Level Of Electricity Consumption Research Articles

An Overview of Electricity Consumption in Europe: Models for Prediction of the Electricity Usage for Heating and Cooling

Although aggregate electricity consumption provides valuable information for market analysis, demand composition, including industrial, residential, illumination, and other uses, and special days, such as national or religious holidays and annual industrial shutdowns, differ for each country. This paper analyzes the hourly electricity consumption of European countries in the European Transmission System Operation for Electricity (ENTSO-E) grid from 2006 to 2018. We propose an outlier detection method to identify special days and a modulated Fourier Series Expansion model to determine the breakdown of industrial versus household consumption and heating versus cooling consumption. The proposed outlier detection method uses the time series for each hour and checks whether a day has more than a threshold number of hours with exceptional electricity consumption levels. The proposed demand prediction model has a 3% average error when electricity usage for heating is not dominant. It also allows country classification based on consumption patterns to efficiently manage regional or country-based electricity markets.

Investigation of Key Technologies for Energy Saving and Consumption Reduction in Chongqing Municipal Wastewater Treatment Plants Based on Carbon Emission Reduction Contribution

<p>In this paper, based on the factors of treatment process, treatment scale, as well as water quality, the level and distribution of electricity consumption, drug consumption, and indirect carbon emission of five typical wastewater treatment plants (WWTPs) were examined. The distribution of electricity consumption within the WWTPs were analyzed in terms of wastewater treatment units. The results uncovered that the biological treatment unit was the treatment unit with a high percentage of electricity consumption in the WWTPs. Carbon emissions of main units in the WWTPs presented that the aeration blower, sewage lifting pump, submersible pusher, phosphorus remover and return sludge pump of the biological treatment unit were the top 5 emission units of carbon emissions in the WWTPs, and the key influencing factors of the carbon emissions of the main carbon emission units had been analyzed. Combined with the current situation of sewage treatment energy consumption in Chongqing, the analysis put forward a library of energy saving and consumption reduction measures for Chongqing WWTPs, which applied them to the sewage treatment plant A energy saving and consumption reduction renovation project.</p>

Comparing different approaches of agent-based occupancy modelling for predicting realistic electricity consumption in office buildings

Having a good grasp on modelling the dynamics of occupants for estimating electricity consumption in office buildings is a vital asset for realistic predictions. Nowadays, agent-based models are widely used for this purpose. Previous approaches to modelling dynamics of occupants in multi-floor office buildings simplified the models by teleporting agents between zones during transitions without considering the routes used to reach their final destination such as going through corridors, stairways and hallways, thus, underestimating the potential energy usage during those transition period. This paper proposes a more realistic approach by incorporating detailed routes of agent movement when transiting from one zone to another. To demonstrate the case, detailed routes and route choice preferences are used as inputs within the model for the agents to make independent decisions when transiting from one place to another within the simulated office building. The route choice preferences are computed from data gained from an earlier extensive real world occupancy detection trial conducted within the model office building using state of the art indoor positioning system. The simulation experiments compare the previous approach against the proposed approach and based on the evaluation it is found that there is approximately 19% underestimation of electricity consumption per day when detailed routes are not considered. The research demonstrates, the proposed approach is applicable to any office buildings and will produce predictions which will be much more realistic and closer to the real world electricity consumption level.

TEKSTİL ENDÜSTRİSİNDE DERİN ÖĞRENME KULLANARAK AŞIRI ELEKTRİK TÜKETİMİNİN ÖNLENMESİNE YÖNELİK BİR VAKA ÇALIŞMASI

Resources are the most critical input in the manufacturing industry therefore, resource consumption is an essential issue to be minimized. On the other hand, consumption depends on several parameters thus, it is difficult to estimate. Recently, Machine Learning (ML) and Deep Learning (DL) are powerful Artificial Intelligence (AI) subdomains for future prediction in any area. In this paper, a DL-supported electricity prediction method is designed for the textile industry as a case study in order to prevent resource over-consumption while the machines are in the standby state. This method provides dynamic consumption thresholds of electricity consumption by sliding window technique based Long-Short Term Memory (LSTM) model that helps the machines to interrupt manufacturing in their decision. These calculated thresholds are also compared with the results of Recurrent Neural Networks (RNN) and Gated Recurrent Units (GRU) as the other DL methods and Automated Regressive Integrated Moving Average (ARIMA) as a traditional method and then the results have been analyzed how close they are to real-time electricity consumption data at standby. According to the results, the LSTM model successfully predicts electricity consumption levels, sends an interrupt signal to Programmable Logic Controller (PLC) unit when the consumption levels reach the threshold and therefore prevents resource over-consumption.

