Drivers Of Energy Use Research Articles

Refinement of the "Candidatus Accumulibacter" genus based on metagenomic analysis of biological nutrient removal (BNR) pilot-scale plants operated with reduced aeration.

Members of the "Candidatus Accumulibacter" genus are widely studied as key polyphosphate-accumulating organisms (PAOs) in biological nutrient removal (BNR) facilities performing enhanced biological phosphorus removal (EBPR). This diverse lineage includes 18 "Ca. Accumulibacter" species, which have been proposed based on the phylogenetic divergence of the polyphosphate kinase 1 (ppk1) gene and genome-scale comparisons of metagenome-assembled genomes (MAGs). Phylogenetic classification based on the 16S rRNA genetic marker has been difficult to attain because most "Ca. Accumulibacter" MAGs are incomplete and often do not include the rRNA operon. Here, we investigate the "Ca. Accumulibacter" diversity in pilot-scale treatment trains performing BNR under low dissolved oxygen (DO) conditions using genome-resolved metagenomics. Using long-read sequencing, we recovered medium- and high-quality MAGs for 5 of the 18 "Ca. Accumulibacter" species, all with rRNA operons assembled, which allowed a reassessment of the 16S rRNA-based phylogeny of this genus and an analysis of phylogeny based on the 23S rRNA gene. In addition, we recovered a cluster of MAGs that based on 16S rRNA, 23S rRNA, ppk1, and genome-scale phylogenetic analyses do not belong to any of the currently recognized "Ca. Accumulibacter" species for which we propose the new species designation "Ca. Accumulibacter jenkinsii" sp. nov. Relative abundance evaluations of the genus across all pilot plant operations revealed that regardless of the operational mode, "Ca. A. necessarius" and "Ca. A. propinquus" accounted for more than 40% of the "Ca. Accumulibacter" community, whereas the newly proposed "Ca. A. jenkinsii" accounted for about 5% of the "Ca. Accumulibacter" community.IMPORTANCEOne of the main drivers of energy use and operational costs in activated sludge processes is the amount of oxygen provided to enable biological phosphorus and nitrogen removal. Wastewater treatment facilities are increasingly considering reduced aeration to decrease energy consumption, and whereas successful BNR has been demonstrated in systems with minimal aeration, an adequate understanding of the microbial communities that facilitate nutrient removal under these conditions is still lacking. In this study, we used genome-resolved metagenomics to evaluate the diversity of the "Candidatus Accumulibacter" genus in pilot-scale plants operating with minimal aeration. We identified the "Ca. Accumulibacter" species enriched under these conditions, including one novel species for which we propose "Ca. Accumulibacter jenkinsii" sp. nov. as its designation. Furthermore, the MAGs obtained for five additional "Ca. Accumulibacter" species further refine the phylogeny of the "Ca. Accumulibacter" genus and provide new insight into its diversity within unconventional biological nutrient removal systems.

Energy use and energy-related carbon dioxide emissions drivers in Egypt's economy: Focus on the agricultural sector with a structural decomposition analysis

Investigating drivers of energy use and its associated CO2 emissions changes is important for sustainable development. Using input-output structural decomposition analysis, this paper decomposed the energy use and resulting CO2 emissions drivers of changes in Egypt's wide economy, particularly in its agricultural sector, between 1972 and 2014. Fuel sources dominating energy use and energy-related CO2 emissions in the agriculture sector were explicitly identified. Affluence and demographic effects were the dominant drivers at both investigation levels. Household and government final consumption changes and exports expansion were important determinants behind changes in energy use and resulting CO2 emissions at the aggregate level from the final demand destination perspective. In the agricultural sector, petroleum and oil were the dominant fuel types. The technological effect was the main driver restraining energy use growth at the aggregate level. However, technological improvements have not been sufficient to make up for the evolving economy and consumption levels. Conversely, technological changes adversely impacted energy-related CO2 emissions at the economy-wide level, and both considered environmental indicators in the agricultural sector. Overall, upgrading technology and improving the final demand structure will be critical for Egypt to promote decoupling in the future.

