Energy Consumption Index Research Articles

Optimizing pump operations in water distribution networks: Balancing energy efficiency, water quality and operational constraints

Optimizing pump operation in urban water distribution systems represents a critical task for enhancing energy efficiency. Despite extensive research, achieving optimal pump operation remains a challenge due to the presence of conflicting objectives, such as minimizing energy costs and ensuring adequate pressure head availability. The recent emphasis on maintaining distributed water quality further increases the complexity of the problem. Mathematically, the optimization problem manifests as a non-convex and non-continuous search space, necessitating the application of non-derivative optimization techniques, which are highly susceptible to constraints. This work presents a comprehensive analysis of optimal pump operation, considering energy efficiency and water quality as primary objectives. A series of constraints are progressively incorporated into the model to evaluate their impact on the final operational strategy. Initially, single-objective optimization is employed using the Particle Swarm Optimization (PSO) algorithm, a robust and widely adopted approach in water resources research. Subsequently, a multi-objective algorithm, the Non-dominated Sorting Genetic Algorithm-II (NSGA-II), is utilized to identify the Pareto front, encompassing both energy consumption and water quality indicators. The results demonstrate that constraints exert a significant influence on the final energy consumption and the convergence of the optimization process. Moreover, by simultaneously considering both objectives, this study proves the feasibility of identifying an optimal operational scheme that minimizes energy consumption while maintaining acceptable water quality.

The Relationship between Renewable Energy Consumption and Human Development

This article examines the relationship between renewable energy consumption and human development indicators in Moldova. The analysis, spanning from 1995 to 2021, employs econometric techniques, including Ordinary Least Squares (OLS) and Two-Stage Least Squares (2SLS) regression, to investigate the impact of renewable energy on the Human Development Index (HDI). The findings reveal a positive correlation between renewable energy consumption and HDI, indicating that increased adoption of renewable energy is associated with improvements in human development. Specifically, the study finds that a one-percentage-point increase in renewable energy consumption leads to approximately a 1.6% increase in HDI. Moreover, the article discusses Moldova's energy policy landscape, highlighting initiatives and policies aimed at promoting renewable energy development and achieving sustainability goals. The conclusions drawn from this study emphasize the critical importance of prioritizing renewable energy solutions. Such prioritization is deemed essential for driving sustainable development, fostering economic growth, bolstering energy security, and mitigating the adverse impacts of climate change. The study underscores that Moldova's experience and policy initiatives could serve as a valuable model for other nations aiming to enhance their human development through sustainable energy practices. This research argues for a continued and intensified focus on the integration of renewable energy, highlighting its fundamental importance within national and international strategies aimed at achieving ambitious human development and sustainability goals. This involves a reassessment and expansion of current commitments, considering not only the ecological benefits, but also the long-term economic and social impact, thereby strengthening a sustainable future for future generations.

Simulation-Based Exploration with Energyplus as an Energy Efficiency Strategy

In countries with tropical or subtropical climates, such as Indonesia, the use of air conditioning is becoming increasingly important for comfort and productivity. However, high energy use in air conditioning systems has significant environmental and economic impacts, including increased greenhouse gas emissions and high operational costs. This research aims to analyze the Energy Consumption Index (ECI) in the Joint Lecture Building, State University of Malang using EnergyPlus software simulation. Research data consists of building data, weather data, building construction data, operating schedules, equipment specifications, and energy usage data. Furthermore, the data analysis technique used ECI calculations based on EnergyPlus software simulation. The research results show that based on the Energy Consumption Index value it is 201,943 kWh/m2/year, but with a savings scenario it becomes 161,964 kWh/m2/year (an increase of 19,797%) so it is classified as efficient. The energy-saving scenario implemented is to replace the Constant Air Volume (CAV) air conditioning system with Variable Air Volume (VAV) and the types of luminaires that were originally fluorescent will be replaced with Light Emitting Diodes (LEDs). The comparison results obtained an energy-saving value of 19,797%. The research results show that using VAV and LEDs technology can increase energy efficiency in the Joint Lecture Building, State University of Malang. The scenario attempts to reduce environmental impact through energy efficiency in commercial buildings and shows that the EnergyPlus simulation method is an effective approach for designing energy savings strategies.