Modified Analytic Hierarchy Process in Formulating Health Index Model for Substation Equipment

The level of electricity consumption per capita in Indonesia has increased by 6.6% per year on average within 2009 to 2019 and is predicted to grow until 2060. Therefore, the importance of transmission system reliability has been escalated to secure the fulfillment of electrical energy needs. A good maintenance strategy needs to be carried out in order to ensure the reliability of transmission system, one of which is through implementation of asset health index. This research aims to propose a framework that combines the Failure Mode Effect Analysis (FMEA), Analytic Hierarchy Process (AHP), and Simple Additive Weighting (SAW) methods to obtain a mathematical model of asset health index. Case study was conducted at PLN UIP3B Sulawesi in order to formulate health index model for medium voltage cubicle through quantifying the opinions of experts and practitioners in the field of 20 kV cubicle maintenance. The conclusions obtained are: 1) the use of framework based on FMEA, AHP, and SAW is able to produce multicriteria decision making model through objective quantitative approach; 2) the framework can be used as a tool to convert qualitative opinions into a series of quantitative weighting factors; 3) the framework was successfully applied to the case study of formulating cubicle health index model in UIP3B Sulawesi; 4) the framework has a good level of scalability so that in can accommodate further addition of other expert opinion in the future.

Adapting to climate change: substitution effect of water on residential electricity consumption

PurposeAs climate change impacts residential life, people typically use heating or cooling appliances to deal with varying outside temperatures, bringing extra electricity demand and living costs. Water is more cost-effective than electricity and could provide the same body utility, which may be an alternative choice to smooth electricity consumption fluctuation and provide living cost incentives. Therefore, this study aims to identify the substitute effect of water on the relationship between climate change and residential electricity consumption.Design/methodology/approachThis study identifies the substitute effect of water and potential heterogeneity using panel data from 295 cities in China over the period 2004–2019. The quantile regression and the partially linear functional coefficient model in this study could reduce the risks of model misspecification and enable detailed identification of the substitution mechanism, which is in line with reality and precisely determines the heterogeneity at different consumption levels.FindingsThe results indicate that residential water consumption can weaken the impact of cooling demand on residential electricity consumption, especially in low-income regions. Moreover, residents exhibited adaptive asymmetric behaviors. As the electricity consumption level increased, the substitute effects gradually get strong. The substitute effects gradually strengthened when residential water consumption per capita exceeds 16.44 tons as the meeting of the basic life guarantee.Originality/valueThis study identifies the substitution role of water and heterogeneous behaviors in the residential sector in China. These findings augment the existing literature and could aid policymakers, investors and residents regarding climate issues, risk management and budget management.

What influences industrial enterprises’ willingness of demand response: A survey in Qinghai, China

Although demand response (DR) is an important means for solving the stability and reliability challenges of renewable energy (RE) power systems, it remains unclear how DR can be widely applied in industrial enterprises, particularly in areas with a high proportional RE system and industrial energy consumption that do not apply any DR programs. The objective of this study is to understand the influencing factors of industrial enterprises' willingness to participate (WTP) in DR in these areas, including one economic factor and three noneconomic factors. This study performs a questionnaire survey in Qinghai, and a total of 556 industrial enterprises completed valid questionnaires, these data are empirically analyzed through Structural Equation Model. The results demonstrate enterprises' DR awareness degree is the most significant predictors of the WTP in DR. Meanwhile, enterprises' electricity consumption level also had a significant impact on the WTP in DR rather than enterprises' potential DR capabilities and the expected minimum compensation. Finally, the study suggests feasible recommendations, such as increasing enterprises' ability of analyzing potential DR, directly encouraging enterprises to participate in DR, designing compensation standards and adopting differentiated DR according to enterprises’ actual DR potential.

Has green finance boosted regional electricity consumption? Based on nighttime light data.