Comment on Bettignies et al. The Scale-Dependent Behaviour of Cities: A Cross-Cities Multiscale Driver Analysis of Urban Energy Use. Sustainability 2019, 11, 3246

Bettignies et al. examine power-law relationships between drivers of energy use and urban features at city and infra-city levels for ten different cities in six countries across four continents, featuring a wide distribution of urban indicators from various data sources. The authors employ univariate linear regression models using selected log-transformed indicators to investigate whether the intensity of energy use scales with urban indicators such as population size, density, and income. Bettignies et al. suggest that based on their findings, the urban energy-use drivers are in fact scale-dependent, and that their results reveal a substantial heterogeneity across and within cities. They reference this as why more consideration needs to be paid to local factors when devising urban policy. On this note, we argue that Bettignies et al. appear to have not only misunderstood the urban scaling literature they have cited, but have also employed flawed methodological design in their analysis that ultimately leaves their conclusions unsubstantiated.

District-level analysis for household-related energy consumption and greenhouse gas emissions: A case study in Lima, Peru

• Household energy use and GHG emissions in the city of Lima were estimated. • Per capita emissions linked to energy use are 305 kg CO 2 eq in 2015. • Most GHG contributions linked to the use of natural gas (direct use and electricity). • Main drivers of energy use and emissions are population growth and affluence. Given the significant contribution of households in terms of energy use and greenhouse gas (GHG) emissions, in addition to the increasing rate of urbanization and growth in developing countries, urban dwellings need to be analyzed in greater depth in order to implement sound policy-making based on informed decisions. Thus, the aim of this study is to quantify the household-related energy use and associated GHG emissions in the city of Lima, Peru, between 2007 and 2015. In order to understand the emission patterns within the city, a further analysis on smaller spatial scales (i.e., districts) has been performed using multiple regression analyses. Results show that household-related energy consumption and emissions, excluding those related to private vehicles, ranged from 2.92 to 3.22 GJ/cap/year and 187 to 249 kg CO 2 eq/cap/year, respectively. Overall, there is a transition in the use of energy and emissions, sustained by an increase in population and a change in the economic status of part of the population. Moreover, liquefied petroleum gas, rather than electricity, was found to be the main energy source in lower-income households. This study aims to help integrate household energy behavior with strategies for sustainable cities and provide a starting point to connect with climate policies.

Investigating the effects of key drivers on energy consumption of nonresidential buildings: A data-driven approach integrating regularization and quantile regression

Investigation of the key drivers of building energy consumption at the city scale could assist in informing energy efficiency policies. This study proposed a data-driven approach by integrating regularization and quantile regression methods. This approach not only identified the key drivers of energy use by group lasso but also moved beyond the typical model of estimating average effects by quantile regression, quantifying the effects of the key drivers under low, medium and high consumption levels. The effectiveness of the proposed approach was evaluated based on a dataset of more than 2000 hospital and education buildings in Chongqing, China. The key drivers of these two types of buildings were found to be mainly related to building characteristics, including gross floor area, number of occupancies and cooling type. Additionally, the quantile regression results showed that most of the key drivers imposed clear heterogeneous effects on energy consumption throughout the quantiles. Compared with the other variables, gross floor area exerts the greatest positive effect on energy use across all the quantiles. The proposed approach can provide a general solution to understanding the effects of key drivers on energy consumption, and the empirical findings could benefit policy design for energy conservation.

Assessment of Energy Infrastructure in India: Is it Sustainable …….

Energy is one of the important building blocks in human development and act as a key factor in determining the economic development of any country. To meeting the demands of a developing nation, Energy requirement in the Form of Coal, Gas, oil and most important Electricity is necessary. This paper attempts to present full picture of Indian energy sector which is growing rapidly. However since, resource allocation and growth in energy supply have failed to meet the demands exerted by the increasing population, rapid urbanization and growing economy. First we identify factor of energy shortage in India, forcing it to rely heavily on imports. Second we develop a multiple linear regression model which includes all independent variables (Population, Inflation, and GDP) to determine energy consumption in India. India is a developing economy. Energy requirement in India are basically electricity, oil, coal, biomass and gas. India’s energy-mix comprises both non-renewable (coal, lignite, petroleum and natural gas) and renewable energy sources (wind, solar, small hydro, biomass, cogeneration biogases etc.). Based on these model we give conclusion such that which independent variable (population, Inflation and GDP) is more impacting coal, oil, gas and electricity consumption in India as well as Current patterns of energy use and assumptions about future trends in economic activity, we constructed an activity driven model to forecast what would be the natural short term evolution of energy use in India for each end use segment by. Through analysis of current patterns of energy use, drivers of energy use were collected at the sub-sector level. We connected drivers of model with evolution of GDP to determine what would be the impact of economic growth on drivers of energy use. It also includes fuel or technology switching in the forecast.

