Energy Consumption of Lactating Mothers: Current Situation and Problems

Recommendations on the adequacy of nutrient intake indicate that lactating mothers have higher nutritional needs than do pregnant mothers. High nutrient intake is necessary to help mothers recover after childbirth, produce milk, and maintain the quantity and quality of breast milk. It also prevents maternal malnutrition. Research has shown, however, that the dietary energy consumption of mothers during lactation was significantly lower than that during pregnancy. The current study explored the factors associated with decreased nutritional intake during maternal lactation. The study was conducted in March–April 2013, and the subjects were mothers with infants aged >6 months. Results revealed that the factors causing low dietary energy consumption among breastfeeding mothers were poor nutritional knowledge and attitude toward high energy intake requirements during lactation, lack of time to cook and eat because of infant care, reduced consumption of milk and supplements, dietary restrictions and prohibitions, and suboptimal advice from midwives/health personnel. Beginning from the antenatal care visit, health personnel should conduct effective counseling on the importance of nutrient intake during lactation. Advice should be provided not only to mothers, but also to their families to enable them to thoroughly support the mothers as they breastfeed their infants

Do homes that are more energy efficient consume less energy?: A structural equation model for England's residential sector

Energy consumption from the residential sector is a complex sociotechnical problem that can be explained using a combination of physical, demographic and behavioural characteristics of a dwelling and its occupants. A structural equation model (SEM) is introduced to calculate the magnitude and significance of explanatory variables on residential energy consumption. The benefit of this approach is that it explains the complex relationships that exist between manifest variables and their overall effect through direct, indirect and total effects on energy consumption. Using the English House Condition Survey (EHCS) consisting of 2531 unique cases, the main drivers behind residential energy consumption are found to be the number of household occupants, floor area, household income, dwelling efficiency (SAP), household heating patterns and living room temperature. In the multivariate case, SAP explains very little of the variance of residential energy consumption. However, this procedure fails to account for simultaneity bias between energy consumption and SAP. Using SEM its shown that dwelling energy efficiency (SAP), has reciprocal causality with dwelling energy consumption and the magnitude of these two effects are calculable. When nonrecursivity between SAP and energy consumption is allowed for, SAP is shown to have a moderately negative effect on energy consumption but conversely, homes with a propensity to consume more energy have a higher SAP rating and are therefore more efficient

The relationships between total, electricity and biofuels residential energy consumption and income in Latin America and the Caribbean Countries

Controlling residential energy consumption in Latino America and the Caribbean countries is crucial to reduce CO2 emissions, as it has an important energy-saving potential, and its environmental controls are difficult to displace offshore. The aim of this study is to analyze the relationships between residential energy consumption and income for 22 Latin America and the Caribbean countries in the period 1990-2013. For this purpose, residential energy environmental Kuznets curves (EKC) are estimated by taking into account the heterogeneity among the countries by including two control variables: one representing the possible effect of urbanization on residential energy use and the second representing the possible effect of petrol production. The EKC are estimated for total residential energy consumption, for residential electricity consumption and for biofuels and waste energy consumption. The elasticities of total, electricity and biofuels residential energy consumption with respect to income are calculated for each year and country, analyzing the different behavior between countries. Obtained results show that the EKC hypothesis is confirmed for the residential sector when the biofuels energy consumption is considered. Moreover, the results also show that the turning point has been reached in some countries. Nevertheless, the EKC is not confirmed when electricity or total residential energy consumption is considered. Thus, for total residential energy consumption, the elasticity is always positive, growing also as the income does. For electricity energy consumption, the elasticity is also always positive, since although the elasticity decreases until a threshold, from an per capita income value it begins to grow

A Lightweight Privacy-Preserved Spatial and Temporal Aggregation of Energy Data

Smart grid provides fine-grained real time energy consumption, and it is able to improve the efficiency of energy management. It enables the collection of energy consumption data from consumer and hence has raised serious privacy concerns. Energy consumption data, a form of personal information that reveals behavioral patterns can be used to identify electrical appliances being used by the user through the electricity load signature, thus making it possible to further reveal the residency pattern of a consumer’s household or appliances usage habit. This paper proposes to enhance the privacy of energy con- sumption data by enabling the utility to retrieve the aggregated spatial and temporal consumption without revealing individual energy consumption. We use a lightweight cryptographic mech- anism to mask the energy consumption data by adding random noises to each energy reading and use Paillier’s additive homo- morphic encryption to protect the noises. When summing up the masked energy consumption data for both Spatial and Temporal aggregation, the noises cancel out each other, hence resulting in either the total sum of energy consumed in a neighbourhood at a particular time, or the total sum of energy consumed by a household in a day. No third party is able to derive the energy consumption pattern of a household in real time. A proof-of- concept was implemented to demonstrate the feasibility of the system, and the results show that the system can be efficiently deployed on a low-cost computing platform

