Conditioning Energy Consumption Research Articles

Exploring the Contribution Roles from Municipal Cities in the Rise in Household CO2 Emissions in China: From a Local Scale Analysis in the Global Context

A major source of carbon dioxide emissions (CO2) arises from the household sector. Recent studies have reported increasing household CO2 emissions (HCO2) in many countries. Cities represent a key administrative level in China and can be managed to mitigate HCO2 if spatial and temporal variations in HCO2 are understood at fine scales. Here, we applied panel data analysis to map HCO2 at a pixel scale of 1 km in China using remotely sensed time series nighttime light data, grid population density data, and provincial energy consumption statistics from 2000 to 2020. Spatial and temporal variations in HCO2 were observed with four growth modes, including high growth (HG), low growth (LG), negative growth (NG), and high negative growth (HNG), for different periods, i.e., 2000–2010, 2010–2020, and 2000–2020. We proposed a local scale analysis of HCO2 growth patterns within a global context to assess the contribution roles of 372 municipal cities to the changes in the national total HCO2 (T-HCO2). The results indicated that T-HCO2 has tripled in the last two decades, but the roles of the contribution to the increase varied among cities. The local scale analysis revealed that more cities contributed to the rise in T-HCO2 through HG and LG than those that suppressed it through NG and HNG. The majority of the cities displayed contributions to the rise in T-HCO2 through two or more of the growth modes, confirming a significant variation in HCO2 across locations, even within a city. This study provides a new approach to understanding the roles cities play in the long-term dynamics of T-HCO2. We recommend increased efforts to encourage HCO2 mitigation in cities that have contributed to the rise in T-HCO2 to help neutralize carbon emissions at the national level.

Experimental Study and Sensitivity Analysis of Performance for a Hydrogen Diaphragm Compressor

As the critical infrastructure of hydrogen in transportation, the energy consumption of hydrogen refuelling stations plays a pivotal role in the progress of hydrogen energy within the transportation sector. Volumetric and isentropic efficiencies serve as metrics for evaluating compressor performance. To investigate the extent of factors, including suction pressure, pressure ratio, overflow pressure, and rotational speed, to the efficiencies of the diaphragm compressor, an experimental rig was set up in this study. The volume and energy losses were analysed by studying pressure–volume diagrams. The result shows that elevating suction pressure results in an increased isentropic efficiency. Specifically, raising suction pressure from 0.2 MPa to 0.8 MPa yields a 10.2% increase in isentropic efficiency. The increase in pressure ratio leads to a reduction in volumetric efficiency but an increase in isentropic efficiency. When the pressure ratio increased from 3 to 7, the volumetric efficiency decreased by 6.5% in volumetric efficiency, but the isentropic efficiency increased by 9.8%. Moreover, the escalation in rotational speed corresponds to a decrease in both volumetric and isentropic efficiencies. As the rotational speed increased from 420 r/min to 660 r/min, volumetric efficiency dropped by 9.6%, and isentropic efficiency experienced a 19.2% decrease.

Research on multi-energy coupled preheating system of city gate station based on Modelica simulation

Natural gas depressurization at city gate stations can cause hydrate formation, necessitating preheating before pressure regulation. To reduce the high energy consumption of traditional city gate station preheating processes, this study introduces four novel multi-energy coupled systems: photovoltaic-air source heat pump (PV-ASHP), solar thermal-combined heat and power-ASHP (ST-CHP-ASHP), CHP-ASHP, and ASHP system, with a typical city gate station in Shanghai taken as the research case. Dynamic energy simulation systems were developed in Dymola software to evaluate the proposed systems from energy, environmental, and economic perspectives, conducting a comparison with conventional boiler preheating systems, including sensitivity analysis of energy prices on system economics. Results indicate PV-ASHP has the lowest annual total cost (ATC) and shortest discounted payback period, while ATC values for ST-CHP-ASHP, CHP-ASHP, and ASHP increase by 6.4 %, 9.6 %, and 12.6 %, respectively. ST-CHP-ASHP and PV-ASHP demonstrate advantages in primary energy rate and carbon emissions, with ST-CHP-ASHP performing slightly better than PV-ASHP. Currently, PV-ASHP demonstrates the highest development potential. If photovoltaic feed-in tariffs remain constant, and natural gas prices decrease by over 12.5 % or CHP feed-in tariffs increase by over 38.4 %, ST-CHP-ASHP becomes more economically viable. This research provides valuable insights for retrofitting China's existing city gate station preheating systems.