Accelerating renewables: Unveiling the role of green energy markets

The innovative local grey forecasting model for energy production prediction emerges as a pivotal tool in the shift towards renewable energy, embodying the essence of adaptability and precision for the urgent need for sustainable solutions. The integration of such forecasting models is imperative for comprehending the complexities of energy production and market dynamics, ultimately facilitating a seamless transition to a sustainable energy future. Given this, the current study investigates the connections between renewable energy consumption and its determinants, focusing on the role of green energy markets across 12 countries from 2003 to 2021. Employing a novel quantile regression approach, this study analyzes the impact of green energy markets, GDP per capita, urban population, and environmental policies on renewable energy consumption, introducing a renewable energy consumption index for novel insights. Rooted in grey system theory, this study offers a unique approach to predicting energy outputs, crucial for guiding the strategic decisions in green energy markets. The quantile regression analysis reveals that green energy markets have a differential impact on renewable energy consumption whereas Renewable Energy Installed Capacity positively affects renewable energy consumption across all quantiles. Environmental Stringency Policies consistently show a negative impact across all quantiles. By providing reliable predictions of energy production, these findings empower policymakers, investors, and operators to make strategic, data-driven decisions that align with the fluctuating demands of the market and the overarching goals of sustainability and environmental stewardship.

Guidelines for data collection on energy performance of higher-education buildings in Egypt: a case study

ABSTRACT Reliable energy analysis of buildings relies heavily on high-quality data leading to proper indicators. Previous studies have highlighted the importance of data quality in analyzing energy usage in residential and non-residential buildings in order to transform declarations to actions, optimise energy efficiency policies and monitor progress and failures in countries. Collected data must adhere to national and international standards for energy performance in buildings. This study aims to provide practical guidelines for effectively collecting and preparing data suitable for evaluating energy performance in Egyptian higher-education (HE) buildings. The guidelines are developed based on a comprehensive case study, considering data availability in typical educational facilities. Architectural and civil engineering drawings, construction specifications, and occupancy details are accessible. However, actual monthly electrical and natural gas consumption data for individual buildings are lacking. To address this, the study proposes the creation of detailed datasheets for each building, encompassing all energy sources and their electrical and power specifications, such as equipment, machinery, and HVAC systems. These datasheets were utilized to calculate energy consumption and energy usage indicators (EUI). The findings demonstrate that the datasheets enable adequate assessment of energy usage in various spaces within educational buildings, including staff rooms, lecture halls, and laboratories. This facilitates the identification of areas in need of targeted energy efficiency improvements. Notably, the study reveals that electricity consumption in the Faculty of Engineering building is significantly influenced by PCs, laboratories, lighting, and air conditioning.

Optimization analysis of distributed energy consumption based on dynamic data synchronization and intelligent control

SummaryWith the rapid development of the global renewable energy source field, the importance of dynamic index processing technology in distributed energy systems has become more and more obvious. To better improve the real‐time dynamic interaction means of microgrids in the energy Internet and optimize the relevant methods for microgrid energy consumption detection, this article proposes to introduce the distributed Hadoop platform into the electrical thermal coupling multivariate data in the form of cluster configuration, and then use the Spark framework to detect and capture real‐time data, to complete the tracking and analysis of energy consumption data. At the same time, the Internet of Things and the cloud intelligent monitoring system are combined to further clean and explore the data, to achieve the in‐depth detection of the energy consumption problem of the microgrid under the premise of reducing the initial investment, and achieve the purpose of reducing the operating cost. In this case, the outliers are detected according to the photovoltaic indicators of photovoltaic power stations, the filtration and purification functions of photovoltaic indicators are used by the nuclear density curve, and the sustainable solar energy is optimized by combining multiple indicators such as wind direction and temperature. Based on reducing energy consumption, the overfitting phenomenon of the controller is controlled, and an optimized controller‐led cloud platform is established. By establishing the objective function model, the robustness of the controller is guaranteed and the detection expectation is satisfied by the experiment of energy consumption data. In addition, when the cloud platform is created, this study uses a genetic algorithm to optimize the controller index and then builds a cloud console detection mechanism that collaborates with the Internet. Through the research, it is found that outliers may lead to the redundancy of energy consumption indicators in the non‐processing state. This study adopts the optimization of energy consumption parameters and the help of a distributed data framework to deal with and effectively solve this problem. In terms of interpolation simulation verification combined with experimental data, this paper proposes to use the Internet of Things, wearable devices, sensors, and other means to monitor the cost of energy consumption, to realize the distributed dynamic storage of massive real‐time data in the process of parallel processing, as well as the evaluation and detection of real‐time data replacement.

