Energy Consumption Factors Research Articles

Decoupling Elasticity and Driving Factors of Energy Consumption and Economic Development in the Qinghai-Tibet Plateau

Decoupling of energy consumption and economic development is a key factor in achieving sustainable regional development. The decoupling relationship between energy consumption and economic development in the Qinghai-Tibet Plateau region is still unclear. This paper uses the logarithmic mean Divisia index (LMDI) decomposition method and Tapio elastic index model to analyze the decoupling degree and driving factors of energy consumption and economic development, and evaluates the decoupling effort level in Qinghai-Tibet Plateau from 2006 to 2016. The results indicate that the Qinghai-Tibet Plateau region showed a weak decoupling as a whole, and that only Tibet experienced expanding negative decoupling in 2006–2007 and an expansion link in 2007–2008. Economic scale is a primary factor that hinders the decoupling of energy consumption, followed by investment intensity and industrial energy structure. The cumulative promotion effect of research and development (R&D) efficiency and intensity and the inhibition effect of investment intensity cancel each other out. With the exception of Tibet and Xinjiang, all provinces in the Qinghai-Tibet plateau have made decoupling efforts. Decoupling efforts made by R&D efficiency contributed the most, followed by energy intensity and R&D intensity. This paper provides policy recommendations for the decoupling of energy consumption experience for underdeveloped regions.

Renewable Energy Consumption in Africa: Evidence From a Bias Corrected Dynamic Panel

Our study investigates the determinants of renewable energy consumption in Sub-Sahara Africa. We explore the driving factors of renewable energy consumption in the context of carbon intensity for 32 Sub-Saharan African countries from 1990 to 2015. Using carbon emission intensity to identify group-specific heterogeneity, we recognize carbon-efficient and least carbon-efficient countries in the region. By relying on the corrected least squares dummy variable estimator (LSDVC), we provide evidence on the driving factors of renewable energy consumption in Sub-Saharan Africa. Consequently, the findings point to varying degrees of impact on renewable energy consumption in the region. For instance, we observe advancement in technology, quality of governance, economic progress, biomass consumption, and climatic conditions influence renewable energy consumption. With a common occurrence across all groups, the implications indicate environmental, socio-economic, and climatic factors playing an important role in renewable energy consumption. The study further shows that urbanization and economic globalization depress efforts towards renewable energy consumption. Apart from these common factors, other controlling variables including; GDP per capita, environmental awareness, and biomass affect each group differently. We conclude that, policy implications can be drawn from common factors towards harmonization of clean energy markets and developing a policy mix that combines environmental, economic, and social factors in attaining the Sustainable Development Goals.

Population ageing and energy consumption for sustainable development

Population ageing has been acknowledged as a cause of the alteration in residential energy consumption pattern, which partly arises from the exit of the workforce from the labour market due to the retirement age limit, promoting a sedentary lifestyle. However, there is a lack of research exploring the shift in energy demand that specifically scrutinises the effect of population ageing on the consumption structure. In this context, the paper aims to investigate the relationship between residential electricity consumption and demographic indicators specific to labour force participation. This study uses a literature survey of household consumption structure, ageing labour force participation, and energy consumption factors, while the econometric approach is proposed to estimate the determinants. Based on the findings, this study supports the inclusion of the employment rate in expressing residential electricity consumption in the population ageing scenario. This paper ends with the development of a proposed model for future research work due to the economics of population ageing in energy.

Population ageing and energy consumption for sustainable development

Population ageing has been acknowledged as a cause of the alteration in residential energy consumption pattern, which partly arises from the exit of the workforce from the labour market due to the retirement age limit, promoting a sedentary lifestyle. However, there is a lack of research exploring the shift in energy demand that specifically scrutinises the effect of population ageing on the consumption structure. In this context, the paper aims to investigate the relationship between residential electricity consumption and demographic indicators specific to labour force participation. This study uses a literature survey of household consumption structure, ageing labour force participation, and energy consumption factors, while the econometric approach is proposed to estimate the determinants. Based on the findings, this study supports the inclusion of the employment rate in expressing residential electricity consumption in the population ageing scenario. This paper ends with the development of a proposed model for future research work due to the economics of population ageing in energy.

