Energy consumption patterns in the accommodation sector—the New Zealand case

Recent improvements in the energy efficiency of agriculture: Case studies from Ontario, Canada

Energy use in Minnesota State-owned facilities

Energy use is an indicator of economic growth. However, high energy intensity has two main disadvantages. First, low energy efficiency increases a country’s dependence on other countries, especially when the country lacks energy sources. Second, if the country’s energy needs are met using traditional fossil fuels, this increases its CO2 emissions and reduces its air quality. Improving energy efficiency and reducing energy intensity are essential to reach the sustainability targets. This paper investigates the determinants of energy use in Turkiye for the period 1991–2019 by taking a dual approach. First, utilizing the Tapio decoupling factor, the decoupling factor analysis is not only being done for total energy use and real GDP, but also for industrial energy use and industrial income. Second, the factors determining the country’s total energy use are also examined, followed by an investigation of the indicators of energy use in the industry sector, which is highly energy intensive. For the industrial sector, two different decomposition analyses are performed and results are compared. The refined Laspeyres index method is adopted, and for each analysis, three main factors are considered. The empirical findings demonstrate that the income effect and population effect increased Turkiye’s total energy use, whereas the energy intensity effect decreased it. The first decomposition analysis for the industrial energy use reveals partly contrasting results with the previously published articles. For the industry sector, the second analysis show that productivity and employment increased Turkiye’s sectoral energy use; however, the sector’s energy intensity reduced it. Turkiye achieved some success in terms of reducing energy intensity at the sectoral and aggregate levels; however, as the findings of the present study demonstrate, further efforts are needed to lessen the country’s energy dependence and also to achieve future environmental sustainability targets. Trends relating to the determining factors in total and sectoral energy use are also compared in this paper, and some policy implications are presented.

An assessment of the energy requirements of different intensive forage production systems was carried out at the Indian Grassland Fodder, and Agroforestry Research Institute, Jhansi. This included assessment of energy use and output for five intensive crop production systems: (1) sorghum (single‐cut)–berseem, (2) cowpea–sorghum (single‐cut)–berseem + mustard–maize + cowpea, (3) sorghum (multi‐cut)–berseem + oats, (4) guar–oats–maize and (5) sorghum (single‐cut)–wheat–fallow. In all the systems, the Napier bajra hybrid (IGFRI‐3) was transplanted in regular plots of 50 m × 13 m. Results revealed that the total annual energy use was highest for sorghum (multi‐cut)–berseem + oats (36 606 MJ ha−1), followed by sorghum (single‐cut)–berseem + mustard, sorghum (single‐cut)–berseem–cowpea, guar–oats–maize and sorghum (single‐cut)–wheat–fallow (for which values were 31 086, 30 449, 29 867 and 25 956 MJ energy ha−1, respectively). The high value found for sorghum (multi‐cut)–berseem + oats might be attributable to the multi‐cuts in this system. Energy use by fertilizers represented the major part of the total energy use, amounting to 28–38 % in all treatments, followed by energy used in electricity, machinery, seeds, human labour and farmyard manure (FYM), in case of all with slight increase in input. In sorghum–wheat, energy use by seeds occupied the second position, followed by energy used in electricity, human labour, FYM and machinery/diesel. Pesticides contributed the least energy utilization in all the treatments. Herbicides were used for the control of weeds. Among the five forage production systems, sorghum (single‐cut)–berseem + mustard–maize + cowpea was found to be the most energy efficient, followed by sorghum (multi‐cut)–berseem + oats–sorghum (multi‐cut), sorghum (single‐cut)–berseem–cowpea, guar–oats–maize and sorghum (single‐cut)–wheat–fallow. Sorghum (single‐cut)–berseem + mustard–maize + cowpea increased the fertility of the soil, resulting in a higher percentage of organic carbon, higher availability of nitrogen and optimal balancing of the C:N ratio in the upper layers of the soil. These intensive crop production systems also maintain the optimum microbial population in the crop root zone. The benefit–cost ratio (B:C ratio) for the most energy‐efficient forage production system was 1.37 : 1. However, the highest B:C ratio was found in the sorghum–wheat rotation (1: 1.57).

Architects frequently face problems in choosing the most appropriate solar orientation for a building due to the constraints imposed by urban grid patterns. Urban patterns are usually designed according to urban criteria that do not address issues such as orientation, building-cluster, solar access and loading. This problem causes thermal discomfort for building occupants in spaces oriented to west and east directions, resulting from radiant temperature, as well as higher use of cooling energy. The study focuses on the orientation, form (surface area) of the building and location and number of windows on the building elevations. An energy simulation program “ENERFACE” is used to analyze the selected buildings in Al-Ain City, UAE. The cooling energy utilized per m2 and the total energies use as a result of different orientations were reported. Results show how the urban patterns could provide a better guide for architects to control the energy use in buildings. On the design level, the study concludes that the total annual energy use (kWh/fear) and the annual cost/m2 could be minimized when the location of windows are kept only on two elevations of the building and the relative orientation of these elevations is 60° North-East. This reduction is about 55%.