Micro-electro-mechanical acoustofluidic mixing system: A response surface-metaheuristic machine learning fusion framework

Micro-mixing is an essential step in various biological and chemical analyses and synthesis processes. In the present study, an acoustofluidic micromixer is designed, modeled, and its functionality has been investigated. Intelligent acoustofluidic is proposed as an evaluation-optimization method, based on Machine Learning algorithms. The first step involves designing a parametric geometry with multiple sharp edges. Following that, mathematical modeling of the system is carried out using Response Surface Methodology (RSM) and computational fluid dynamics (CFD). Geometrical parameters including the sharp edge tip angle and the channel thickness, along with the peak-to-peak actuation voltage were investigated. It was revealed that, due to the inherent nonlinearity of acoustic streaming, each design parameter could significantly impact the flow patterns and mixing performance of the acoustofluidic mixer. The mathematical surrogate model obtained from data-based modeling procedure is then employed for the artificial intelligence multi-objective optimization algorithms, i.e. Differential Evolution (DE) and Non-dominated Sorting Genetic Algorithm two (NSGA-II). The two objective functions are the mixing index and the mechanical energy consumption index (ME). The derived optimum solutions had low Mehcanical Energy (ME) values while yielding homogeneity in a wide range of applied voltages. This ensures the controllability of the proposed system, which is an invaluable feature in synthesis processes to determine the critical physical properties of the nanoparticles. Through the algorithm evaluation process, which was conducted to distinguish the more accurate and robust optimization method, it was revealed that the DE algorithm outperformed NSGA-II in generating optimum solutions with superior functionalities. In the final section, three optimum models, operating under three different Vp-p values, 10, 20, 30, were selected, and their acoustofluidic characteristics were analyzed. It was concluded that utilizing baffles with three sharp edges at low Vp-p values results in a 96% Mixing Index and 48% decrease in the power consumption of the system. However, at high Vp-p values, baffles with one sharp edge demonstrated superior mixing functionality, with a MI of 98%. Moreover, it is revealed that utilizing baffles with three-sharp edges results in an 80% increase of Mixing Index of the acoustofluidic micromixer.

An improved memetic algorithm for multi-objective resource-constrained flexible job shop inverse scheduling problem: An application for machining workshop

Resource-constrained flexible job shop scheduling problems are commonly encountered in some manufacturing industries, and have been widely studied in recent years. However, traditional resource constrained flexible job shop scheduling problem rarely consider the uncertainties in actual manufacturing systems, which may make the original schedule become suboptimal or even unfeasible. Therefore, a resource constrained flexible job shop inverse scheduling problem (RCFJISP) is proposed in this paper, which aims to cope with uncertain events by simultaneously adjusting the machine, worker and process parameters of the original schedule. A multi-objective optimization model is constructed to minimize the makespan, worker cost, machine energy consumption and deviation index. Furthermore, an improved memetic algorithm (IMA) is developed for solving the proposed problem. In IMA, a novel double-layer encoding mechanism is designed to enhance the capacity in exploring new solution’s domains. Three initialization strategies utilizing original scheduling information are designed to improve the quality of initial solutions. An adaptive mutation strategy and a local search mechanism are designed to enhance exploration and exploitation ability of the algorithm. And a crowding operator is proposed to reflect the diversity of the population effectively. In computational experiments, 28 extended benchmarks are constructed, and the effectiveness of the proposed strategy and algorithm is verified by comparing IMA with its 4 variants and other 4 widely used algorithms. Finally, two inverse scheduling problems of a real-world hydraulic cylinder machining workshop under two uncertain situations are studied. The results demonstrate that IMA can effectively solve the actual inverse scheduling problem. With a slight adjustment to the original scheduling, it can reduce the makespan by 11.5%, the worker cost by 8.1% and the machine energy consumption by 27.9% on average.