Optimization based feasibility analysis for Energy Conservation Building Code compliance of opaque wall assemblies in different climatic zones of India

Thermal performance of building envelopes is the determinant factor of building energy consumption. The total built up area of commercial buildings in India is expected to touch 1.9 Billion Sq. m. by 2030 from the 2015 figure of 847 Million Sq. m., a growth of three times. According to Central Electricity Authority of India the projected energy consumption load for 2030 is five times that of the current load in the region. Energy Conservation Building Code (ECBC) for commercial buildings was first launched in 2007 towards establishing new benchmarks for energy efficient buildings in India. There has been inadequate implementation of ECBC in the country and the success of these codes, policies and regulations depends on the market response to be self-sustaining in the long term. This research uses the evolutionary optimization model for the feasibility analysis of ECBC and incremental ECBC code compliance, in various climatic zones of India towards wider acceptability by the building industry with respect to the thermal transmittance of opaque wall assemblies in building envelope for different typologies of commercial buildings using the available materials in the Indian context / market.

Research on building energy efficiency based on energyplus software

Energyplus is an important software in the field of building energy efficiency, which can accurately simulate building energy consumption. By comparing simulated situation of building energy consumption with actual usage of energy, the energy consumption can be shown in some buildings so as to optimize the building energy efficiency, especially for promoting the thermal performance of buildings. This paper utilizes the building energy simulated software-EnergyPlus to simulate the energy consumption of the dormitory of Southeast University so as to verify the key factors of building energy consumption.

Electric Vehicle Charging Warning and Path Planning Method Based on Spark

Electric vehicles (EVs) are increasing popular as clean transportation. However electric vehicle (EV) users often encounter a shortage of electricity in the driving process. Especially when vehicle charging path planning system request reaches peak hour, it will increase the system calculation pressure. To promote penetration and popularity of electric vehicles, it is of critical importance to determine location and size of charging stations more scientifically and improve the computational power of path planning. In this paper, a method is developed for charge warning and path planning of insufficient energy EVs. The method utilizes the appropriate energy consumption factor to construct the electricity early warning model, analyzes the consumption of electricity in the vehicle in real time, and promptly warns the users when the vehicle has insufficient electric energy. Meanwhile, it combines the actual traffic information and map information to construct a network topology based on time weight, queuing mechanism, and charging calculation model to obtain path network model data, charging station queuing times and charging times as parameters. EV path planning problem is solved to minimize the total times of travel using the Dijkstra algorithm with the input path network model data, charging station queuing times and charging times as parameters. At the same time, the Spark computing framework is joined innovatively to improve the computing speed and solve the problem of path planning peak period. The Spark framework is utilized to parallelize the optimized Dijkstra algorithm. The experiment shows that the electric vehicle charging warning and path planning method provides an effective way for drivers of electric vehicles to charge.

Evaluating the impact of clean energy consumption and factor allocation on China’s air pollution: A spatial econometric approach

China’s air pollution has become a widespread concern in the academic world, but there are few studies based on spatial measurement methods to quantify the impact of clean energy consumption and factor allocation on a variety of pollutants. Based on an exploratory spatial data analysis, a spatial Durbin model and extended Cobb-Douglas production function (C-D production function) are used to study the direct, indirect and total effects of clean energy consumption and element allocation on China’s air pollution emissions. The exploratory spatial data analysis results showed that the three emissions had significant agglomeration effects, and the spatial aggregation patterns of the emissions were similar to the patterns of fossil energy consumption spatial aggregation. The spatial Durbin model estimation results showed that the clean energy consumption proportion and factor allocation of energy and labour inhibited air pollution emissions. The spatial spillover effect was greater than the direct effect. The fossil energy structure and factor allocation of energy and capital stock were positively related to air pollution emissions. These findings help to formulate regional industrial policies and energy policies and contribute to the governance of air pollution and the sustainable development of economic, environmental and energy resources.

Energy substitution effect on transport sector of Pakistan: A trans-log production function approach

Abstract Transport sector of Pakistan is one of the highest fossil fuel consumption and carbon dioxide (CO2) emission sector. As a key user of fossil fuel energy, Pakistan’s transport sector has created many problems i.e. noise, air pollution, CO2 emissions and environmental problem. In this study, trans-log production function model for Pakistan’s transport sector is established and input factors of labor, capital, and energy consumption are comprised. The output elasticity of an individual, input elasticity between a pair of factors, and elasticity of substitution between input factors are analyzed. To manage the multicollinearity issue, ridge regression is applied at different values of K. From 1991 to 2018, the outcomes show that output elasticities and a pair of input factor’s elasticities are increasing. The elasticities of substitutions between labor-energy consumption and capital-energy consumption show higher outcomes because of the more elevated amount of substitution elasticities. The substitution elasticity between labor and capital starts from 0.9797 to 0.9326 with slightly decreasing trend. The outcomes suggest that maximum output can be achieved by increasing capital and energy technologies. By allocating the maximum capital in the transport industry, energy-saving technologies, substitution between energy and capital, and substitution among capital-labor can enhance environmental protection. In the above’s view function, the present study estimates the level of energy-saving and CO2 emissions reduction under the different scenarios from 2015 to 2018. The policy suggestions of the results are given below.