Estimating the economy-wide rebound effect using empirically identified structural vector autoregressions

The size of the economy-wide rebound effect is crucial for estimating the contribution that energy efficiency improvements can make to reducing greenhouse gas emissions and for understanding the drivers of energy use. Existing estimates, which vary widely, are based on computable general equilibrium models or partial equilibrium econometric estimates. Using a structural vector autoregressive (SVAR) model, we identify the dynamic causal impact of structural shocks, including an energy efficiency shock. The identification method is based on independent component analysis. In this manner, we are able to estimate the rebound effect with a minimum of a priori assumptions. We apply the SVAR to U.S. monthly and quarterly data, finding that after four years rebound is around 100%, which implies that in the long run no energy is saved.

How large is the economy-wide rebound effect?

The size of the economy-wide rebound effect is crucial for estimating the contribution that energy efficiency improvements can make to reducing greenhouse gas emissions and for understanding the drivers of energy use. Economy-wide rebound from an energy efficiency improvement includes changes in the use of energy to produce complementary and substitute goods or inputs and other flow-on effects that affect energy use across the economy as well as the direct rebound due to energy users using more of an energy service that has become less costly as result of improved energy efficiency. Jevons first argued in 1865 that improvements in energy efficiency increase total energy use, and in recent decades researchers have argued for and against this “backfire” hypothesis. Theory provides some guidance on the factors affecting rebound but does not impose much constraint on the range of possible responses. Historical evidence suggests that the improved energy efficiency of recent technology has not reduced energy use because consumption has shifted to more energy-intensive goods and services. Simulations and econometric research have produced mixed results. Some recent general equilibrium studies find large rebound, around 100%, but more research is needed to confirm or refute these findings.

How Large is the Economy-Wide Rebound Effect?

The size of the economy-wide rebound effect is crucial for estimating the contribution that energy efficiency improvements can make to reducing greenhouse gas emissions and for understanding the drivers of energy use. Economy-wide rebound from an energy efficiency improvement includes changes in the use of energy to produce complementary and substitute goods or inputs and other flow-on effects that affect energy use across the economy as well as the direct rebound due to energy users using more of an energy service that has become less costly as result of improved energy efficiency. Jevons first argued in 1865 that improvements in energy efficiency increase total energy use, and in recent decades researchers have argued for and against this “backfire” hypothesis. Theory provides some guidance on the factors affecting rebound but does not impose much constraint on the range of possible responses. Historical evidence suggests that the improved energy efficiency of recent technology has not reduced energy use because consumption has shifted to more energy-intensive goods and services. Simulations and econometric research have produced mixed results. Some recent general equilibrium studies find large rebound, around 100%, but more research is needed to confirm or refute these findings.

Comparative analysis of drivers of energy consumption in China, the USA and India – A perspective from stratified heterogeneity

With the limited amount of resources, developing effective strategies to make full use of them and decrease the energy consumption without too much sacrifice of economic output requires identifying key drivers of energy consumption growth rate as a prerequisite. Meanwhile, as top three consumers of primary energy of the world, China, the United States of America, and India burn over 45% of global fuels in 2016. Conducting an empirically comparative analysis of them can also set up pilot scheme for other economies to develop more efficient strategies for energy consumption. The paper modified the original Geographical Detector model with a different sampling method to detect the key driver of energy consumption growth rate, which filling the gap that there are possible interactions of potential factors. The results show that coal intensity is the biggest driver to change overall energy consumption growth rate in China and India. In comparison, for the United States, the leading drivers of energy use are the factors of individual incomes and oil intensity. In addition, all factors have interactions and enhance each other when influencing total energy consumption growth rate. India has the strongest factor interactions when influencing the energy consumption growth rate among the three economies, all interactions between factors in US is not significant as those in China and India. Besides providing outcomes that can contribute towards developing new strategies to use energy more efficiently, this research offers a pilot example of analyzing energy issues from the perspective of stratified heterogeneity in consideration the characteristic differences of each factor.