Developing a global sustainable electricity use index using the pressure-state-response framework

This study analyse and compare the sustainable electricity usage in 60 countries listed on the official websites of World Energy Consumption Statistics and Climate Bond Initiative. The study also analyses the impact of increased usage of sustainable electricity on the economies' dependence on non-renewable energy sources in the evaluation system. We used a standard index system based on the Pressure-State Response (PSR) model to measure global sustainable electricity usage. Model results convey that Norway is the best performer in sustainable electricity usage, while several European countries display commendable scores, confirming their commitment to sustainable electricity practices. On the other hand, despite being the leading economies in terms of GDP, major economies such as the United States, China, Japan, and India have underperformed compared to others in the evaluation system. The study employs regression techniques to explain the relationship between sustainable electricity usage and non-renewable energy dependence. Results confirm a negative relationship between the variables, indicating the role of sustainable energy practices in reducing fossil fuel consumption. It emphasizes the urgency of a balanced approach to economic growth and natural resource usage to support a green future.

Optimizing Electric Vehicle (EV) Charging with Integrated Renewable Energy Sources: A Cloud-Based Forecasting Approach for Eco-Sustainability

As electric vehicles (EVs) are becoming more common and the need for sustainable energy practices is growing, better management of EV charging station loads is a necessity. The simple act of folding renewable power from solar or wind in an EV charging system presents a huge opportunity to make them even greener as well as improve grid resiliency. This paper proposes an innovative EV charging station energy consumption forecasting approach by incorporating integrated renewable energy data. The optimization is achieved through the application of SARLDNet, which enhances predictive accuracy and reduces forecast errors, thereby allowing for more efficient energy allocation and load management in EV charging stations. The technique leverages comprehensive solar and wind energy statistics alongside detailed EV charging station utilization data collected over 3.5 years from various locations across California. To ensure data integrity, missing data were meticulously addressed, and data quality was enhanced. The Boruta approach was employed for feature selection, identifying critical predictors, and improving the dataset through feature engineering to elucidate energy consumption trends. Empirical mode decomposition (EMD) signal decomposition extracts intrinsic mode functions, revealing temporal patterns and significantly boosting forecasting accuracy. This study introduces a novel stem-auxiliary-reduction-LSTM-dense network (SARLDNet) architecture tailored for robust regression analysis. This architecture combines regularization, dense output layers, LSTM-based temporal context learning, dimensionality reduction, and early feature extraction to mitigate overfitting. The performance of SARLDNet is benchmarked against established models including LSTM, XGBoost, and ARIMA, demonstrating superior accuracy with a mean absolute percentage error (MAPE) of 7.2%, Root Mean Square Error (RMSE) of 22.3 kWh, and R2 Score of 0.87. This validation of SARLDNet’s potential for real-world applications, with its enhanced predictive accuracy and reduced error rates across various EV charging stations, is a reason for optimism in the field of renewable energy and EV infrastructure planning. This study also emphasizes the role of cloud infrastructure in enabling real-time forecasting and decision support. By facilitating scalable and efficient data processing, the insights generated support informed energy management and infrastructure planning decisions under dynamic conditions, empowering the audience to adopt sustainable energy practices.