Carbon footprint associated to urban water cycle of Córdoba (Spain)

Carbon footprint is a measure used to quantify greenhouse gases (GHG) emissions associated with organizations, events or activities or with the life cycle of a product to determine its contribution to climate change, being expressed in kg of CO2 equivalent. GHG emissions can be direct or indirect, derived last ones from energy consumption. In the case of the urban water cycle (which uses 3% of the total energy consumption in Spain), the emissions come mainly from the use of energy. Thus, to estimate this type of emissions it can be taken into account both, energy consumption and emission factors, these last ones which associate quantity of energy consumed and quantity of gases emitted. This work presents the result to calculate the carbon footprint related to urban water cycle of Córdoba in the last six years: 4,225.541 T of CO2 eq/year are emitted, at a rate of 12.92 kg of CO2 eq/year per inhabitant, and 0.176 kg of CO2 eq. per m3/water managed by the company. If we compare this result with the obtained in our last studied period (2012-14) which reached a value of 5,015.80 T of CO2 eq/year (-15%) and a rate of 15.2 kg of CO2 eq/year per inhabitant, we can conclude that the company has shown a relevant positive environmental balance.

Carbon footprint associated to urban water cycle of Córdoba (Spain)

Carbon footprint is a measure used to quantify greenhouse gases (GHG) emissions associated with organizations, events or activities or with the life cycle of a product to determine its contribution to climate change, being expressed in kg of CO2 equivalent. GHG emissions can be direct or indirect, derived last ones from energy consumption. In the case of the urban water cycle (which uses 3% of the total energy consumption in Spain), the emissions come mainly from the use of energy. Thus, to estimate this type of emissions it can be taken into account both, energy consumption and emission factors, these last ones which associate quantity of energy consumed and quantity of gases emitted. This work presents the result to calculate the carbon footprint related to urban water cycle of Córdoba in the last six years: 4,225.541 T of CO2 eq/year are emitted, at a rate of 12.92 kg of CO2 eq/year per inhabitant, and 0.176 kg of CO2 eq. per m3/water managed by the company. If we compare this result with the obtained in our last studied period (2012-14) which reached a value of 5,015.80 T of CO2 eq/year (-15%) and a rate of 15.2 kg of CO2 eq/year per inhabitant, we can conclude that the company has shown a relevant positive environmental balance.

Evaluation and optimization of electrocoagulation for treating Kraft paper mill wastewater

The performance of electrocoagulation (EC) using iron as sacrificial electrode was investigated at low current densities (0.6–1.9 mA/cm2) for treating a waste stream from a Kraft paper mill. A multi-level factorial design of experiments and response surface methodology (RSM) were applied to quantify the effects of current density and reaction time on the removal efficiency of color, COD, BOD5, tannin/lignin as well as on the enhancement of biodegradability index (BI). Increasing current density or reaction time significantly improve the treatment performance. An antagonistic effect of the two studied factors was identified, indicating that simultaneously increasing current density and reaction time does not yield added improvement in treatment performance. An RSM was developed to predict the optimum current density and reaction time within the experimental data range of this study. It was found that an optimized treatment combination exists at relatively low current density and reaction time. The optimum value for maximum pollutants removal was ∼1.5 mA/cm2 and ∼25 min. Under the optimum condition, the removals of tannin/lignin and color were >70 % and >95 %, and BI was increased to 0.41 BOD5/COD. Current density is the dominant factor for energy consumption and operating cost compared to reaction time and electrode consumption. This study demonstrated that EC can be operated at low current density to remove the bio-refractory organics effectively and increase the biodegradability index to the level suitable to biological treatment at low operating costs.

Intelligent models to predict the indoor thermal sensation and thermal demand in steady state based on occupants’ skin temperature

The correct prediction of thermal sensation is an important factor in energy consumption and satisfaction of occupants. This study examined the effectiveness of six different intelligent approaches for predicting thermal sensation and demand using body temperature data of 615 experiments with an exposure time of 3 h in a controlled office place. At each hour, the temperature of 14 uncovered body points was measured and finally, 1845 temperature data points were extracted. The exposure time had a significant effect on the thermal sensation and insufficient impact on the body temperature. Among all measured temperature data points, four points including middle of forehead (MFH), left cheek (LC), Nose (No), and left hand (LH), were taken as models' inputs. The results indicated that the Gaussian Process Regression (GPR) method offers the best outcomes in prediction of thermal sensation with mean absolute error (MAE) of 0.571 and R2 of 0.84 for the test data points. The MAE and R2 obtained by this model were 0.95 and 0.69, respectively, suggesting that GPR is more accurate and reliable than well-known method PMV. Regarding thermal demand, it was found that the accuracies of the GPR and PMV models were 86% and 69%, respectively. Therefore, the GPR approach is capable of predicting outstanding results for thermal demand compared to the existing models on the basis of environmental factors such as PMV Overall, the present study suggested that intelligent methods based on occupants’ physiological factors estimate the thermal sensation and demand better than available standard methods.