Energy Consumption Optimization Analysis of LNG Receiving Station Based on Genetic Algorithm

In response to the problem of high energy consumption in LNG receiving stations, based on literature research, numerical simulation combined with genetic algorithm optimization methods were used to establish a calculation model and ASPEN HYSYS steady-state simulation model for the LNG receiving station equipment, each unit process and the overall receiving station process. The various parameters in the LNG receiving station will have a certain impact on the overall energy consumption of the LNG receiving station. Combined with the operating requirements of the LNG receiving station site under different working conditions, and considering the coupling effect of multiple factors, the technology of optimizing the operating parameters of the LNG receiving station to reduce energy consumption is selected, and the adjustable decision-making parameters and their range of changes are selected, and the constraints are set according to the actual situation and Establish the energy optimization objective function of the LNG receiving station process system, and evaluate and analyze the energy consumption indicators of the optimized process system.

Does financial inclusion and information communication technology affect environmental degradation in oil-producing countries?

Advances in financial inclusions have contributed to economic growth and poverty alleviation, addressing environmental implications and implementing measures to mitigate climate change. Financial inclusions force advanced countries to progress their policies in a manner that does not hinder developing countries' current and future development. Consequently, this research examined the asymmetric effects of information and communication technology (ICT), financial inclusion, consumption of primary energy, employment to population ratio, and human development index on CO2 emissions in oil-producing countries (UAE, Nigeria, Russia, Saudi Arabia, Norway, Kazakhstan, Kuwait, Iraq, USA, and Canada). The study utilizes annual panel data spanning from 1990 to 2021. In addition, this study investigates the validity of the Environmental Kuznets Curve (EKC) trend on the entire sample, taking into account the effects of energy consumption and population to investigate the impact of financial inclusion on environmental degradation. The study used quantile regression, FMOLS, and FE-OLS techniques. Preliminary outcomes revealed that the data did not follow a normal distribution, emphasizing the need to use quantile regression (QR). This technique can effectively detect outliers, data non-normality, and structural changes. The outcomes from the quantile regression analysis indicate that ICT consistently reduces CO2 emissions in all quantiles (ranging from the 1st to the 9th quantile). In the same way, financial inclusion, and employment to population ratio constrains CO2 emissions across each quantile. On the other side, primary energy consumption and Human development index were found to increase CO2 emissions in each quantile (1st to 9th). The findings of this research have implications for both the academic and policy domains. By unraveling the intricate interplay between financial inclusion, ICT, and environmental degradation in oil-producing nations, the study contributes to a nuanced understanding of sustainable development challenges. Ultimately, the research aims to guide the formulation of targeted policies that leverage financial inclusion and technology to foster environmentally responsible economic growth in oil-dependent economies.

Novel Additives in Copper Electrorefining-Small Laboratory Scale.