Study of oblique granular impingement flow through CFD-DEM simulations and energy consumption statistics

This paper investigates an experimental granular impingement flow system through simulations and energy consumption statistics. With the increase of solid concentration, flow patterns undergo cross flow (dense edge and dilute center), dispersed flow, and mixed-jet flow (dilute edge and dense center) in turn. The influence of gas phase on the flow pattern was investigated. According to the analysis of force and time-averaged velocity, it is revealed that gas-solid interaction will cause energy dissipation of granular flow, which cannot be ignored in specific cases. Then, a flow map is proposed according to both the Archimedes number and solid concentration. In addition, the flow pattern transition and local structure variations can be reflected by energy consumption statistics. Based on the simulations and methods developed in this work, it is promising to deepen the understanding and study of granular impingement flow from the viewpoints of energy consumption and new-type flow map.

Energy structure optimization of shandong province by 2035 under double carbon goal

Abstract In this paper, based on the existing population size, economic development level, industrial structure, current status of energy production and consumption, and current status of electricity production and consumption in Shandong Province, the LEAP-Shandong model was constructed by using the Long-range Energy Alternatives Planning (LEAP) model as a research tool, which is used to forecast the population size, GDP, energy consumption, and electricity structure of different industries in Shandong Province up to 2035 under three scenarios: policy development scenario, energy efficiency improvement scenario, and enhanced low-carbon scenario. The result shows that the population of Shandong province is expected to peak at 10, 426, 000 by 2033, and then slowly decline; in the enhanced low-carbon scenario, with an appropriate slowdown in economic development, further optimization of industrial structure and enhancement of energy efficiency can effectively reduce the energy consumption of the terminal energy-using sector in Shandong province. Based on the results, the main recommendations for Shandong’s energy development are as follows: to build a low-carbon industrial system in Shandong in light of new development opportunities; to build a new energy development system in light of Shandong’s energy characteristics; and to promote the low-carbon transformation of Shandong’s energy in light of the new model of the energy industry.

The Impact of Future Land Use Change on Carbon Emission and Its Optimization Strategy

Rapidly changing climate issues and increasingly severe carbon emissions are great challenges to the carbon peaking and carbon neutrality strategy. Analyzing the impact of future land use changes on carbon emissions can provide an important basis and reference for scientifically constructing a low-carbon and sustainable territorial spatial planning, as well as realizing the goal of the dual-carbon strategy. Based on land use data, agricultural production activity data, and energy consumption statistics, this study simulated the land use changes of the Yangtze River Delta region (YRDR) from 2030 to 2060 under the natural development (ND) scenario and sustainable development (SD) scenario by using the Patch-generating Land Use Simulation (PLUS) model and analyzed the impacts of future land use changes on carbon emissions. The results showed that: (1) The land use simulation results obtained by using the PLUS model under the sustainable development scenario were highly consistent with the actual land use with an OA value of 97.0%, a Kappa coefficient of 0.952, and a FoM coefficient of 0.403; (2) Based on the simulated land use under the SD scenario from 2030 to 2060, the quantity of construction land was effectively controlled, and the spatial distributions of cropland and forests were found to dominate in the north and south of the Yangtze River, respectively; (3) Anhui Province was the major contributor (accounted for 49.5%) to the net carbon absorption by cropland while Zhejiang Province was the major contributor (accounted for 63.3%) to the net carbon absorption by forest in the YRDR during the period 2020–2060 under the SD scenario; (4) Carbon emissions from construction land were the main source of carbon emissions from land use in the YRDR during the period 2020–2060 with proportions higher than 99% under both the ND and SD development scenarios. These findings underscore the urgent need for the government to take measures to balance the relationships between cropland and ecological protection and economic development, which provides a reference for the optimization of land use structure and policy formulation in the future.

