Reducing Energy Intensity Research Articles

A nexus study of carbon emissions and financial development in China using the decoupling analysis.

Investigating the linkage between financial development (FD) and carbon emissions is important for mitigating climate change. Nevertheless, there is a scarcity of studies investigating how carbon emissions decouple from FD. Here, we investigate the relationship between FD and carbon emissions by using the decoupling model based on cross-province data of China during 2000–2019. Then, we use the decomposition method to analyze the nine drivers of decoupling elasticity of FD and CO2 emissions. We found that China experienced weak decoupling and strong negative decoupling in most years. Only the finance develops at a very high level; the FD had spare capacities to promote the reduction in the carbon emissions. For example, several developed provinces (e.g., Tianjin, Zhejiang, Guangdong) realized strong decoupling after 2012. The reduction in energy intensity and the increase of foreign direct investment promoted the decoupling of FD from carbon emissions. During the financial recession period, developing a bank-based financial market helped the emissions reduction. Once financial crisis is overcome, developing a market-based financial market promoted the decoupling of FD from emissions. This is because that with the fast FD, the development of stock market contributed to emission reductions through technological improvement, while the bank loans inhibited the decoupling process through the expansion of capital-labor inputs. Overall, these results help in the assessment of the emissions impacts of FD and in addressing climate change problems.

Modeling, validation and exergy evaluation of a thermally-integrated industrial cryogenic air separation plant in Colombia

This work deals with the modeling, computer simulation, validation, and exergy evaluation of a ~40 ton/h industrial air separation plant located in Colombia. The model was implemented in Aspen Hysys® at the corresponding operating conditions of the facility. Initially, the reliability of the model was verified by comparison with data reported in the plant manual (EDM) from the base operating point. An additional validation was performed by comparison with historical records which involved a statistical analysis of plant metadata. The model showed good agreement with both sets of data, displaying deviations< 10%, and it was used to identify that compressors (1325 kW), multichannel heat exchanger (519 kW) and the high-pressure column (448 kW) exhibit the largest exergy destruction rates. By mean of a sensitivity analysis, main controlled variables affecting the process performance were identified. This enabled to detect the feasible operating zones, and a local minimum of exergy destruction for each variable. The overall exergy efficiency was estimated in 0.18, and the specific power consumption with respect to produced oxygen was 1.26 kWh/Nm3 O2. Results indicate that plant performance needs to be highly improved to reduce energy intensity and that the obtained model is suitable for further optimization.

Impact of financial development on the energy intensity of developing countries

Climate change is an important and urgent challenge facing the world, affecting the survival and development of human beings from multiple dimensions such as environment and economy. Carbon emissions are a barometer of climate change, and energy consumption is closely related to carbon emissions. Reducing energy consumption can reduce carbon emissions, thereby optimizing the human living environment. Compared with developed countries, the carbon emissions and energy consumption of developing countries are still growing strongly, so it's significant to study the energy consumption of developing countries. Energy intensity can be reduced through reducing energy intensity, and financial development can have an impact on energy intensity. So, using panel data from 67 developing countries from 1995 to 2018, this study selected six financial development indicators and uses the system Generalized Method of Moments (sys-GMM for short) establishes a dynamic panel model to study the impact of financial development on energy intensity from the six aspects of access, depth and efficiency of financial institutions and financial markets, which are more implementable and more realistic for formulating relevant financial policies to reduce energy intensity. We introduced the index of industrial structure upgrading and studied the interaction between financial development and financial development in the impact of financial development on energy intensity for the first time. The findings revealed that improving financial institutions' access, depth, and efficiency, as well as financial markets' access, depth, and efficiency, might dramatically lower developing countries' energy intensity. The improvement of industrial structure resulted in a reduction in the energy intensity of financial development. Furthermore, the 67 developing countries were grouped from five different perspectives, and the influence of financial development on energy intensity reduction was found to be robust. Therefore, the research results significantly reduce energy intensity for financial development in developing countries.

Efficiency of energy storage control in the electric transport systems

The problems of storage and supplying the energy, together with reducing energy intensity for transport, are now crucial for developing sustainable and reliable transport systems. The energy network must be gradually adapted to new loads and power consumption patterns, especially in railways. The article aims to develop the simulation model to investigate the energy storage systems in its use in the electric transport infrastructure. The authors review selected technical solutions for electric energy storage in transport. The theoretical aspects of energy exchange in the energy storage systems were presented as a base for a continuous simulation model of electric transport power supply. In the non-periodic random voltage input applied to the storage unit, it is pro-posed to use the calculation method based on the Duamel integral to analyze its charge-discharge processes. The resistance functions were applied to analyze the traction power supply mode with variable in time and space by active loads. The simulation showed that the direct connection of the unit to the traction network significantly reduces the traction energy consumption.