Exploring the perceptions of drivers of energy behaviour

Many models have been applied to predict energy use and savings, yet few studies have investigated people's own perceptions of what drives their energy use. Understanding these perceptions can help design energy policy that is likely to be trusted and perceived as credible. This study assessed the perception of the drivers of energy use among young adults, who had recently become independent energy consumers, but were not yet paying for their energy bills. Focus groups were conducted in which the drivers of energy use were discussed, and discussions were analysed using a framework of a successful existing behavioural model – the Comprehensive Action Determination Model – that includes both conscious and unconscious drivers of energy consumption. The findings show (1) participants did not tend to believe they saved energy to conserve the environment, (2) adherence to egoistic values, apparent in the lack of motivation to save energy in the absence of financial incentives, and (3) strong awareness of energy habits. Policy makers targeting young adults' energy use are advised how to align energy policy with these perceptions.

Uncovering Variations, Determinants, and Disparities of Multisector-Level Final Energy Use of Industries Across Cities

With continuous industrialization and urbanization, cities have become the dominator of energy consumption, to which industry is making leading contribution among all sectors. Given the insufficiency in comparative study on the drivers of energy use across cities at multisector level, this study selected seven representative cities in China to quantify and analyze the contributions of factors to changes in final energy use (FEU) in industrial aggregate and sectoral levels by using Logarithmic Mean Divisia Index method. Disparities in the drivers of industrial FEU across cities were explicitly revealed within two stages (2005–2010 and 2010–2015). Some key findings are presented as follows. Alongside the increase in industrial output of seven cities within two stages, the variation trends in industrial FEU are different. Industrial output effect (contribution rate 16.7% ~ 184.0%) and energy intensity effect (contribution rate −8.6% ~ −76.5%) contributed to the increase in aggregate FEU positively and negatively, respectively. Beijing had the largest contribution share of industrial structure effect (−24.4% and −12.8%), followed by Shenyang and Xi’an. Contributions of energy intensity effect and industrial output effect for Chemicals, Nonmetals, Metals, and Manufacture of equipment were much larger than those of other sectors. The results revealed that production technological innovations, phase-out of outdated capacities of energy intensive industries, and industrial restructuring are crucial for reduction in industrial FEU of cities. This study also provided reference to reasonable industrial layout among cities and exertion of technological advantages from a national perspective.

A data-driven analysis of building energy use with emphasis on operation and maintenance: A case study from the UAE

Abstract A pre-requisite to reducing the energy intensity of the building sector is to assess and understand current drivers of energy consumption. Drivers include building design characteristics, outdoor environmental conditions, as well as the operation patterns of building systems by facility managers and occupants. In the United Arab Emirates (UAE), where buildings consume more than 70% of the total electricity generated, a comprehensive analysis of the drivers of building energy performance has not yet been conducted. This paper proposes an empirical top-down statistical framework to comprehensively evaluate drivers of energy use for a stock of buildings. The approach is unique in its ability to capture the combined effects of technical, as well as, operation-related building parameters. A combination of data analysis techniques, including CHAID decision trees and regression models, is proposed and applied to data gathered from 713 mixed-use buildings in downtown Abu Dhabi. Results indicate that in addition to physical building parameters such as window tinting, operational parameters, including AC cleanliness, chiller condition, and thermostat temperature setting have shown significant impacts on energy consumption levels. The findings shed light on the crucial – yet often overlooked – role of facility managers and occupants in ensuring efficient energy performance in the building sector.