Green finance is gradually becoming essential to enhancing regional electricity consumption. This paper uses panel data from 30 Chinese provinces and regions from 2011 to 2021 and develops a system Generalized Method of Moments (GMM) and a mediating effect model to test the effects of green finance on regional electricity consumption level and the mechanism of action. The results show that green finance significantly boosts regional electricity consumption level, which still holds after various robustness tests. In terms of heterogeneity, the promotion effect of green finance on electricity consumption is significantly more substantial in the Eastern regions than in the central and western regions; the regions with a higher level of urbanisation are more robust than those with a lower level of urbanisation, and the regions with a higher level of the advanced industrial structure are stronger than those with a lower level. In terms of impact mechanisms, both foreign direct investment and green technology innovation have partial mediating effects on the impact of green finance on regional electricity consumption level. Green finance raises the level of regional electricity consumption by increasing the level of green technology innovation. At the same time, foreign direct investment has a "substitution effect" on the impact of green finance on regional electricity consumption. Therefore, it is essential to increase green financial policy support to raise regional electricity consumption, help upgrade electricity consumption, and help achieve the "double carbon goal".

A scenario framework for electricity grid using Generative Adversarial Networks

This study proposes a synthetic data framework to decrease the cost of smart metering. It offers clustering of real-time buses based on their electricity consumption levels and synthetic data generated by Generative Adversarial Networks (GANs) to provide sufficient information and data in grid modeling. GANs can be used to predict the electrical dissipation profiles of buses and their total energy consumption since they replicate the distributional features of the recorded data. This study is expected to facilitate energy distribution companies and grid operators in the progress of efficient and intellectual modeling of smart grids by stimulating the bottom-up contribution of end-users. It aims to decrease the data collection cost while allowing realistic scenarios. Furthermore, it introduces a new metric to measure the accuracy of the GANs and illustrates some empirical analysis using single consumer data.

The Effects of Urban Sprawl on Electricity Consumption: Empirical Evidence from 283 Prefecture-Level Cities in China

Under the urban development trend of sprawl, improving energy use efficiency is a proper way to promote green and low-carbon construction in cities. This paper uses panel data from 283 prefecture-level and above cities in China from 2008 to 2019 to measure the urban sprawl index, and analyze the spatial-temporal evolution law of urban sprawl and electricity consumption. The relationship between urban sprawl and electricity consumption is empirically examined, and the differential effect of urban sprawl on electricity consumption is analyzed. Finally, the impact of urban sprawl on electricity consumption based on a spatial perspective is explored in depth by establishing a spatial error model. We found the following: (1) The levels of urban sprawl and urban electricity consumption are on the rise. The spatial distribution of urban sprawl is more dispersed, and cities with high electricity consumption levels are mostly concentrated in the eastern coastal areas. (2) Urban sprawl exacerbates electricity consumption, and this conclusion is still robust after a series of robustness tests were conducted and endogeneity issues were taken into account. In terms of the influence mechanism, urban sprawl mainly affects electricity consumption by changing the allocation of land resources, increasing the dependence on private transportation, and inhibiting green technology innovation. (3) The incremental effect of urban sprawl on electricity consumption is more pronounced in cities with high sprawl levels, weak environmental regulations, and low green innovation levels, as well as in west cities. (4) Urban sprawl and electricity consumption both have a significant positive spatial correlation. Electricity consumption of cities is not only related to their own regions but also influenced by the adjacent regions, and the spatial correlation is mainly reflected in the random error term. This paper deepens the understanding of the basic laws of urban sprawl affecting urban low-carbon development, which also has implications for new urbanization strategies and green development.

Bottom-up assessment of household electricity consumption in dynamic cities of the Global South—Evidence from Kigali, Rwanda

Data on electricity consumption is crucial for assessing and modeling energy systems, making it a key element of sustainable urban planning. However, many countries in the Global South struggle with a shortage of statistically valid, geocoded, and disaggregated household-level data. This paper aims to develop a generic methodology for the generation of such a database in terms of electricity consumption. The methodology was tested in Kigali, the capital city of Rwanda, with a focus on all single-family residential building types of the inner city. Discrete data on buildings is obtained through combined information products derived from very high resolution (VHR) satellite imagery, field surveys, and computer assisted personal interviewing. In total, 509 valid geocoded survey datasets were used to evaluate and model household electricity consumption, as well as electrical appliance ownership. The study's findings reveal that the arithmetic mean of specific electricity consumption was 3.66 kWh per household per day and 345 kWh per capita per year in 2015. By subdividing the data into distinct building types as well as their spatial location, and weighting the specific values according to their proportion in the study area, a more accurate mean value of 1.88 kWh per household per day and 160 kWh per capita per year was obtained. Applying this weighted mean to extrapolate household electricity consumption for the study area, in conjunction with the sample's precision level, resulted in an estimate of 126–137 GWh for the year 2015. In contrast, using the arithmetic mean would have led to values twice as high, even exceeding the total electricity consumption of the entire city, including multi-family and non-residential buildings. The study highlights the significance of on-site data collection combined with geospatial mapping techniques in enhancing of understanding of residential energy systems. Using building types as indicators to distinguish between households with contrasting electricity consumption and electrical appliance load levels can address the challenges posed by rapid urban growth in the Global South. This proposed method can assist municipal administrations in establishing a database that can be updated resource-efficiently at regular intervals by acquiring new satellite images.