Experimental investigation on cooling load caused by unorganized ventilation in subway station: A case study

Unorganized ventilation significantly increases the energy consumption of subway stations. However, the quantitative description of cooling load caused by the unorganized ventilation in subway stations with platform screen doors (PSDs) still needs to be explored in depth. This study firstly proposed an experiment-based method to determine the airflow rate of unorganized ventilation, and then the corresponding cooling load is determined. Based upon this method, a series of field tests were conducted at a subway station in Shanghai. By analyzing the experimental data, the characteristic of unorganized ventilation at PSDs and entrance/exit passages was obtained. As for PSDs, the volume of unorganized ventilation in a cycle (the period from the upward train entering the station to the next upward train starting to approach the station) from the tunnel to the platform and from the platform to the tunnel is 2112.24 m3 and 4219.31 m3, respectively. With regard to entrance/exit passages, the volume of unorganized ventilation is 2655 m3 in a cycle. Thereafter, the above-mentioned results were used to determine cooling load. Owing to the difference in the departure density of train on weekdays and weekends, two typical summer days were selected for the purpose of cooling load analysis. The overall cumulative value of the cooling load caused by unorganized ventilation was 1416 kWh on a working day and 1718 kWh for the weekend, which accounted for 16.74 % and 19.19 % of the daily cooling load, respectively.

Optimization research on energy consumption and carbon emissions in hydrogen-blended natural gas pipeline networks considering compressor performance - A case study of the west-east gas pipeline (line 1)

The blending of hydrogen into natural gas can utilize existing natural gas pipeline infrastructure to achieve long-distance, large-scale, and cost-effective hydrogen transportation. However, the introduction of hydrogen poses a series of new technical and safety challenges to the operation of natural gas pipelines. Addressing these issues, this paper focuses on minimizing compressor station energy consumption and carbon emissions as objective functions, conducting a comprehensive analysis of station operation modes. Incorporating the characteristics of the model, the soft frost search strategy, the hard frost penetration mechanism, and the positive greedy selection mechanism are introduced to propose the IRIME algorithm. Comparisons are made with well-performing optimization algorithms using classical test functions to evaluate algorithm performance. To further validate the algorithm's accuracy, the West-East Gas Pipeline (Line 1) is considered, comparing the convergence speed and precision of the IRIME algorithm, leading to its selection for solving the model. Furthermore, consider analyzing the impact of different hydrogen blending ratios on the performance curves and operating points of compressors. Results indicate that compared to conditions without hydrogen blending, the compressor outlet temperature and pressure drop increase for hydrogen blending ratios of 5%, 10%, 15%, and 20%; as the hydrogen blending ratio increases, the efficiency of fuel-driven compressors, efficiency of electrically driven compressors, and average efficiency of compressor units decrease; with an increase in hydrogen blending ratio, carbon emissions from fuel-driven compressors and electric-driven compressors both decrease. In conclusion, with an increasing hydrogen blending ratio, total energy consumption increases by 3.14%, 5.69%, 8.68%, and 10.90%, while total carbon emissions decrease by 2.99%, 5.36%, 8.32%, and 10.68%. These findings provide valuable insights for operational guidance in the field.

Research on low-energy consumption automatic real-time regulation of cascade gates and pumps in open-canal based on reinforcement learning

ABSTRACT Cascade gates and pumps are common hydraulic structures in the open-canal section of water transfer projects, characterized by high energy consumption and substantial costs, making it challenging to regulate. By implementing cascade gates regulation to control the hydraulic process, lift distribution of pump stations can be optimized, thus enhancing operational efficiency and reducing energy consumption. However, the selection of control models and parameter optimization is difficult because hydraulic processes are nonlinear, high-dimensional, large hysteresis, strong coupling, and time-varying. This study considers minimum energy consumption of pump stations as the regulation objective and employs the reinforcement learning (RL) algorithm for optimization regulation (OR) within a typical canal section of the Jiaodong Water Transfer Project. Our results demonstrate that after regulating, OR can precisely control the water level to achieve the high efficiency lift interval of pump station, enhancing efficiency by 4.12–6.02% compared to previous operation. Moreover, using optimized hyperparameters group, the RL model proves robust under different work conditions. The proposed method is suitable for complex hydraulic processes, highlighting its potential to support more effective decision-making in water resources regulation.