Is China's energy policy effective for power plants? Evidence from the 12th Five-Year Plan energy saving targets

We propose the meta-frontier hybrid data envelopment analysis (MHDEA) model, a non-parametric methodology, to assess the total factor carbon performance index (TCPI) of China's fossil fuel power plants from 2005 to 2015. The model allows for both proportional and non-proportional variables within the production technology. In contrast to conventional DEA models, the MHDEA model simultaneously incorporates the characteristics of group heterogeneity, undesirable outputs as well as hybrid measures. In accordance with the definition of the Malmquist-Luenberger (ML) index, we further explore the driving factors of productivity progress, including efficiency change (EC), best-practice gap change (BPC) and technology gap change (TGC). We employ the difference-in-differences (DID) strategy by combining the variations across regions and years to identify the effect of the 12th Five-Year Plan (FYP) energy saving targets on the TCPI of power plants. Frist, we find that the average TCPI of power plants is 0.50 and the average growth rate is 3.58% per year. Second, the productivity progress of the central group is driven by the innovation effect, while the local group stems from the catch-up effect. Third, the energy intensity reduction target is 1% above the average value and the TCPI is expected to increase by 0.32%. We suggest that the Chinese government design different policies for the central and local power plants based on this heterogenicity.

The Impact of Industrial Intelligence on Energy Intensity: Evidence from China

With the sustainable development of cyber-physical science and information technologies, artificial intelligence technology is becoming more and more mature and has been used widely in various walks of life. As one part of this development, industrial intelligence has been applied diffusely to improve the productivity and energy efficiency of factories and governments. Meanwhile, the social ecological environment change has also caused widespread social concern in recent years, and energy efficiency, which is related to climate change, has forced almost every country to reduce their carbon emissions for bettering environmental quality. However, there is little research that has studied this problem from the perspective of industrial robots, even though they are an indispensable part in modern industrial systems. In order to promote the development of artificial intelligence and its application in industrial fields effectively and raise the energy consumption efficiency of production, this paper investigates the impact of industrial intelligence on energy intensity in China, as it is the largest manufacturing and energy consumption country in the world, and we also hope that the experimental results in this study can guide relevant departments and governments to formulate reasonable policies to enhance the utilization efficiency of energy and improve the environmental quality synchronously. For the sake of the rigor of this research and the accuracy of the experimental results, this study explores the corresponding effect mechanisms of industrial intelligence on China’s energy intensity from 2008 to 2019 by using the classical linear regression model OLS (Ordinary Least Squares) and WLS (Weighted Least Squares) separately, which were applied in the previous studies. The results of this study reveal three major findings. The first is that it further proves that the application of artificial intelligence can indeed reduce energy intensity, and the wide applications of artificial intelligence can reduce energy intensity significantly by reducing energy consumption. Besides, the ownership structure of state-owned enterprises will have a positive impact on energy efficiency. The environmental performance of state-owned enterprises is better than that of foreign-funded and private enterprises. Finally, the models further verify the significant impact of the enterprise scale effect on energy intensity. It will bring about the improvement of economic efficiency, and the larger the enterprise, the more obvious the economies of scale effect and the lower the energy intensity.

Analyzing the phased changes of socioeconomic drivers to carbon dioxide and particulate matter emissions in the Yangtze River Delta