Beyond Petroleum - Whither Nigeria?

Oil and Gas (Otherwise known as Petroleum) provide a vital contribution to the Global Energy mix. Economic development and progression are known to be symmetrically linked with increasing hydrocarbon consumption but there is increasing evidence of global demand with attendant impact on alleged global warming and carbon footprint. This has led to a clamour for reduction in hydrocarbon exploration and production operations. The question therefore is: Is there a future for Petroleum? In this paper the question is answered with an emphatic YES. The paper goes further to present appropriate facts and data to support the claim as well as forward-looking statements, particularly those regarding Global/African economic growth, population and productivity growth, energy consumption, energy mix, policy support for gas utilisation and renewable energies, sources of energy supply and growth of hydrocarbon supply. There is increasing gap between global demand and supply especially in developing economies of China, India and in particular Nigeria where increasing economic growth/development, inward consumption by growing modern population, manufacturing industries and energy consumption and current low hydrocarbon recovery factor are identified as the catalysts for the demand outstripping supply. There is evidence that there will be a gap of almost 5million barrels per day between demand and supply in Nigeria by the year 2030 which will inevitably turn Nigeria into an oil importing country if there is no urgent intervention. There is scope/opportunity for aggressive exploration into and production from Deepwater environment and existing mature assets using game changing fit-for-purpose technologies and skilled manpower development supported by forward looking strategic framework enabler policies by Government working with industry the details of which are presented in the paper.

Does financial development and trade openness enhance industrial energy consumption? A sustainable developmental perspective

Purpose The purpose of this paper is investigate the dynamic linkages among industrial energy use, industrial value added, financial development (FD) and trade openness, in case of India. The study covers the annual frequency data on both aggregate and disaggregate variables for the period 1971–2016. Design/methodology/approach The autoregressive distributed lag bounds testing approach is applied to examine the long-run relation among variables under consideration. Also, Johansen and Juselius (1990) and vector error-correction mechanism results confirm the result of cointegration. Furthermore, non-linear relationship in the model is also tested. Findings It has been found that there exists long-run relationship among variables. Long-run estimates show that increasing FD leads to more energy uses. Hence, FD should be directed in such a way that it incentivises firms to invest in energy-efficient technology. Furthermore, it is also found that study supports the evidence of conservative hypothesis, which supports that the energy conservation policy should be adopted in the industrial sector. Energy efficiency programme needs to be designed very carefully to achieve a higher level of energy efficiency. This leads to a sustainable growth and low carbon emission. Originality/value This paper examines the recent trend in Indian industrial energy consumption and does a comprehensive analysis using a robust econometric method. We have developed a lucid model to examine the deriving factors of industrial energy consumption.

Perspectives on environmental CO2 emission and energy factor in Cement Industry

The global challenges of pollution emission in cement industry in relation to energy consumption factor are overwhelming. No doubt it has been established that economic development has an impact on the environmental pollution and ecological system. With major constituent of environmental affluence been CO2, the consideration has to be on the type of manufacturing process, the consumption mix and the additive ratio. This paper focuses on the cement industry and aims to provide a systematic review of the specific operations, its trend and its impact on environmental pollution in terms of energy consumption and emissions evolved.

INVESTIGATING THE EFFECT OF DIGITAL TECHNOLOGIES, ENERGY CONSUMPTION AND CLIMATE CHANGE ON CUSTOMER’S EXPERIENCE: A STUDY FROM INDONESIA