Hydrogen fueling station: review of the technological state of hydrogen fuel usage

RELEVANCE. In the modern world, on the verge of global climate change, the search and implementation of alternative energy sources become particularly significant. Hydrogen energy is one of the most promising directions, offering a revolutionary approach to the decarbonization of various industrial sectors. The development of technologies related to the production, storage, and use of hydrogen opens new horizons for creating a sustainable and environmentally friendly energy infrastructure.OBJECTIVE. To review the technological state of hydrogen refueling stations (HRS), analyze the latest global trends and developments in this area, identify factors contributing to the efficiency of HRS components, and present thermodynamic principles of hydrogen fuel use. To outline the main problems associated with the need for widespread implementation of hydrogen infrastructure and to identify potential directions for their resolution. To develop suggestions for creating a modular layout of a container-type hydrogen station, which will allow a flexible approach to organizing hydrogen infrastructure with the possibility of rapid scaling and adaptation under various operating conditions.METHODS. Based on the use of literature data. The method of prototyping an autonomous hydrogen refueling station was used, and mathematical calculations of thermodynamic processes occurring in the components of HRS were conducted.RESULTS. Studies in the field of technological state of stations have been examined and systematized, and development trends have been identified. The main components involved in the operation of a hydrogen station are described. Thermodynamic processes of hydrogen fuel use that contribute to a significant reduction in energy consumption of hydrogen stations have been investigated.CONCLUSION. The hydrogen station combines efficient conversion of hydrogen into electricity, minimization of emissions, energy independence, and flexibility in energy storage. Determining the optimal operating parameters of HRS equipment based on the thermodynamics of processes, considering the specifics of temperature regimes of Russian regions, is important for reducing costs and increasing the energy efficiency of hydrogen fuel systems. The proposed structure of the container-type HRS is an optimal platform for subsequent modernizations and innovations in the field of hydrogen technologies.

Optimization of the Coupling between Water and Energy Consumption in a Smart Integrated Photovoltaic Pumping Station System

Agricultural irrigation requires significant consumption of freshwater resources and energy. The integration of photovoltaic power generation into irrigation systems has been extensively investigated in order to save the cost of energy. However, current research often neglects the coupling relationship between photovoltaic power generation and irrigation schemes. This study presented a novel smart integrated photovoltaic pump station system to effectively address the issue associated with water and energy consumption in irrigation. An optimization model was proposed to synchronize the energy consumption of irrigation pump stations with photovoltaic power generation, accurately meeting the irrigation water demand while maximizing solar energy utilization. The optimization model incorporates power balance, grid-connected power, and total water demand as constraints while considering pump speed as the decision variable and aiming to minimize daily operational costs. Finally, a high-standard farmland was used as a case study to validate the efficacy of the optimization strategy through two photovoltaic grid-connected policies—one allowing for the sale of surplus power and the other prohibiting it. An improved dynamic programming method was employed to solve for optimal energy consumption schemes under different water demand conditions; the results were compared against traditional methods, revealing potential cost savings ranging from 6.2% to 30.5%. The optimization model and method propose a new operational concept for the irrigation system with photovoltaic generation, effectively utilizing the distinctive features of both irrigation and photovoltaics to optimize water and energy resources.

Prediction of energy consumption for manufacturing small and medium-sized enterprises (SMEs) considering industry characteristics

There has been a growing demand for energy consumption statistics in the manufacturing industry to establish national energy and greenhouse gas policies. Despite its importance, the Korean government faces significant challenges in collecting energy data at a facility level in a precise and timely manner. To address the lack of timely data, this paper employs machine-learning models to predict the annual total energy consumptions of each manufacturing facility. We first designed four prediction models that take into account the characteristics and energy consumption behaviors of industry sub-sectors. As input variables, these prediction models mainly included electricity consumption, employee size, energy types, gas consumption and other accessible data. Finally, we conducted numerical experiments on approximately 100,000 facilities and evaluated the prediction performance of various machine-learning algorithms such as linear regression, decision tree regression, random forest regression, gradient boost regression, and extreme gradient boosting regression. The numerical experiments provided insights into which model and algorithm offer the best prediction performance for each industry sub-sector. In addition, we identified the important variables for predicting total energy consumption, revealing that not only electricity but also various other energy sources and variables representing industry-specific characteristics play a crucial role in improving prediction performance.

