Per-capita Electricity Consumption Research Articles

Urban 3D building morphology and energy consumption: empirical evidence from 53 cities in China

The impact of building morphology on building energy consumption has been extensively studied. However, research on how 3D building morphology affects energy consumption at a macroscopic scale is lacking. In this study, we measured the mean building height (BH), mean building volume (BV), and mean European nearest neighbor distance (MENN) of the city to quantify the 3D building morphology. We then used a spatial regression model to analyze the quantitative impact of urban 3D building morphology on per capita electricity consumption (PCEC). Results indicate that at the macroscopic scale of the city, the BH and the MENN have a significant positive impact on the PCEC, while the BV has a significant negative impact on the PCEC. Moreover, the inclusion of the 3D building morphology greatly improves the model’s ability to explain building energy efficiency, surpassing the impact of traditional economic factors. Considering the 3D building morphology indicators together, buildings with a lower height, a larger volume, and a more compact 3D morphology have greater potential for energy savings and are more conducive to electricity conservation. This study offers valuable insights for the energy-efficient arrangement of buildings.

A Calculation Model of Carbon Emissions Based on Multi-Scenario Simulation Analysis of Electricity Consumption

In order to reach the peak of carbon emission in China by 2030 and to meet the low-carbon conversion of energy and the growing demand for electricity, this study aims to propose a more accurate and scientific method to calculate the carbon emissions of the entire power industry chain. This paper analyzes the historical actual operation data of the energy and power industry from 2000 to 2020, and originally proposes a carbon emission calculation model based on a multi-scenario simulation analysis of electricity consumption. This paper is an original study from the perspective of the whole industry chain of electricity production, transmission, and consumption. Firstly, a carbon emission model of the power system is established based on the carbon emission composition and transmission mechanism of the whole power industrial chain, which consists of calculation models for carbon emissions from overall electricity demand and carbon emissions from electricity network losses. Secondly, the concept of carbon emission coefficient is proposed, and the key parameters of the carbon emission coefficient of the power system are obtained through the econometric model. On this basis, the carbon emission coefficient is obtained by regression fitting of multiple key parameters according to historical data. Finally, electricity consumption per unit output value (ECPUOV) and per capita electricity consumption (PCEC) are used to predict electricity consumption in the next 15 years. This paper also makes a quantitative analysis of the relationship between CO2 emissions from the power system and electricity consumption. This paper takes G province, which ranks first in total energy consumption and economic aggregate in China, as an example and calculates its CO2 emissions and achievement of peak CO2 emissions by multi-scenario analysis. The case study results show that the low carbon scenario(LC) is the best route for G province to peak CO2 emissions from energy consumption. The method proposed in this paper can set an achievable goal of 2030 carbon peaking for the government and industry policymakers, and find a feasible implementation path.

Economic Growth and Electricity Consumption in India: An Econometric Analysis

The purpose of this study is to analyse the long-run causal relationship among the per capita electricity consumption ( PCEC) and per capita gross domestic product ( PCGDP), urban population ( UP) and employment ( EMP) pattern in India over the period of 1991–2018. To analyse the long-run association Johansen co-integration test has been used. The results of the Granger-causality test imply that there exists a bidirectional causal relationship between the PCEC and PCGDP whereas there exist unidirectional causality from EMP and UP to PCGDP. Jel Codes: L52, C53, P1

An evaluation on energy self–sufficiency model of a rural cluster through utilization of biomass residue resources: A case study in India

Lack of electricity access is a major deterrent to economic growth in rural areas of developing and underdeveloped countries. Until last decade, the government's focus for enhancing electricity access was on augmenting fossil fuels and increasing centralized grid infrastructure leading to environmental and economic concerns. This paper, through a case study (BB Nagar block, Uttar Pradesh, India) investigates biomass resources in a rural area for decentralized power generation to reduce the dependence on imported fossil fuels by making them self-sufficient for energy needs and mitigating greenhouse gas (GHG) emissions. The study outlines a methodology to evaluate regional biomass availability and its power generation potential and assess the associated uncertainties by considering different scenarios. It further estimates the GHG emissions from biomass power and compares them with biomass-equivalent coal-based power. Results for different scenarios show that biomass power potential in the block varies from 6783.7 to 21,852.5 kW, with a baseline potential of 10,637 kW.Similarly, GHG emissions saving potential by using biomass for power instead of coal varies from 33,267.8 to 106,459.5 t CO2eq.y−1 with a baseline saving of 51,956.4 t CO2eq.−1 (i.e., ~74% emission savings vis-à-vis coal). The biomass-based per-capita electricity potential for the block is estimated to be 655.1 kWh/capita, which is much higher than the current per-capita electricity consumption indicating that the block can be made self-sufficient using surplus biomass. The paper also highlights the obstacles that exist in the introduction of biomass as a replacement of fossil fuels and suggests recommendations for its greater implementation as a resource for power generation. The findings can help boost decentralized planning for energy self-sufficiency in rural areas by mapping bioenergy and emissions profiles covering diverse feedstocks.