The objective of this study is to investigate the effect of digital transformation, energy consumption and Climate change on customer experience in the region of Indonesia. Research design is based on primary data collection through structural questionnaire. For customer experience, two factors under the title of hedonic and recognition are finalized. Digital transformation is observed through smart technologies. A sample of 282 respondents from Indonesian economy is selected and confirmatory factor analysis CFA is applied. All items of selected variables reflect reasonable factor loading, providing a good way for the regression analysis. Findings through regression indicates that awareness of smart technologies is increasing customer's experience in the form of entertaining, sense of comfort and educational. Learning of smart technologies has a significant influence on customer experience through entertaining and educational. Renewable energy consumption factor (usage of gas is friendly) has a significant and direct influence on all items of hedonic except sense of comfort. For recognition, all items are directly and significantly affected by the GFRIENDLY except sense of beauty. This study has significant covered the literature gap from the context of digital technologies, consumption of energy from new sources and climate change for better customer experience. Managerial implication of the study provides an evidence for the relevant experts in their field.Keywords: Digital transformation, energy consumption, climate change, factor analysis.JEL Classifications: N70, Q54DOI: https://doi.org/10.32479/ijeep.8280

Energy-efficient flexible flow shop scheduling with worker flexibility

The classical flexible flow shop scheduling problem (FFSP) only considers machine flexibility. Thus far, the relevant literature has not studied FFSPs with worker flexibility, which is widely seen in practical manufacturing systems. Worker flexibility may greatly affect production efficiency and productivity. Furthermore, with the increase of environmental pollution and energy consumption, manufacturers require innovative methods to improve energy efficiency. In this paper, we propose an energy-efficient FFSP with worker flexibility (EFFSPW), in which the flexibility of machines and workers as well as the processing time, energy consumption and worker cost related factors are considered simultaneously. A hybrid evolutionary algorithm (HEA) is then presented to solve the proposed EFFSPW, where some effective operators and a new variable neighborhood search approach are designed. Comprehensive experiments including 54 benchmark instances of the EFFSPW are carried out, and Taguchi analysis is used to determine the best combination of key parameters for the HEA. Experimental results show that the proposed HEA can obtain better solutions for most of these benchmark instances compared to two other well-known algorithms, demonstrating its superior performance in terms of both solution quality and computational efficiency.

Validation of Genetic Markers Associated to Oxygen Availability in Low-Grade Copper Bioleaching Systems: An Industrial Application.

Forced aeration is one of the major energy consumption factors of the bioleaching process of run-of-mine ore. The effect of aeration in the microbial community has scarcely been studied at industrial level. Leptospirillum ferriphilum is one of the most representative species of the Fe3+ producing population in this kind of systems. We analyzed the effect of oxygen availability on L. ferriphilum by growth activity and transcriptional dynamics of its two terminal oxidases (cbb3 and bd complexes) under different experimental test: culture reactor, bioleaching column, and industrial heap tests. Relatively low O2 availability triggered important changes in the microbial community composition, cell growth, microbial activity and cydAB genes transcription in all cases of study. We assessed the potential role of the terminal oxidases on the adaptation to variable aeration conditions in different lifestyles of L. ferriphilum and identified transcriptional markers associated to oxygen metabolism in an industrial system. An interesting hypothesis about the possible role of the cbb3 complex in the response to oxidative stress as well as their role as a high oxygen-affinity oxidase in L. ferriphilum is proposed and discussed. This study successfully proves the function of the cydAB genes as valid genetic markers for low-grade copper industrial bioleaching systems.

Combined nonlinear effects of economic growth and urbanization on [formula omitted] emissions in China: Evidence from a panel data partially linear additive model

Economic growth and urbanization have been adequately studied due to their profound influence on pollution. However, the collective development of economy and urbanization poses a challenging problem in terms of reducing CO2 emissions. One of the prerequisites for solving this problem is to examine the simultaneous impacts of economic growth and urbanization on CO2 emissions. Within an extended STIRPAT framework, the present study aims to thoroughly investigate the combined nonlinear effects of economic growth and urbanization on CO2 emissions using provincial panel data from China that spans the period of 1997–2016. Considering the heterogeneity and dynamicity across the panel, this is the first attempt to comprehensively explore the selected variables and the CO2 emissions nexus by building a new two-way fixed-effects panel data partially linear additive model. The results show an inverted “U-shaped” nonlinear impact of economic growth on CO2 emissions, but urbanization has a different influence, revealing a “roller coaster” pattern with three turning points. The results also suggest that the factor of energy consumption will lead to an increase in CO2 emissions, while technology diffusion and industrial upgrading can facilitate the reduction of CO2 emissions. To provide an in-depth understanding of these impacts, nonlinear marginal analyses are performed at different stages of socioeconomic advancement. In addition, the consistency of the empirical results and the merits of the suggested method are corroborated by the different estimation techniques. The conclusion recommends that the evaluation of carbon pollution needs to be heightened by further research from multidimensional perspectives, and the concepts of green production and low-carbon development should be promoted to achieve the sustainable goals of the whole society.