Enhancing the environmental collaborative governance to reduce carbon dioxide (CO2) and particulate matter (PM2.5) emissions is an important and significant low-carbon green growth strategy in China. However, few studies have explored the periodical positive and negative impact changes of the drivers in interannual phases on both CO2 and PM2.5 emissions hitherto. With the panel data in the Yangtze River Delta urban agglomeration (YRDUA) from 2005 to 2017, here we analyzed the annual evolution characters of CO2 and PM2.5 emissions and the 10 socioeconomic drivers to these emissions. The spatial Durbin error model (SDEM) by the overall panel was developed to examine the socioeconomic drivers that can synergistically affect CO2 and PM2.5 emissions. Moreover, SDEM by the phased panel was employed to observe the periodical positive and negative impact changes of the drivers. The results reveal that, the drivers that can significantly realize the collaborative governance are normalized difference vegetation index (NDVI), energy intensity (EI), and fixed asset investment (FAI). Main results include: Every 1% increase in the NDVI can reduce CO2 emissions by 0.276% and PM2.5 concentration by 0.223%; every 1% increase in EI can increase CO2 emissions by 0.024% and PM2.5 concentration by 0.003%; and every 1% increase in FAI can increase CO2 emissions by 0.049% and PM2.5 concentration by 0.018%. Increased greening levels, reduced energy intensity, and reduced investment in fixed assets can significantly alleviate CO2 and PM2.5 emissions in the YRD. Furthermore, the phased panel estimation shows that after 2013, the positive impact of economic growth on CO2 and PM2.5 emissions weakens gradually. Similarly, the positive impact of technological upgrading on CO2 emissions begins to diminish, the rise in urbanization still increases CO2 emissions, and the PM2.5 pollution weakens after 2013. Increased traffic loads lead to an increase in PM2.5 emissions, and its effect is gradually enhanced in the later stage. In addition, the positive effect of FAI on CO2 emissions increases gradually and aggravates PM2.5 pollution as well. Therefore, this study suggests that the environmental collaborative governance should focus on reforming the clean energy structure; accelerating the green transformation of industrial structure; and promoting the low-carbon industrial system, energy system, and lifestyle.

Changes in Energy-Related Carbon Dioxide Emissions of the Agricultural Sector in Poland from 2000 to 2019

This paper analyzes the changes in carbon dioxide (CO2) emissions related to energy consumption in the Polish agricultural sector between 2000 and 2019. Based on the Logarithmic Mean Divisia Index (LMDI), the changes in agricultural CO2 emissions are viewed in the context of changes in six factors, i.e., CO2 emission intensity, substitution of fossil fuels, penetration of renewable energies, energy intensity, labor productivity and number of employees. The analysis demonstrated that total energy consumption declined over the study period; this was related to a reduction in the intake of energy derived from solid fossil fuels (−1.05%), crude oil (−1.01%), electricity (−4.89%), and heat (−1.37%), and to an increased consumption of natural gas (5.78%) and biofuels (0.82%). Furthermore, it follows from the analysis that changes in CO2 emissions witnessed in that period were consistent with changes in energy consumption levels; this resulted from a negligible transformation of the energy mix (largely determined by fossil fuels). Generally, CO2 emissions declined over the study period at a rate comparable (−0.9%) to that of the reduction in energy consumption (−1.03%). In light of the LMDI method, the reduction in CO2 emissions from fuel consumption in the Polish agricultural sector was mainly driven by a reduction in energy intensity and in employment. Conversely, rapid growth in labor productivity was the key factor in increasing carbon dioxide emissions. Compared to these impacts, changes in other factors (i.e., emission intensity, energy mix and penetration of renewable energies) had an extremely small or marginal effect on the variation in CO2 emissions.

The Driving Forces of Energy-Related CO2 Emissions in the United States: A Decomposition Analysis

This paper uses the logarithmic mean Divisia index (LMDI) approach to decomposition analysis to identify the factors that influence changes in carbon dioxide (CO2) emissions in the United States.&amp;nbsp; The LMDI approach decomposes CO2 emissions into specific determinants.&amp;nbsp; The data set includes the 50 states plus the District of Columbia from 1998 &amp;ndash; 2018.&amp;nbsp; The five factors that influence the change in CO2 emissions include the emissions per unit of fossil fuel consumption, share of fossil fuels in total energy consumption, energy intensity, GDP per capita, and population.&amp;nbsp; The results indicate that, during the 20-year period, CO2 emissions declined in 36 states plus the District of Columbia.&amp;nbsp; The reduction in energy intensity served as the most important factor in the change of CO2 emissions, offsetting 63 percent of the effects of per capita GDP and population.&amp;nbsp; From the perspective of climate change, the importance of a change in energy intensity demonstrates the effectiveness of a decrease in primary energy consumption per unit of GDP.&amp;nbsp; &amp;nbsp;

Eliminating the need for anodic gas separation in CO2 electroreduction systems via liquid-to-liquid anodic upgrading