Wasserstein‐metric‐based distributionally robust optimization method for unit commitment considering wind turbine uncertainty

AbstractThe penetration of wind turbines in the power grid is increasing rapidly. Still, the wind turbine output power has uncertainty, leading to poor grid reliability, affecting the grid's dispatching plan, and increasing the total cost. Thus, a distributionally robust optimization method for thermal power unit commitment considering the uncertainty of wind power is proposed. For this method, energy storage and interruptible load are added to simulate increasingly complex electricity consumption scenarios. Furthermore, the amount of load cutting reflects the satisfaction level of electricity consumption on the user side. Based on Wasserstein metric, an ambiguity set is established to reflect the probabilistic distribution information of the wind power uncertainty. An ambiguity set preprocessing method is proposed to depict the probability distribution of ambiguity set more clearly, to minimize the operation cost under the condition that the uncertainty of wind turbine output power obeys the extreme probabilistic distribution of the ambiguity set. The test case in a modified version of the IEEE 6‐bus system shows that the proposed method can flexibly adjust the robustness and economy of optimization decisions by controlling the sample size and the confidence of Wasserstein ambiguity set radius. In addition, the proposed ambiguity set preprocessing method can obtain more economical dispatching decisions with a smaller sample size.

Inequality in electricity consumption and economic growth: Evidence from a small area estimation study.

Our study uses a small area estimation method to estimate the average and inequality of per capita kWh consumption for small areas in Vietnam. It shows evidence of a large spatial heterogeneity in the electric power consumption between districts and provinces in Vietnam. Households in the mountains and highlands consumed remarkably less electricity than those in the delta and coastal areas. Notably, we find a U-shaped relationship between the inequality of electricity consumption and economic levels in Vietnam. In poor districts and provinces, there is very high inequality in electricity consumption. Inequality is lower in middle-income districts and provinces.

A Novel Evasion Attack Against Global Electricity Theft Detectors and a Countermeasure

The smart grid advanced metering infrastructure (AMI) is vulnerable to electricity theft cyber-attacks in which malicious smart meters report low readings to reduce the consumers’ bills. To avoid this problem, several machine-learningbased detectors have been proposed to detect electricity theft. Most of these detectors are global in the sense that they are trained on different consumption levels, including low and high consumptions, to be used for all consumers. In this paper, we introduce a novel type of evasion attacks against global detectors as follows. A malicious consumer who has high consumption level can send false readings for a low consumption profile (that resembles the profiles the detector is trained on) to evade the detector, i.e., steal electricity without being detected. We firstly conduct experiments to prove that the existing global detectors are vulnerable to this new kind of evasion attacks. To launch this attack, we train a Generative Adversarial Network (GAN) on a real dataset to generate fake low consumption readings that can evade the detector. The given results indicate that the success rate of the attack is between 82% and 97%. To thwart this attack, we divide the consumers into clusters of close electricity consumption levels and train one detector for each cluster. Therefore, if a malicious consumer in any cluster tries to imitate the consumption profiles of consumers in other clusters, he/she will be detected. On the other hand, it is not profitable to imitate the electricity consumption profiles of consumers in his/her cluster to evade detection. To prove the effectiveness of our countermeasure, extensive experiments are conducted and the results indicate that our countermeasure can successfully thwart the attack.

Electricity supply quality and use among rural and peri-urban households and small firms in Nigeria

We present a household and enterprise energy survey dataset collected within the framework of the PeopleSuN project in Nigeria in 2021. Across three Nigerian geopolitical zones, a total of 3,599 households and 1,122 small and medium-sized enterprises were surveyed. The sample is designed to be representative of rural and peri-urban grid-electrified regions of each zone. Our surveys collect data on demographic and socioeconomic characteristics, energy access and supply quality, electrical appliance ownership and usage time, cooking solutions, energy related capabilities, and supply preferences. We encourage academic use of the data presented and suggest three avenues of further research: (1) modelling appliance ownership likelihoods, electricity consumption levels and energy service needs in un-electrified regions; (2) identifying supply-side and demand-side solutions to address high usage of diesel generators; (3) exploring broader issues of multi-dimensional energy access, access to decent living standards and climate vulnerability.