Energy Transition toward Cleaner Energy Resources in Nepal

Energy is an important input for socioeconomic development and human well-being. The rationality of energy transitions toward cleaner energy resources is not only to improve individual living conditions, but also to enhance the economic growth of a nation. Nepal is considered to be one of the countries with a low per-capita electricity use, heavily relying on traditional energy resources such as firewood and agricultural residues. The country is rich in hydropower resources. However, various economic and socioeconomic constraints have left the significant potential for hydroelectricity untapped. This study describes the energy transition patterns in Nepal based on a literature review and field survey of household energy use in the winter. We collected data from 516 households in the Solukhumbu, Panchthar, and Jhapa districts of Nepal. The rate of per-capita electricity consumption was 330 kWh/capita/year, which is significantly lower than that of other contemporary global societies such as India 1000 and China 4900 kWh/capita/year. The increasing trend in hydroelectricity production has optimistically transformed the energy sector toward cleaner resources; this correlates with the GDP per capita. Solar home systems, mini- and micro-hydropower plants, biogas technology, and improved cook stoves have been widely used, which has lowered the health and environmental burdens in rural areas. By analysing the survey data, we found that 25% of the households only relied on traditional cooking fuel, while 67% and 8% of the households relied on mixed and commercial cooking fuels, respectively. Moreover, 77% and 48% of traditional and mixed-fuel-using households were unhappy with current cooking fuels while 40% and 66% of these households preferred to use clean cooking fuels. The share of traditional energy resources decreased from 78% to 68%, while that of commercial energy resources increased from 20% to 28% from 2014/15 to 2019/20. This study suggests that future energy policies and programs should acknowledge the reality of energy transition to achieve sustainability by establishing reliable and clean sources of energy.

Assessing the Disparities of Per-capita Electricity Consumption in North-Eastern States of India Using Gini Index and Lorenz Curve

Electricity is the basic need for individuals, households and industries. It is used for everyday life activities by the households, agricultural activities, commercial buildings and industries. The per capita electricity consumption is a proxy indicator growth and development status of a region. All the north eastern states consume per-capita electricity lesser than the national average except Sikkim. The per capita consumption of electricity by Nagaland, Assam and Manipur is nearly just one-third of the national average. Notably, these three north eastern states consume just 40 percent of per capita electricity compared to the highest consumer state Meghalaya. The present study is an evaluation of the per-capita electricity consumption trend and inequality amongst the north-eastern states of India using Gini coefficient and Lorenz curve for two different time periods i.e. 2007 and 2017. We find that Tripura’s annual linear growth rate of per-capita electricity consumption is the highest (3 percent) while that of Meghalaya is the least (1 percent) and the other five states are growing equally at the rate of 2 percent. The Gini coefficient is found to be decreasing from 0.137 in 2007 to 0.122 in 2017. We conclude that the level of inequality is reducing among north eastern states of India though the per-capita consumption of electricity is less than the national average.

Dynamic nonlinear influence of urbanization on China’s electricity consumption: Evidence from dynamic economic growth threshold effect

On the basis of considering the inertia characteristic of electricity consumption (EC), the nonlinear dynamic threshold effect of urbanization on EC was studied by using dynamic threshold model. The mediating effect of economic growth on the influence of urbanization on EC was studied under heterogeneous samples. The spatial-temporal heterogeneity of the influence of urbanization on EC was investigated. The main conclusions are as follows: Firstly, when the threshold variable value crosses the threshold, the promoting effect of population urbanization rate (PUR) on per capita electricity consumption (PEC) and per capita industrial electricity consumption (PIEC) will be significantly weakened, but the promoting effect of PUR on per capita household electricity consumption (PHEC) will be significantly enhanced. Secondly, the positive mediating effect of economic growth on the influence of urbanization on EC is significant. The mediating effect on regions that cross the threshold is also greater than those that do not. Thirdly, the impact of PUR on PEC or PIEC in the eastern region is weaker than that in the central and western regions, while the impact of PUR on PHEC in the eastern region is stronger than that in the central and western regions.