This research aimed to evaluate the effectiveness of new organic substances, including a novel ionic liquid based on polyhexamethylenebiguanidine, polyhexamethyleneguanidine, and safranin in the copper electrorefining process. Experiments were conducted on a small laboratory scale using industrial copper anodes. Single doses of new additives did not improve process indicators (current efficiency, average cell voltage, specific energy consumption) or the quality of copper cathode deposits. However, a combination of a new ionic liquid based on polyhexamethylenebiguanidine and thiourea resulted in a satisfactory current efficiency of 97%, an average cell voltage of 0.110 V, a low specific energy consumption index of approximately 100 kWh/tCu, and smooth cathode surfaces. These results were superior to those obtained with industrial additives (bone glue and thiourea). The findings enhance our understanding of how these substances influence the electrorefining process and suggest the potential for more efficient and sustainable methods. Further research is recommended to validate these findings and explore their industrial applications.

Evaluation indicators for road traffic energy consumption: a review and prospect.

Indicators for evaluating road traffic energy consumption are critical parameters in the research field of road traffic energy consumption. Improving the applicability of energy consumption indicators can promote the development of green transportation in cities. However, there is currently a lack of systematic analysis of energy consumption indicators in research. Therefore, based on a comprehensive analysis of relevant literature, this study divides the indicators for evaluating road traffic energy consumption into two categories: macro (aimed at traffic systems or traffic flow) and micro (aimed at vehicles). These indicators are subdivided into four categories according to their application characteristics, including general, specific, predictive, and comprehensive. This paper provides a complete summary of various evaluation indicators, including their scope of application, advantages, and limitations, and highlights the relationships between them. Additionally, recommendations are made for the future development of evaluation indicators. Research has found that micro-level general indicators serve as the fundamental components of the mathematical structure for almost all other indicators. Specialized indicators primarily evaluate energy consumption in different driving states of vehicles. Predictive indicators are mainly used for assessing transportation energy consumption in simulation conditions. Comprehensive indicators are mainly applied to evaluate the life cycle energy consumption of vehicles or transportation systems. In future research, the performance of indicators can be improved through the design of standardized indicators, enhancement of energy consumption prediction accuracy, and integration of traffic flow parameters. The research contributes to upgrading energy-saving technologies in road transportation and developing sustainable urban transportation systems.

An environmental perspective of energy consumption, overpopulation, and human capital barriers in South Asia

Prior literature is substantive in highlighting the nexus between pollutant and socio-economic predictors; however, the role of human interaction has not been sufficiently explored. Thus, the present study examines the validity of the environmental Kuznets Curve (EKC) hypothesis in the presence of energy consumption, overpopulation, and human capital index in five South Asian countries. It employs fixed effects, random effects, and dynamic panel causality techniques with a set of panel data from 1972 to 2021. The baseline results validate the existence of the EKC hypothesis in the recipient panel. Nevertheless, the findings reveal that energy consumption and population density have positive effects, while human capital has negative impacts on CO2 emissions. Furthermore, the study observes that energy consumption and per capita GDP have a significant causal link with CO2 emissions, whereas CO2 emissions are evident to have causality with population density and human capital index. The results are robust and suggest that the consolidation of an effective regulatory framework and technological improvements are substantial measures to improve environmental quality in South Asia. Moreover, allocating sufficient resources to uplift contemporary educational and health status would be imperative to improving environmental quality as aspired to by the Paris Agreement.

Study on the Impact of Cutting Platform Vibration on Stalk Cutting Quality in Industrial Hemp

In response to the unclear impact of header vibration on cutting performance (stalk cutting quality and cutting energy consumption) during field operations of industrial hemp harvesters, this study utilized a vibration recorder to collect information on header vibration during the operation of industrial hemp harvesters. Through data processing, the dominant range for the resonant frequency and amplitude of the cutting platform is primarily concentrated between 5–45 Hz and 0–35 mm, respectively. Using numerical simulation techniques, a quadratic orthogonal rotation combination experiment was conducted with vibration frequency and amplitude as experimental factors and stalk cutting quality and cutting energy consumption as indicators. Regression equations were established to reveal the relationships between indicators and factors, elucidating the influence of each factor and its interactions on the indicators. Specifically, for the crack length indicator, the amplitude has a highly significant influence on the model, and there is a significant interaction effect between vibration frequency and amplitude on the model. As for the cutting energy consumption indicator, frequency and the interaction between frequency and amplitude significantly affect the model, while amplitude has an extremely significant impact on the model. Through comprehensive fuzzy evaluation, the optimal vibration parameter combination satisfying comprehensive cutting performance indicators was determined as a vibration frequency of 37.86 Hz and an amplitude of 5.34 mm. Furthermore, the reliability of the model has been further validated. This research can provide a reference for improving the field performance of industrial hemp harvesters.