Geovisualization of household energy consumption characteristics

A vast amount of quantitative data is available within the energy sector, however, there is limited understanding of the relationships between neighbourhoods, demographic characteristics and domestic energy consumption habits. We report upon research that will combine datasets relating to energy consumption, saving and loss with geodemographics to enable better understanding of energy user types. A novel interactive interface is planned to evaluate the performance of these energy-based classifications. The research aims to help local governments and the energy industry in targeting households and populations for new energy saving schemes and in improving efforts to promote sustainable energy consumption. Energy based neighbourhood classifications will also promote consumption awareness amongst domestic users. This poster describes the research methodology, data sources and visualization requirements

Predicting fuel energy consumption during earthworks

This research contributes to the assessment of on-site fuel consumption and the resulting carbon dioxide emissions due to earthworks-related processes in residential building projects, prior to the start of the construction phase. Several studies have been carried out on this subject, and have demonstrated the considerable environmental impact of earthworks activities in terms of fuel consumption. However, no methods have been proposed to estimate on-site fuel consumption during the planning stage. This paper presents a quantitative method to predict fuel consumption before the construction phase. The calculations were based on information contained in construction project documents and the definition of equipment load factors. Load factors were characterized for the typical equipment that is used in earthworks in residential building projects (excavators, loaders and compactors), taking into considering the type of soil, the type of surface and the duration of use. We also analyzed transport fuel consumption, because of its high impact in terms of pollution. The proposed method was then applied to a case study that illustrated its practical use and benefits. The predictive method can be used as an assessment tool for residential construction projects, to measure the environmental impact in terms of on-site fuel consumption. Consequently, it provides a significant basis for future methods to compare construction projects.Peer ReviewedPostprint (author's final draft

Urban energy consumption and CO2 emissions in Beijing: current and future

This paper calculates the energy consumption and CO2 emissions of Beijing over 2005–2011 in light of the Beijing’s energy balance table and the carbon emission coefficients of IPCC. Furthermore, based on a series of energy conservation planning program issued in Beijing, the Long-range Energy Alternatives Planning System (LEAP)-BJ model is developed to study the energy consumption and CO2 emissions of Beijing’s six end-use sectors and the energy conversion sector over 2012–2030 under the BAU scenario and POL scenario. Some results are found in this research: (1) During 2005–2011, the energy consumption kept increasing, while the total CO2 emissions fluctuated obviously in 2008 and 2011. The energy structure and the industrial structure have been optimized to a certain extent. (2) If the policies are completely implemented, the POL scenario is projected to save 21.36 and 35.37 % of the total energy consumption and CO2 emissions than the BAU scenario during 2012 and 2030. (3) The POL scenario presents a more optimized energy structure compared with the BAU scenario, with the decrease of coal consumption and the increase of natural gas consumption. (4) The commerce and service sector and the energy conversion sector will become the largest contributor to energy consumption and CO2 emissions, respectively. The transport sector and the industrial sector are the two most potential sectors in energy savings and carbon reduction. In terms of subscenarios, the energy conservation in transport (TEC) is the most effective one. (5) The macroparameters, such as the GDP growth rate and the industrial structure, have great influence on the urban energy consumption and carbon emissions

How Peer Effects Influence Energy Consumption

This paper analyzes the impact of peer effects on electricity consumption of a network of rational, utility-maximizing users. Users derive utility from consuming electricity as well as consuming less energy than their neighbors. However, a disutility is incurred for consuming more than their neighbors. To maximize the profit of the load-serving entity that provides electricity to such users, we develop a two-stage game-theoretic model, where the entity sets the prices in the first stage. In the second stage, consumers decide on their demand in response to the observed price set in the first stage so as to maximize their utility. To this end, we derive theoretical statements under which such peer effects reduce aggregate user consumption. Further, we obtain expressions for the resulting electricity consumption and profit of the load serving entity for the case of perfect price discrimination and a single price under complete information, and approximations under incomplete information. Simulations suggest that exposing only a selected subset of all users to peer effects maximizes the entity's profit.Comment: 9 pages, 4 figure