Electrochemical reduction of CO2 to multi-carbon products (C2+), when powered using renewable electricity, offers a route to valuable chemicals and fuels. In conventional neutral-media CO2-to-C2+ devices, as much as 70% of input CO2 crosses the cell and mixes with oxygen produced at the anode. Recovering CO2 from this stream adds a significant energy penalty. Here we demonstrate that using a liquid-to-liquid anodic process enables the recovery of crossed-over CO2 via facile gas-liquid separation without additional energy input: the anode tail gas is directly fed into the cathodic input, along with fresh CO2 feedstock. We report a system exhibiting a low full-cell voltage of 1.9 V and total carbon efficiency of 48%, enabling 262 GJ/ton ethylene, a 46% reduction in energy intensity compared to state-of-art single-stage CO2-to-C2+ devices. The strategy is compatible with today’s highest-efficiency electrolyzers and CO2 catalysts that function optimally in neutral and alkaline electrolytes.

Spatio-temporal evolution relationships between provincial CO2 emissions and driving factors using geographically and temporally weighted regression model

• There was a significant positive spatial correlation between provincial CO 2 emissions in China. • Spatio-temporal heterogeneity analysis of driving factors of CO 2 emissions was carried out using GTWR model. • Inter-provincial differences in the impact of industrial structure on CO 2 emissions were minimal. • Energy intensity is a key factor in achieving dual reduction in provincial CO 2 emissions, followed by trade openness. To achieve the 2060 carbon neutrality target, each province in China needs to ensure rapid reduction in carbon dioxide (CO 2 ) emission according to its own developmental characteristics. Meanwhile, to achieve sustainable emission reduction, it is important to explore the development path of dual reduction of total CO 2 emissions and CO 2 emission intensity in each province. Based on the data of 30 provinces in China for the period 2005–2019, in this study, we analyzed the spatial and temporal evolution trends of CO 2 emissions in each province and determined the spatial autocorrelation of provincial CO 2 emissions. We used the geographically and temporally weighted regression (GTWR) model to analyze the spatio-temporal evolution of the driving factors of provincial CO 2 emissions. The results showed that CO 2 emission intensity of each province gradually decreased, and the CO 2 emissions between provinces were spatially autocorrelated. Energy intensity had the highest influence on total CO 2 emissions, and the influence of trade openness on CO 2 emission intensity had the largest inter-provincial differences. At present, reducing energy intensity and the proportion of secondary industries, improving trade openness, and using electricity alternatives are the key for some provinces to achieve dual reduction of total CO 2 emissions and CO 2 emission intensity.

Effect of China’s technology spillovers on energy intensity in Africa

Empirical studies on the effects of China–African economic relations on energy intensity in Africa are scarce. To fill the gap in the literature, this study investigates the technology spillover effects of the China–Africa trade and investment relations on energy intensity. It uses both linear and nonlinear dynamic panel estimation methods for 42 African countries from 2003 to 2015. The results show that China's technology spillover through imports significantly reduces energy intensity in Africa. The findings are consistent across specifications and sample groups. Moreover, the technology spillover coming from foreign direct investment (FDI) improves energy intensity, particularly in lower-middle-income African countries. The dynamic threshold estimation results show that countries' absorptive ability is important for technology spillover effects of FDI and imports on energy intensity. The results suggest that countries' absorptive capacity should be increased to maximize the benefits of trade and investment technology spillovers.

A systematic assessment of city-level climate change mitigation and air quality improvement in China

China is facing dual challenges of air pollution and climate change. By using city-level data, we comprehensively assessed air quality and CO2 emission changes from 2015 to 2019 for 335 Chinese cities. We selected important regions for air pollution control and categorized all cities into different classes according to their development levels. Our novel approach revealed new insights on different patterns of changes of PM2.5, O3, and CO2 by region and city class. We found that PM2.5 concentrations decreased remarkably due to mandatory city-level reduction targets, especially in the Beijing-Tianjin-Hebei (−27%) region. Nonetheless, O3 concentrations and CO2 emissions increased in 91% and 69% of Chinese cities, respectively. Observed CO2 emission reductions in more developed cities were mainly due to prominent energy intensity reduction and energy structure improvement. Our study indicates a lack of synergy in air pollution control and CO2 mitigation under current policies in China. To address both challenges holistically, we suggest setting mandatory city-level CO2 emission reduction targets and reinforcing clean energy and energy efficiency measures.