The link between electricity consumption and stock market during the pandemic in Türkiye: a novel high-frequency approach

This article examines the relationship between electricity consumption and the stock market in the Turkish economy during the COVID-19 pandemic. A novel high-frequency model is used, incorporating the hourly energy consumption and Borsa Istanbul (BIST) National stock market index variables. To determine the effect of electricity consumption on the stock market index and vice versa, a high-frequency VAR-based spillover approach, time-varying Granger causality, and time-varying Bayesian VAR analysis are employed. The findings reveal a positive and weak relationship between electricity consumption and the stock market but it has a time-varying nature in an emerging market context in the post-COVID-19 period in the Turkish economy.

Analysis of energy intensity indicators in production of mineral fertilizers

The article presents the results of the calculation of the energy consumption of the main and auxiliary technologies for the production of mineral fertilizers, interrelated as a single energy technology system (ETS). At the stage of the primary analysis of fuel, electricity and heat consumption indicators of individual technologies of the mineral fertilizers industry, the task was set to determine the energy intensity of specific technologies depending on intra-system distribution of flows of fuel and energy resources (FER) between the ETS production facilities for the output of target products.On the basis of a mathematical model, according to the averaged specific consumption rates of fuel and energy resources of the main and auxiliary technologies for the production of one ton of product, the values of specific energy consumption for target technologies for the production of mineral fertilizers were obtained. The analysis of the calculated indicators of energy consumption enabled to identify the most energy-intensive technologies.. It is shown that in the total energy consumption of the target technology, the share of energy consumption of auxiliary industries is up to 97% of the total energy intensity. Besides, the paper cosiders the key factors that determine the level of energy efficiency of industry enterprises under study.Through the example of an existing technology for the production of mineral fertilizers of one technological line with a capacity of 27.5 t/h (in terms of the finished product), the calculation of energy consumption was performed. The paper states that the modernization of the technology for the production of mineral fertilizers through the utilization of thermal SERs emitted into the environment would reduce the energy consumption of the technology by 6.33%.The mathematical model used in the study makes it possible to determine the values of energy consumption of technologies in the mineral fertilizer production industry with the greatest accuracy. The analysis of the data obtained shows that the studied technologies of the industry are characterized by irrational consumption of fuel and energy resources and, therefore, need a more thorough and systematic approach in determining energy saving reserves and ways to rationalize them in the energy technology system (ETS).

Dynamics of stress-index and spectral indicators of cardiointervalogram of elite skiers-racers in the preparatory, competition and transition periods depending on the volume and intensity of training loads

Acutality: Heart rate variability, allows you to assess the state of the autonomic nervous system, which is useful for studying the condition of athletes, including endurance athletes, for example, ski racers. The stress index indicator is often used to assess the impact of stress on a person. It is shown that the stress index is closely correlated with the intensity of metabolism and with the indicators of energy consumption of the body.Objective. To provide information on the values of the stress index for athletes depending on a number of factors, including their sports specialization, and for ski racers on the volume and intensity of the training load during the preparatory, competitive, transitional periods of the ski season.Materials and methods. The literature analysis in the Pubmed database for the last 10 years in English and Russian languages has been carried out.Conclusion. A review of the literature on the magnitude of the stress index showed that athletes who train for endurance, this indicator is low, especially among ski racers, if it is compared with the magnitude of the index in representatives of other sports, as well as with healthy people who do not play sports. There is no information about the value of the stress index in athletes in foreign literature, but there is such information in the domestic literature, which allowed us to present this review, which also includes the results of our research.