Benchmarking building energy efficiency using quantile regression

We propose a new building energy use benchmarking system to rank buildings via quantile regression. This methodology addresses several leading issues with current benchmarking practices by constructing a data-driven probabilistic model of performance, reducing outlier-effects, determining the varying effect of inputs across the distribution, and creating a theoretical maximum performance-level for each building. Influence plots constructed to examine a variable's effect on the conditional distribution can identify main drivers of energy consumption at each quantile, visually displaying any nonlinear effects on energy consumption. The methodology produces a score for each building based on efficiency, compares buildings with their constructed distribution of scores, and extracts the strongest indicators of energy use. To illustrate the model's effectiveness, we analyzed electricity consumption from a dataset containing ∼1000 buildings and found that cooling degree days and the presence of gyms, spas, and elevators were large drivers of energy use. Additionally, the number of employees per unit area had a larger effect on total energy consumption for poor performing buildings as compared to top performers. This more robust and standardized benchmarking model may improve resource allocation for energy-efficient programs, encourage competition between buildings, put pressure on poor performers, and provide insight into building energy drivers.

Understanding energy demand in Kuwaiti villas: Findings from a quantitative household survey

Residential buildings in Kuwait account for almost 60% of the country's national electrical power generated, considerably greater than all other sectors or building types. This paper identifies key drivers of energy use in Kuwaiti villas based on a survey of 250 households undertaken throughout the six districts of Kuwait. The survey consists of a cross-sectional interviewer-administered questionnaire designed to gather detailed information about building physical characteristics, occupants’ socio-demographic background and energy-use behaviours. Survey data is analysed using both statistical descriptive methods and multiple linear regression analysis to identify key determinants of energy consumption in a sample of Kuwaiti villas. Analysis indicates that an occupant driven cooling behaviour (air-conditioning thermostat temperature set points) is the major driver of energy use, followed by the number of rooms and the number of occupants. Together, such drivers significantly explain 32% of the variability in energy consumption Survey findings provide descriptive information about Kuwaiti households and insights into the key drivers of energy use to better inform further research and policy interventions in this field.

Does energy flux predict density-dependence? An empirical field test.

Changes in population density alter the availability, acquisition, and expenditure of resources by individuals, and consequently their contribution to the flux of energy in a system. While both negative and positive density-dependence have been well studied in natural populations, we are yet to estimate the underlying energy flows that generate these patterns and the ambivalent effects of density make prediction difficult. Ultimately, density-dependence should emerge from the effects of conspecifics on rates of energy intake (feeding) and expenditure (metabolism) at the organismal level, thus determining the discretionary energy available for growth. Using a model system of colonial marine invertebrates, we measured feeding and metabolic rates across a range of population densities to calculate how discretionary energy per colony changes with density and test whether this energy predicts observed patterns in organismal size across densities. We found that both feeding and metabolic rates decline with density but that feeding declines faster, and that this discrepancy is the source of density-dependent reductions in individual growth. Importantly, we could predict the size of our focal organisms after eightweeks in the field based on our estimates of energy intake and expenditure. The effects of density on both energy intake and expenditure overwhelmed the effects of body size; even though higher density populations had smaller colonies (with higher mass-specific biological rates), density effects meant that these smaller colonies had lower mass-specific rates overall. Thus, to predict the contribution of organisms to the flux of energy in populations, it seems necessary not only to quantify how rates of energy intake and expenditure scale with body size, but also how they scale with density given that this ecological constraint can be a stronger driver of energy use than the physiological constraint of body size.

Exploring the everyday energyscapes of rural dwellers in Wales: Putting relational space to work in research on everyday energy use

Rural dwellers face a series of considerable, inter-locking challenges in the coming transition to a low-carbon society. As the highest emitters of domestic carbon per head of capita in Britain, understanding how and why rural households use energy in the ways that they do, and how this changes through time, is critical to gaining an insight into the ways in which we might reduce domestic energy demand.Although a plethora of conceptual approaches exists for enriching our understanding of the social drivers of energy use and demand, it is also important to better elucidate processes that give form to lives as lived in relational rural spaces. The article deploys complementary concepts of biography, practice and lived relational space, utilises them as part of a bespoke methodology for studying extended case narratives, and reports original analyses of more nuanced understandings of sense-making about dynamic changes in life processes and lived spaces. Insights are offered into difficult to resolve narrative tensions arising when expectations, uncertainties, aspirations and imaginaries work in a relational way to frame energy use in the present, and when socio-cultural ideals and identity-forming processes manifest in rural dwellers’ energyscapes are involved in the making of the future present.