Dynamics of the carbon intensity of the economy by macro-regions using the intensity index of energy intensity reduction

The paper studies the dynamics of the energy intensity of the economies of the countries of the world. It is shown that by 1980 the process of reducing the energy intensity of the economy began only in the developed countries of North America, Europe, the Asia-Pacific region, as well as in China. The intensity index of energy intensity reduction was calculated, which made it possible to identify the features of the dynamics of GDP energy intensity in developed and developing countries. The presence of convergence in the time series of energy intensity and carbon intensity of the economy of all macroregions, with the exception of the Middle East, where divergence is observed, is determined.

Analyzing the green financing and energy efficiency relationship in ASEAN

PurposeThe need to improve energy efficiency as an essential factor for achieving the Sustainable Development Goals (SDGs) through green financing is one of the most important issues worldwide. It is even more important for ASEAN (Association of Southeast Asian Nations) countries because of their potential for economic growth and the challenge of their environmental problems. This paper therefore addresses the question of whether and how green finance (with the proxy of issued green bonds [GBs]) promotes energy efficiency (with the proxy of energy intensity) in the ASEAN member countries.Design/methodology/approachThe paper runs a two-stage generalized method of moments (GMM) system model for the quarterly data over the period 2017–2020. It also uses a linear interaction model to explore how the pandemic may affect the relationship between green finance and energy efficiency in this region.FindingsThe main results only demonstrate the short-term negative impact of GBs on energy intensity. Furthermore, per capita income, economic integration and renewable energy supply can be used as potential variables to reduce energy intensity, while modernization in ASEAN increases energy intensity. Establishment of digital green finance, long-term planning of a green finance market, trade liberalization and policies to mitigate the negative impacts of COVID-19 are recommended as golden policy implications.Research limitations/implicationsThe present study has several limitations. First, it accounts for explanatory variables by following a number of previous studies. This may lead to omissions or errors. Second, the empirical estimates were conducted for 160 observations due to the repositioning of GBs in ASEAN, which is not bad but not good for an empirical study.Originality/valueTo the best of authors' knowledge, there has not been any in-depth study focusing on the relationship between energy efficiency and green financing for the case of ASEAN economies.

Additive economy and new horizons of innovative business development

The disruptive technologies and cyber-physical production systems are important factors that bring transformations to socio-economic formations. The paper aims to formulate the content, key directions, positive and negative effects of additive economy (AE) in the current transition phase to Industry 4.0. The research method is based on the analysis of structural links in socio-economic systems, where the additive economy potential is realized. The additive economy is treated as a new approach to production technological aspect based on the additive principle of manufacturing and aimed at minimizing the use of primary natural resources for dematerialization of social production. AE is the antithesis of the subtractive economy, which dominates today and uses only a tiny proportion of extracted natural resources. Among the positive effects of AE, there are the reduction in energy intensity of products, dematerialization of production, solidarity of society, economic systems sustainability, and intellectualization of technologies and materials. Among the negative expectations of AE, there are increased information vulnerability of production, risk of losing control over cyber-physical systems, expanding the unification of individuals, and increasing psychological stress. The additive economy is more sustainable than the subtractive economy since it does not require extra components to the production spheres, reduces the resource scarcity, and could satisfy more economic agents’ needs. Therefore, improved production efficiency due to AE promises economic growth acceleration, environmental burden and social risk reduction. Acknowledgment The publication was prepared in the framework of the research projects “Sustainable development and resource security: from disruptive technologies to digital transformation of Ukrainian economy” (№ 0121U100470); Fundamental bases of the phase transition to an additive economy: from disruptive technologies to institutional sociologization of decisions (No. 0121U109557).

Validation of the environmental Kuznets curve hypothesis and role of carbon emission policies in the case of Russian Federation.

Climate change currently observed and expected in the future is associated with risks to security and sustainable development and natural and irreversible consequences. To minimize these risks, it is necessary to adapt the public administration, economic sectors, and regional infrastructure to the changing climate conditions. This paper discovers the links between CO2 emissions and their key determinants such as economic growth, energy consumption, population, trade openness, and financial development including the period from 1990 to 2020 to test the environmental Kuznets curve (EKC) hypothesis by using ARDL bound test for the Russian Federation. Findings reveal that energy consumption and population have a positive impact on CO2 emissions, while economic growth, financial development, and trade openness have been found to decrease CO2 emissions in the long term. The results of this paper show that there is a “U”-shaped relationship between CO2 emissions and economic growth in the Russian Federation. This shows that EKC is valid up to a certain income level in the Russian Federation, and when this income level is exceeded, a positive relationship will begin between economic growth and environmental degradation. As a policy implementation, policymakers must implement clean energy technology policies to achieve the 2060 net zero carbon target. Policies such as fossil-based energy use and reducing energy intensity should be adopted.