ФІНАНСОВА БЕЗПЕКА УКРАЇНИ ТА ОСОБЛИВОСТІ ЇЇ ІНСТИТУЦІЙНИХ ДЕФОРМАЦІЙ

The article examines the peculiarities of institutional deformations of the financial security of Ukraine. It was established that the functioning of the national economy of Ukraine in the conditions of the Russian-Ukrainian war creates additional risks of the negative impact of macroeconomic, financial and social factors on the economic security of the country. It was determined that the main institutional threats to the financial security of Ukraine in modern conditions are the shadow economy, corruption, raiding and unproductive capital outflow. First of all, the shadow economy is considered as an economic activity that is not taken into account, not controlled and not taxed by the state, aimed at obtaining income by violating the legislation of Ukraine. In Ukraine, the shadow economy accounts for almost a third of the produced GDP, and in 2010–2022 its size varies between 28–36 %. The calculation of the integral indicator of shadowing of the economy is calculated with the help of various methods: energy (level of electricity consumption), monetary (demand and supply of cash), estimation of population expenditure / retail turnover and unprofitability of enterprises. It was established that among the factors restraining the processes of detinization of the national economy are a low level of property rights protection, an insufficient level of intellectual property protection, a low level of stock market liquidity, the protection of investors’ rights and the inability of the regulator to counter abuses of money, etc. Secondly, it was established that the anti-corruption rating for 2022 has worsened, the country received 33 points out of 100 possible in the Corruption Promotion Index. It was determined that the dynamics of this indicator is improving, but there has been a certain “stagnation” in the fight against corruption in recent years. Thirdly, it was established that raiding complicates doing business and worsens investment attractiveness, and in conditions of martial law, increases the risk of illegal seizure of other people’s property. Raiding is not only a threatening institutional deformation, but also the most acute business problem. The problems of raiding are solved by tracking registration actions that are carried out with immovable property. It has been established that unproductive capital outflow is an illegal and inefficient operation of capital withdrawal abroad. Since the beginning of 2022, the Russian-Ukrainian war has significantly affected investor confidence and caused capital outflows, which has jeopardized the country’s economic and financial stability. Key words: shadow economy, raiding, corruption, unproductive capital outflow, financial security.

Impact of climate and geological storage potential on feasibility of hydrogen fuels

Electrofuels, including hydrogen, methane and ammonia, have been suggested as one pathway in achieving net-zero greenhouse gas energy systems. They can play a role in providing an energy storage and fuel or feedstock to hard-to-abate sectors. In future energy systems their role is often studied in case studies adhering to specific region. In this study we study their role by defining multiple archetypal energy systems, which represent approximations of real systems in different regions. Comparing the role of electrofuels across the cost-optimized systems relying only on renewable energy in power generation, we found that hydrogen was a significant energy vector in all systems, with its annual quantity approaching the classic electricity demand. The role of renewable methane was very limited. Electrofuel storages were needed in all systems and their capacity was the highest in the northern Hemiboreal system. Absence of cavern storage potential did not hamper the significance of electrofuels but increased the role of ammonia and led to average 5.5% systemic cost increase. Systems where reservoir hydropower was scarce or level of electricity consumption was high, needed more fuel storages. The findings of this study can help for better understanding of what kind of storage and generation technologies will be most useful in future carbon-neutral systems in different types of regions.

Optimizing the Performance of Commercial Demand Response Aggregator Using the Risk-Averse Function of Information-Gap Decision Theory