Defining geographical boundaries with social and technical variables to improve urban energy assessments

Within residential electricity consumption there exists significant variability from home-to-home due to the differences in building thermal properties, appliances, and inhabitants. Electricity analyses at sub-city scales using predefined geographies, such census tracts, might artificially split areas with homogenous characteristics leading to analyses that don't effectively contrast the drivers of energy use. The objective of this study is to use the spatial relationships between demographics, building types, and electricity consumption to form new geographies with less variability for use in residential energy assessment. Using Los Angeles and New York City as case studies, differences in energy use variability within predefined geographies (e.g., census tract) are compared to geographies defined by clustering on socio-technical characteristics. Socio-technical clustering, regardless of the chosen subset of variables, reduces the energy consumption variability over pre-defined geopolitical boundaries with high statistical significance (p<<0.0001). By clustering, intra-regional variability is reduced by 13% in Los Angeles and 29% in New York, therefore improving opportunities for prediction and forecasting. This is the first study to examine the role of spatial boundaries in urban energy assessment. The creation of socio-technical geographies for electricity assessment creates opportunities for improving predictions and forecasts for future sub- and cross-city energy studies.

Energy-climate-manufacturing nexus: New insights from the regional and global supply chains of manufacturing industries

The main objectives of this research are to improve our understanding of energy-climate-manufacturing nexus within the context of regional and global manufacturing supply chains as well as show the significance of full coverage of entire supply chain tiers in order to prevent significant underestimations, which might lead to invalid policy conclusions. With this motivation, a multi region input–output (MRIO) sustainability assessment model is developed by using the World Input–Output Database, which is a dynamic MRIO framework on the world’s 40 largest economies covering 1440 economic sectors. The method presented in this study is the first environmentally-extended MRIO model that harmonizes energy and carbon footprint accounts for Turkish manufacturing sectors and a global trade-linked carbon and energy footprint analysis of Turkish manufacturing sectors is performed as a case study. The results are presented by distinguishing the contributions of five common supply chain phases such as upstream suppliers, onsite manufacturing, transportation, wholesale, and retail trade. The findings showed that onsite and upstream supply chains are found to have over 90% of total energy use and carbon footprint for all industrial sectors. Electricity, Gas and Water Supply sector is usually found to be as the main contributor to global climate change, and Coke, Refined Petroleum, and Nuclear Fuel sector is the main driver of energy use in upstream supply chains. Overall, the largest portion of total carbon emissions of Turkish manufacturing industries is found in Turkey’s regional boundary that ranged between 40% and 60% of total carbon emissions. In 2009, China, United States, and Rest-of-the-World’s contribution is found to be more than 50% of total energy use of Turkish manufacturing. The authors envision that a global MRIO framework can provide a vital guidance for policy makers to analyze the role of global manufacturing supply chains and prevent significant underestimations due to inclusion of limited number of tiers for sustainable supply chain management research.

Understanding Variability To Reduce the Energy and GHG Footprints of U.S. Ethylene Production.

Recent growth in U.S. ethylene production due to the shale gas boom is affecting the U.S. chemical industry's energy and greenhouse gas (GHG) emissions footprints. To evaluate these effects, a systematic, first-principles model of the cradle-to-gate ethylene production system was developed and applied. The variances associated with estimating the energy consumption and GHG emission intensities of U.S. ethylene production, both from conventional natural gas and from shale gas, are explicitly analyzed. A sensitivity analysis illustrates that the large variances in energy intensity are due to process parameters (e.g., compressor efficiency), and that large variances in GHG emissions intensity are due to fugitive emissions from upstream natural gas production. On the basis of these results, the opportunities with the greatest leverage for reducing the energy and GHG footprints are presented. The model and analysis provide energy analysts and policy makers with a better understanding of the drivers of energy use and GHG emissions associated with U.S. ethylene production. They also constitute a rich data resource that can be used to evaluate options for managing the industry's footprints moving forward.