Electricity consumption and income nexus: evidence from Bangladesh

PurposeThe purpose of this study is to examine the causal relationship between per capita electricity consumption (PCEC) and per capita gross national income (GNI) (PCGNI) in Bangladesh for the period of 1971-2014.Design/methodology/approachVector error correction modeling approach.FindingsThe study reveals that positive short-run unidirectional causal flow running between PCEC to PCGNI without feedback which implies that an increase in electricity consumption promptly affects economic activity. The results of both long-run and joint causality yield strong evidence of bidirectional causal relationship between PCEC and per capita real GNI with feedback.Originality/valueTherefore, both electricity generation and conservation policy will be effective for Bangladesh economy. So, smooth supply of electricity is necessary to meet the growing electricity demand for consumption; consequently, it will lead to sustain of growth and take it even higher level.

Long-term electricity consumption forecasting based on expert prediction and fuzzy Bayesian theory

Long-term electricity consumption (EC) forecasting is a very important part for the expansion planning of power system. Instead of point forecasting, based on fuzzy Bayesian theory and expert prediction, a novel long-term probability forecasting model is proposed to predict the Chinese per-capita electricity consumption (PEC) and its variation interval over the period 2010–2030. The special model structure can improve the reliability and accuracy of expert prediction through econometric methodology. It contains three components: fuzzy relation matrix, prior prediction, and fuzzy Bayesian formula. To contend with the long-term uncertainty, the prior prediction is implemented to combine the advantages of expert's experience with other time-based methods from the perspective of probability. With the utilization of fuzzy technique, the multiple effects of influencing factors (IFs) on PEC can be expressed as a fuzzy relation matrix. It can rule the results of prior prediction to obey the long-run equilibrium relationship of natural evolution thorough probability calibration. To demonstrate its efficiency and applicability, the result of this method is compared with that of other 6 approaches and 4 agencies. The case study shows that the proposed methodology has higher accuracy and adaptability.

Linking residential electricity consumption and outdoor climate in a tropical city

The link between climate and energy consumption has been widely established in many cities. The residential electricity consumption majorly for space conditioning is especially responsive to the outdoor climate. As a hot and humid tropical city, Singapore is expected to see increasing residential electricity consumption with global climate change. In this study, a multiple regression analysis is performed to relate the residential electricity consumption per capita and per household with outdoor climate during a 12-year period (2005–2016). The per-capita electricity consumption during 2008–2016 sees substantial decrease from that during 2005–2007, possibly due to the various energy saving measures taken by Singapore government. The different responses to outdoor climate variation in different household types are examined. It is found that generally dwellers in public households are more sensitive to outdoor climate variations, while private household occupants are not much affected by climate. Due to increasing ownership of electricity-consuming appliances in 1–2 Room and 3 Room public households, their electricity consumptions show a consistent increasing trend during the period studied. Under the worst scenario of projected future temperature increase, the per-capita and per-household electricity consumptions are projected to increase by 47% and 41%, respectively.

AID AND GROWTH IN BANGLADESH: A REASSESSMENT

Although the amount of foreign aid received by Bangladesh as a share of GDP has declined over the years, Bangladesh remains one of the heavily aiddependent countries in Asia. The results of most empirical studies that have examined the effectiveness of foreign aid or other forms of development assistance for economic growth have varied considerably depending on the econometric methodology used and the period of study. As the debate and controversy over aid-effectiveness for economic growth continue to grow, this paper reinvestigates the short-run and long-run effects of foreign aid received on percapita real income of Bangladesh over the period 1972–2015. A vector error correction model is estimated. The results indicate lack of any significant short-run and long-run relation between foreign aid and per-capita real income. Results further indicate short-run unidirectional causalities from per-capita real GDP to domestic investment (in proportion to GDP), from government expenditure (in proportion to GDP) to inflation rate, from inflation rate to domestic investment (in proportion to GDP), and from domestic investment to foreign aid (as percentages of GDP). Short-run bidirectional causality is observed between per-capita electricity consumption and per-capita real GDP, and between per-capita real GDP and government expenditure (in proportion to GDP).