Evaluation of integrated transport efficiency and equity at the county level——taking the counties in ningbo city as an example

The evaluation research on integrated transport has mainly focused on urban agglomeration or cities. Yet the integration of various modes of transportation is increasingly being seen at smaller scales, such as counties, due to population concentration and rising affluence levels. Consequently, it is becoming necessary to evaluate the efficiency and convenience of transportation services for people at each location in the county space. This study has evaluated the efficiency and equity of integrated transport from a county perspective. With respect to efficiency indexes, in addition to transportation infrastructure investment, transportation energy consumption, transportation turnover, and other general indicators, we also take into account the accessibility of people to various types of hubs in the county. We use the BCC (Banker, Charnes, Cooper) model in DEA (Data Envelopment Analysis) for efficiency evaluation. Besides, we evaluate transport equity by using the Gini coefficient to reflect the population distribution of the county and GIS-based spatial accessibility of each hub site. Finally, we establish an evaluation matrix of efficiency and equity; we employ a spatial overlay method to classify research counties into different quadrants. By comprehensively considering the relationship between efficiency and equity in integrated transport, this study can identify areas for improvement in transportation development. The case study evaluation in Ningbo counties, using data from 2017 to 2020, provides practical questions and constructive suggestions for integrated transport development in these areas. Overall, this research has important implications for local transportation development and could inform policy decisions related to integrated transport.

Analysis and Prediction of Factors Influencing Carbon Emissions of Energy Consumption Under Climate Change

Climate change is one of the major challenges currently facing the world. The factors influencing the carbon emission of energy consumption and the future trend are important guidance for proposing scientific carbon reduction strategies to mitigate climate change. In this paper, the Logarithmic Mean Divisia Index (LMDI) model and stochastic impacts by regression population, affluence and technology (STIRPAT) model are established to analyze and predict the carbon emission of energy consumption. The LMDI model is used to factorize the CO2 changes generated by residential domestic energy consumption, and to decompose and analyze the carbon emission factors of residential domestic energy consumption in terms of energy carbon emission intensity, energy consumption structure, energy consumption intensity, economic development, and population to determine the driving factors leading to carbon emission changes; based on the above study, we set up nine different development scenarios and applied the scalable stochastic environmental impact assessment model to project energy carbon emissions in 2035; based on carbon emission prediction and analysis, the CO2 emissions of total energy consumption, total electricity consumption, industrial energy consumption and terminal energy consumption were selected, and the correlation coefficients with relevant climate indicators such as temperature change and humidity change were analyzed, and the stress model of energy consumption on climate change was constructed. The results show that: the correlation coefficients of energy consumption indicators and temperature change indicators all pass the significance test at P = 0.01 level, among which the correlation coefficients with temperature difference are the highest, all of them are greater than 0.9 and pass the significance test at P = 0.001 level; among the indicators of energy consumption, the correlation coefficient between total industrial energy consumption and temperature difference was slightly higher than that of total energy consumption and electricity consumption; the stress relationship between the increase of energy consumption and the temperature difference is consistent with the growth of the third polynomial curve.

Does energy consumption play a key role? Re-evaluating the energy consumption-economic growth nexus from GDP growth rates forecasting

Given the strong link between economic growth and energy consumption, we construct a new set of energy consumption indices (ECI) to forecast GDP growth rates. The findings demonstrate the strong predictive power of the new indices. In particular, the electricity consumption index from the industrial sector has an excellent predictive performance and deserves the attention of the government and academia to help formulate economic growth targets. The mixed-frequency model with the Least Absolute Shrinkage and Selection Operator approach (MIDAS-LASSO) enables the full exploitation of energy consumption information for stable forecasting performance, even in the context of crisis fluctuations and geopolitical risk shocks. Furthermore, we apply forecast encompassing tests and variable selection to statistically investigate the predictive information. By analyzing energy consumption characteristics, we contribute to the economic growth hypothesis at the level of energy consuming sectors and provide new insights into GDP forecasting.