Analysis of Energy Consumption of Robotic Welding Stations

Analysis of the energy consumption of industrial robots during the implementation of a technological task can increase efficiency and minimize production costs, as well as extend the service life of robots, taking into account the rational use of energy sources. An important challenge in designing sustainable production processes is the selection of energy-saving technological equipment supporting the work of robots. Additionally, optimization of robot program codes, including parameters and robot movement paths, determines the energy efficiency of the robotic station. Welding is one of the most popular areas of process robotization due to its ability to increase production efficiency and improve the quality of part assembly compared to manual work. In these studies, an exemplary process of robotic welding of aluminum alloy parts was designed, analyzed, and optimized with an orientation towards sustainable development guidelines. This work also presents a review of current publications discussing the authors’ contribution to achieving energy efficiency in robotic welding processes. The purpose of this analysis is also to indicate directions for the development of future research work in order to minimize energy consumption in production processes with an appropriate level of product quality and increase production efficiency.

Energy-driven TBM health status estimation with a hybrid deep learning approach

This study provides a hybrid deep learning approach that enables the researchers to estimate the Tunnel Boring Machine (TBM) health status during excavation process using energy consumption data. By analyzing the energy forms and paths in the tunneling process, the key energy factors' consumption performance behavior affecting TBM safety is identified. The Ensemble Empirical Mode Decomposition (EEMD) method and a Convolution neural network-Long short term memory-Depth Neural Network (CNN-LSTM-DNN, CLDNN) model are integrated for Health Performance Parameter (HPP) prediction. A classification method is constructed by the Generative Adversarial Imputation Networks (GAIN) model for health status estimation. The SHapley Additive exPlanations (SHAP) method is performed to quantify the relationships between energy consumption and system health status. A tunneling case in Singapore is used to test the effectiveness of the developed methodology. It is found that (1) the proposed method provides TBM cutter wear and cooling oil prediction with a R-square (R2) of 0.8591 and 0.8727, respectively, and (2) classification results indicate that the estimation F1-score of 0.9863 can be achieved based on health status evaluation indicator (HSEI). The novelty of the proposed approach lies in its capabilities of (1) fully extracting and utilizing the energy consumption information in every regard to predict TBM health performance; (2) providing a good one-step-ahead health status estimation for the TBM excavation process. Therefore, this study proposes a feasible direction for the application of energy consumption data in TBM health status estimation.

Allocation of CO2 emission target in China under the “1 + N” policy: Considering natural carbon sinks and wind-solar-hydropower endowments

The CO2 emission control targets (CET) allocation faces the unavoidable issues of “energy equity” and “carbon sink equity”, which are complicated by changing regional characteristics. By combining regional characteristics assessment, energy consumption status in the “1 + N” policy, and Data Envelopment Analysis (DEA) modeling, two schemes for China's net CET allocation in 2020–2030 are implied to reveal the impacts of regional carbon sinks and renewable energy endowments differences on the allocation. The results indicate that: firstly, significant regional differences are inevitably intrinsic within China's renewable energy endowments and carbon sinks to substantially affect the equity of carbon emission allocation in 2020–2030; secondly Provinces with high fossil energy reliance, such as Hebei, Shandong, Shanxi, and Inner Mongolia, are generally facing continuous pressure to reduce CO2 emissions during the 14th FYP and 15th FYP; thirdly, as a regional ecological advantage, carbon sinks are an essential source of additional carbon budgets for provinces, as in Sichuan and Yunnan. As a dominating impact factor of carbon sinks on CET allocation, terrestrial carbon sinks are much higher than ocean carbon sinks with clear provincial attribution; fourthly, in our allocation scheme, the exploitation of renewable energy endowments directly affects the provincial carbon budget and can promote low carbon energy supply; finally, the foundation to build the CET allocation mechanism, China's carbon market needs to be improved. This study helps policymakers understand and balance the equity issues in CET allocation due to regional differences in renewable energy endowments and carbon sinks.