Is California More Energy Efficient than the Rest of the Nation? Evidence from Commercial Real Estate

California’s per-capita electricity consumption is 50 percent lower than national per-capita consumption. Mild climate, deindustrialization, and its demographics explain part of this differential. California energy efficiency policy is often claimed to be another key factor. A challenge in judging this claim is the heterogeneity of the real estate capital stock. Residential homes differ along a large number of physical attributes. We access a proprietary dataset from a large hotel chain that allows us to evaluate the environmental performance of comparable commercial real estate across the United States. Controlling for climate conditions and geographic location, we document that California’s commercial real estate stock is the most energy efficient at a point in time but this differential is quantitatively small. However, over the years 2007 to 2013, California’s hotels achieved much greater energy efficiency progress than hotels in other states.

A Causal Study Between Electricity Consumption and CO<SUB>2</SUB> Emissions in India

This study examines the causal relationships between per-capita electricity consumption and per capita Co 2 emissions for India covering the period 1971 to 2006 by using the cointegration and error correction models. Our study reveals that there exists co integration between the per capita Co 2 emissions and per-capita electricity consumption. Both the time series per capita Co 2 emissions and Electricity consumption are non stationary in level form, but are individually integrated of order one. Granger's causality test provided enough evidence of longer-term equilibrium relation between the two variables, which will attract the attention of policymakers in drawing out efficient policy making to drive the economy with less pollutant emissions. Empirical results have shown that up to lag 7, there exists a bilateral causality between the two variables that shows how these two variables are associated and there is a need for a closer look at the consumption of fossil fuels in generating and consuming the electricity in association with the environment. Further, VAR technique is used for forecasting the per capita Co2 emissions; the forecast for the future years are accurate with very mild forecast error.

A Fuzzy Multi Criteria Decision Making to Evaluate Energy Policy Based on an Information Axiom: A Case Study in Turkey

Turkey has been one of the fastest-growing power markets in the world with its young and growing population, rapid urbanization, strong economic growth and low per-capita electricity consumption for two decades. The recent figures indicate that electric power demand and supply have shown rapid increases over the last few years and is expected to continue in the future. Therefore, selecting the appropriate energy policy is very important for Turkey. In this article, energy policies for Turkey are evaluated using multi criteria axiomatic design including information axiom and fuzzy information axiom due to having subjective and objective criteria in the evaluation of the energy policy problem. Fossil fuels, wind power, nuclear power, natural gas, solar power and hydropower are evaluated in the long term (2008–2018) for Turkey. In the evaluation process, economic, environmental, technical, and social benefits are mainly taken into account as the main criteria.

Electricity consumption and economic growth: A cross-country analysis

Electricity has been the foundation of economic growth, and constitutes one of the vital infra-structural inputs in socio-economic development. The world faces a surge in demand for electricity that is driven by such powerful forces as population growth, extensive urbanization, industrialization, and the rise in the standard of living. This paper attempts to ascertain whether there is a systematic relationship between electricity consumption and economic growth. To this end, we use a large set of data that spans 88 countries during the period, 1975–2004. A statistically significant inverted-U-shaped relationship between per-capita consumption of electricity and per-capita income is detected. Nevertheless, by using a purchasing power parity that is much higher than the per-capita income of all the countries in the world, the level of per-capita income is estimated at the peak point of per-capita electricity consumption to be $61,379 in 2000 constant international dollars. Moreover, we segment the sample into Organization for Economic Cooperation and Development (OECD) countries and non-OECD countries, and separately analyze the developed and developing countries. The separate estimation shows that even though the peak income is higher than the average per-capita income, a statistically significant inverted-U-shaped relationship is found in OECD and developed countries but not in non-OECD and developing countries.

Partition of regional types on rural household energy consumption of province-level regions in China

In order to classify regional types of rural household energy consumption (RHEC) in China, this paper selects the following as the characteristic indices for regional classification: per-capita available heat consumption for cooking and heating per day (PCAHC), annual per-capita electricity consumption for livelihood (PCEC), ratio of commercial energy in PCAHC (RCE), per-capita energy consumption of coal equivalent (PCECOCE), per-capita commercial energy consumption (PCCEC), per-capita income (PCI) and average annual temperature (AAT), etc. By using cluster analysis, this thesis divides the surveyed regions into five different types, according to the similarities of their characteristic indices made out through analysing national statistical data of rural household energy consumption; it also describes the main characteristics of each region, which provides a reference for rural energy policy-making and research on regional differences.