Power systems face challenges with regard to handling the high penetration of renewable energies, including energy intermittency and fluctuations, which are not present in conventional electricity systems. Various flexibility models have been developed to address these fluctuations, including demand-side flexibility, which offers a practical solution with which to overcome these challenges in all demand sectors, including the commercial sector. This paper proposes a new structure for the participation of the commercial sector in the electricity market to integrate and coordinate the consumption of the commercial sector. Unlike previous studies that had commercial consumers participate in the electricity market individually and sometimes fail to meet the requirements for flexibility programs, this study adopts a commercial aggregator to enhance the responsiveness of commercial systems. The proposed structure includes a mathematical model for commercial systems, e.g., shopping centers, with responsive ventilation systems to achieve demand flexibility. The study also uses the information-gap decision theory to address time-based commercial demand response planning from 24 h ahead to near real time. Moreover, a multi-layered structure is proposed to integrate the flexibility of shopping centers from the demand side to the supply side through a newly invented commercial demand response aggregator. The proposed approach was implemented in the New York electricity market, and the results show that it provides demand flexibility for up to 18% of the nominal level of electricity consumption compared to the traditional system. The paper aims to present a responsive structure for commercial systems, addressing the challenges of integrating renewable energies with the electricity system.

The Impact of Electricity Consumption to Human Development Index in Asian Countries: Analysis Panel Vector Error Correction Model

This study answers the question of the effect of the level of electricity consumption (LEC) on the level of human welfare both in the short and long term. Recently, many studies that investigated the impact of LEC to economic growth has been conducted. Most of them argued that electricity consumption have positive impact to economic growth. However, the study that analyzes LEC's impact on the Human Development Index (HDI) and Happiness Index (HI) still needs to be completed. The research data uses secondary data sourced from the worldbank from 2012-2019. The number of countries as sample of this study were 38 countries in Asia. We choose and analyze the countries that have complete data for all variables until 2019. The analysis tool used is the Panel Vector Error Correction Model (PVECM). We analyzed data using PVECM because we wanted to understand the impact of LEC on HDI and HI both in the short and long run. The results revealed the relation between LEC and human welfare indicators. Finally, we found in the long run, there are no variables that have a significant effect on electricity consumption. Meanwhile, based on the short-term equation, it was found that in model 1 with the LEC dependent variable, it was found that only the previous period's LEC variable was significant. In Model 2, with the HDI dependent variable, it is known that the previous period's HDI variable has a significant effect. These results are also similar to the HI and LAK variables. The HI and LAK variables are only influenced by HI and LAK variables in the previous time lag. The implication of this study is increasing of electricity consumption would not direct impact on human development index and happiness index.

Use of nested multi-regional input–output analysis for the evaluation of subnational emission reduction policies: A case of Japanese prefectures

Many subnational regions around the world, in their own capacity for policy formulation and implementation, have set targets for net-zero carbon dioxide or greenhouse gas emissions. However, most targets do not consider consumption-based emissions, which include emissions generated outside their borders to produce the products they consume. It is partly due to methodological difficulties and data gaps that subnational governments often face but this situation could misguide their efforts in the face of the goal of reducing global emissions. To facilitate an understanding of consumption-based subnational carbon emissions and informed subnational policymaking, this study proposes a relatively simple and less data-intensive methodology for subnational consumption-based emission analysis. It uses a nested multi-regional input–output approach by combining a publicly available global multi-regional input–output table and a subnational input–output table, whose computation is repeated for multiple regions for comparison. A case study of three Japanese prefectures with different levels of renewable electricity consumption was conducted using the 2015 datasets. The results showed that two prefectures with similar socioeconomic conditions, except for their totally different renewable electricity ratios (90 % and 4.6 %), exhibited a 25 % difference (4.4- and 6.0-ton CO2) in per capita household consumption-based carbon dioxide emissions in 2015. Counterfactual policy scenarios were analyzed using the constructed models. Application of an increased renewable electricity scenario indicated that an approximate 10 % increase in the prefectural renewable electricity ratio could reduce household per capita consumption-based emissions by 151–179 kg CO2 in those prefectures. Consumption-based emissions disaggregated by product category indicated that increasing the renewable electricity ratio had significant consumption-based emission reduction effects on not only electricity itself but also on services, particularly commerce, hotel, and restaurant services. The potential impacts of changing renewable electricity ratios on consumption-based emissions were found to be high for products whose share in household spending on local products and electricity cost in their production was large. A scenario analysis of home electrification, assuming the total replacement of household fuel gases and kerosene with electricity, resulted in a modest consumption-based emission reduction potential of less than 10 % for a prefecture with a high renewable electricity ratio and even showed a slight emission increase for a prefecture with a low renewable electricity ratio. In using commonly available datasets without the need to model trade flows between subnational regions, this study's method could realistically be applied by subnational governments to estimate consumption-based emissions of their jurisdiction, compared with other regions, and analyze the potential effects of emission reduction policy scenarios.