Behavioural and environmental sustainability determinants of residential energy management information systems use

Notwithstanding appliance energy efficiency and advances in Smart Home Technologies (SHT), residential energy consumption is still on the rise. Residential Energy Management Information Systems (REMIS) that enable users to monitor, analyse, and benchmark energy use and consumption can contribute to energy savings and mitigate unnecessary consumption. Nevertheless, the withdrawal of Microsoft's Hohm and Google's Powermeter and the low adoption rate of smart meters in the US, UK, Canada, and Australia signposts missed opportunities in reducing energy intensity. Thus, the question of how to influence the successful adoption of REMIS and other related sustainable technologies captivates both researchers and practitioners. In this paper, based on a conceptual model developed by integrating the theory of planned behaviour and pro-environment behaviour research, we analysed survey data from 208 Singapore testbed eco-town apartment precinct households. The study found that environmental altruism and sustainability information exposure induce normative pressures towards REMIS use and a sense of volitional control over REMIS use. Furthermore, presence of children and the apartment size moderate the causal relationship between attitude, subjective norm and perceived behavioural control concerning REMIS adoption. These findings offer theoretical and empirical insights to cleaner production, residential energy consumption research and other sustainable technologies use that can contribute to UN's Sustainable Development Goals (SDGs). It also discusses the strategic, policy and design implications for energy and technology providers, and residential energy users.

Modeling tools for improving energy efficiency of water drainage complexes at iron ore underground mines

The natural presence of water in underground mining processes creates significant difficulties and requires constant water drainage, i.e. pumping water to the day surface into special reservoirs. The so-called main drainage complexesare used for perform-ing these operations. Mine water drainage is an energy-consuming process. Therefore, the need to reduce energy intensity of water drainage complexes is a relevant task, which is so far unsolved to a level that is fully adequate. To solve the above-mentioned prob-lem on a systemic basis, it is of primary importance to assesstools of improving energy efficiency of water drainage complexes ac-cording to their efficiency levels. It is vital for further formatting the scientific search for options that possess sufficient efficiency. For such technology-intensive types of consumers as underground mines, modeling of power consumption is an effective and afford-able method of obtaining preventive results, which are close to real-life conditions. Of course, the level of adequacy of the modeling results obtained depends on compliance of input parameters of a model with real values. Such a model of the electric power system as a complex of groundwater drainage to the day surface can be used as a supporting option for modeling in order to get the required results for both developing energy-efficient control measures for drive electromechanical complexes and designing an effective struc-ture of the entire drainage complex. It is in this perspective that the research methodology is built, the results of which are outlined in the article.Modeling the levels of efficiency in the use of some modern trends in improving the operation of drainage complexes of iron ore underground production made it possible to rank them according to the levels of implementation, starting with the use of controlled electric drives and ending with the optimization of their operating modes.

Modeling tools fo rimproving energy efficiency of water drainage complexes at iron ore underground mines

The natural presence of water in underground mining processes creates significant difficulties and requires constant water drainage, i.e. pumping water to the day surface into special reservoirs. The so-called main drainage complexesare used for perform-ing these operations. Mine water drainage is an energy-consuming process. Therefore, the need to reduce energy intensity of water drainage complexes is a relevant task, which is so far unsolved to a level that is fully adequate. To solve the above-mentioned prob-lem on a systemic basis, it is of primary importance to assesstools of improving energy efficiency of water drainage complexes ac-cording to their efficiency levels. It is vital for further formatting the scientific search for options that possess sufficient efficiency. For such technology-intensive types of consumers as underground mines, modeling of power consumption is an effective and afford-able method of obtaining preventive results, which are close to real-life conditions. Of course, the level of adequacy of the modeling results obtained depends on compliance of input parameters of a model with real values. Such a model of the electric power system as a complex of groundwater drainage to the day surface can be used as a supporting option for modeling in order to get the required results for both developing energy-efficient control measures for drive electromechanical complexes and designing an effective struc-ture of the entire drainage complex. It is in this perspective that the research methodology is built, the results of which are outlined in the article.Modeling the levels of efficiency in the use of some modern trends in improving the operation of drainage complexes of iron ore underground production made it possible to rank them according to the levels of implementation, starting with the use of controlled electric drives and ending with the optimization of their operating modes.