields. Skill is computed considering all days from the four austral summers during the DERF period. Skill is shown for 1, 4, 6, 8, and 11 days lead time. Correlations greater than 0. 50 are indicated by dark shading. View raw image Fig. 4. (a) Mean outgoing longwave radiation (OLR) during austral summer (1 November–31 March). Contour interval is 5 W m−2 and shading indicates OLR is less than 220 W m−2. (b) Total OLR standard deviation during austral summer.

Output: pass


Input: Claim: Renewable energy reduces reliance on fossil fuels and helps decrease carbon emissions. [SEP] Text: (2022) propounded that the carbon intensity for has been constant at approximately 79 tCO2/TJ over the past years, which is greater than the average for the G20 countries. The authors further indicated that this high level emanates from the perpetual dominance of fossil fuels in South Africa. is hooked on fossil fuels (Winkler 2022). (IMF 2023) propounded that South Africa's energy industry was responsible for roughly 86% of the emissions in 2019 and was responsible for roughly 91% of the GHG emission upsurge over the past three decades. This is due to the fact that coal accounts for about 77% of South Africa's primary energy needs. Winkler (2022) postulated that 85% of all electricity generated in is manufactured in coal-powered plants, which is higher than in all the other countries except Mongolia and Kosovo. Further, Winkler (2022) specified that is presently ranked 8th in the world in terms of the total amount of coal used in electricity production. is one of the 196 parties that embraced the Paris Agreement, which launched a process for countries to design their plans to decrease emissions, dubbed Contributions (NDCs). Therefore, in 2021, devoted itself to setting targets for GHG emissions echelons of 350–420 MtCO2e by 2030 and 398–510 MtCO2e by 2025 (which are substantially less than targets outlined in 2016) and has gazetted in its Low-Strategy a target to attain net zero emissions by 2050. Moreover, South Africa's NDCs incorporate several policy instruments to achieve these targets, e. g. , the policy instrument on carbon tax. The 2021 Pact, established at the COP26 climate change summit, conceded that attaining net zero emissions by 2050 is essential and the key to repressing temperatures to 1. 5°C of warming. is under enormous pressure to curtail pollution because it is tied to the Kyoto Protocol, the Paris Agreement, and the 1992 on Climate Change. Consequently, it has been striving to reduce CO2Es and diversify its energy sources. Given that the burning of coal significantly contributes to GHG emissions and climate change, generating electricity from coal is a problem, with enormous pressure and incentives to scale this down (Winkler 2022).

Output: corroborate