Energy Conservation for Mobile Cellular Systems through Site Optimization (Base Transceivers Station Optimization)

Mobile communication systems have cemented their position as a vital and practical tool for communication that supports productive corporate operations. The energy use of wireless access networks has grown to be a significant economic and environmental problem, the potential for utilizing the conserved energy was highlighted along with techniques that may boost energy efficiency in this paper's investigation of the energy consumption of base transceivers stations (BTS). Analysis of the data reveals that by optimizing 60 BTSs, the energy consumption decreases by 36.83% with 127.4MWh of monthly reserve. This is equivalent to powering 35 additional BTSs with a demand of 3.64224MWh or 69 homes for a daily power supply of 10 hours with a demand of 1.8452MWh and 440 homes with a total demand of 289.8KWh.

Application research of deep learning-based BIM Technology in Intelligent Construction

Abstract With the increasing importance of energy-efficient renovations in architecture, the pressing issue of how to enhance the effectiveness of such renovations through advanced technology has come to the forefront. This article affirms the significance of building information modeling (BIM) technology in energy consumption renovations. It introduces a BIM-based building energy monitoring system, which not only provides comprehensive and effective static information for energy consumption analysis but also offers real-time visualization of energy consumption status for daily management. By collecting all necessary data for energy management and storing it in the cloud, this system greatly facilitates data analysis. Moreover, the article proposes a short-term electricity load forecasting method for community buildings based on empirical mode decomposition (EMD)-gated recurrent unit (GRU). The EMD-GRU method proposed in this article exhibits superior accuracy compared to other methods, with a mean absolute percentage error value of 14.43%. By considering factors such as historical load, weather, and date, this method accurately predicts short-term electricity loads for communities. This research provides a robust framework for improving energy management in buildings through advanced technologies, ultimately leading to more effective energy-saving renovations and optimizations.

A global catalogue of CO2 emissions and co-emitted species from power plants, including high-resolution vertical and temporal profiles

Abstract. We present a high-resolution global emission catalogue of CO2 and co-emitted species (NOx, SO2, CO, CH4) from thermal power plants for the year 2018. The construction of the database follows a bottom-up approach, which combines plant-specific information with national energy consumption statistics and fuel-dependent emission factors for CO2 and emission ratios for co-emitted species (e.g. the amount of NOx emitted relative to CO2: NOx/CO2). The resulting catalogue contains annual emission information for more than 16 000 individual facilities at their exact geographical locations. Each facility is linked to a country- and fuel-dependent temporal profile (i.e. monthly, day of the week and hourly) and a plant-level vertical profile, which were derived from national electricity generation statistics and plume rise calculations that combine stack parameters with meteorological information. The combination of the aforementioned information allows us to derive high-resolution spatial and temporal emissions for modelling purposes. Estimated annual emissions were compared against independent plant- and country-level inventories, including Carbon Monitoring for Action (CARMA), the Global Infrastructure emission Database (GID) and the Emissions Database for Global Atmospheric Research (EDGAR), as well as officially reported emission data. Overall good agreement is observed between datasets when comparing the CO2 emissions. The main discrepancies are related to the non-inclusion of auto-producer or heat-only facilities in certain countries due to a lack of data. Larger inconsistencies are obtained when comparing emissions from co-emitted species due to uncertainties in the fuel-, country- and region-dependent emission ratios and gap-filling procedures. The temporal distribution of emissions obtained in this work was compared against traditional sector-dependent profiles that are widely used in modelling efforts. This highlighted important differences and the need to consider country dependencies when temporally distributing emissions. The resulting catalogue (https://doi.org/10.24380/0a9o-v7xe, Guevara et al., 2023) is developed in the framework of the Prototype System for a Copernicus CO2 service (CoCO2) European Union (EU)-funded project to support the development of the Copernicus CO2 Monitoring and Verification Support capacity (CO2MVS).