We analyze the operating-energy histories of three homes of different ages that have approached or attained net-use of no fossil fuels and climate neutrality. The first house (H-60) with 1,200 ft2 is a conventional 1950s house that has been caulked, insulated and equipped with an airtight woodstove and a 3.3 kW photovoltaic system that reduced its annual use of fossil fuels by 86%. Its total annual energy use excluding any passive gain is ∼57 billion joules. House two (H-30) with 2,300 ft2 is a 1980s, passive-solar house with a recently added 4.0 kW photovoltaic system that reduced its annual use of fossil fuels by 71%. Its total annual energy use excluding any passive gain is ∼58 billion joules. House three (H-1) with 1,300 ft2 is a 1 year old, passive solar house with a 3.1 kW photovoltaic system and an evacuated-tube solar hot water system that uses no fossil fuels, exports annually ∼900 kWh to the grid making it energy and climate positive, and provides all operating energy from on-site sunshine. Its total annual energy use excluding any passive gain is ∼29 billion joules.

The Missouri Agricultural Energy Saving Team-A Revolutionary Opportunity (MAESTRO) program brought together a team of representatives from government, academia, and private industry to enhance the availability of energy efficiency services for small livestock producers in the State of Missouri. The Missouri Department of Agriculture (MDA) managed the project via a subcontract with the University of Missouri (MU), College of Agriculture Food and Natural Resources, MU Extension, the MU College of Human Environmental Sciences, the MU College of Engineering, and the Missouri Agricultural and Small Business Development Authority (MASBDA). MU teamed with EnSave, Inc, a nationally-recognized expert in agricultural energy efficiency to assist with marketing, outreach, provision of farm energy audits and customer service. MU also teamed with independent home contractors to facilitate energy audits of the farm buildings and homes of these livestock producers. The goals of the project were to: (1) improve the environment by reducing fossil fuel emissions and reducing the total energy used on small animal farms; (2) stimulate the economy of local and regional communities by creating or retaining jobs; and (3) improve the profitability of Missouri livestock producers by reducing their energy expenditures. Historically, Missouri scientists/engineers conducted programs on energy use in agriculture, such asmore » in equipment, grain handling and tillage practices. The MAESTRO program was the first to focus strictly on energy efficiency associated with livestock production systems in Missouri and to investigate the applicability and potential of addressing energy efficiency in animal production from a building efficiency perspective. A. Project Objectives The goal of the MAESTRO program was to strengthen the financial viability and environmental soundness of Missouri's small animal farms by helping them implement energy efficient technologies for the production facility, farm buildings, and the homes on these farms. The expected measurable outcomes of the project were to improve the environment and stimulate the economy by: • Reducing annual fossil fuel emissions by 1,942 metric tons of carbon dioxide equivalent, reducing the total annual energy use on at least 323 small animal farms and 100 farm homes by at least 8,000 kWh and 2,343 therms per farm. • Stimulating the economy by creating or retaining at least 69 jobs, and saving small animal farmers an average of $2,071 per farm in annual energy expenditures. B. Project Scope The MAESTRO team chose the target population of small farms because while all agriculture is traditionally underserved in energy efficiency programs, small farms were particularly underserved because they lack the financial resources and access to energy efficiency technologies that larger farms deploy. The MAESTRO team reasoned that energy conservation, financial and educational programs developed while serving the agricultural community could serve as a national model for other states and their agricultural sectors. The target population was approximately 2,365 small animal farm operations in Missouri, specifically those farms that were not by definition a confined animal feeding operation (CAFO). The program was designed to create jobs by training Missouri contractors and Missouri University Extension staff how to conduct farm audits. The local economy would be stimulated by an increase in construction activity and an increasing demand for energy efficient farm equipment. Additionally, the energy savings were deemed critical in keeping Missouri farms in business. This project leveraged funds using a combination of funds from the Missouri Department of Natural Resources’ Missouri Energy Center and its Soil and Water Conservation Program, from the state's Linked Deposits, MASBDA's agricultural loan guarantee programs, and through the in-kind contribution of faculty and staff time to the project from these agencies and MU. Several hundred Missouri livestock producers were contacted during the MAESTRO project. Of the livestock producers, 254 invited the team to conduct a farm energy assessment which complied with ASABE 612. A total of 147 livestock farm upgrades were implemented, representing 57.5 percent of the farms for which a farm energy assessment was completed. This represented a statewide average annual savings of 1,088,324 kWh and 75,516 therms. The team also reviewed the condition of the livestock producer’s home(s). A total of 106 home energy assessments were completed and 48 individual homes implemented their recommended upgrades, representing 45 percent of the farm homes for which an energy assessment was completed. This represented a statewide average annual savings of 323,029 kWh, and 769.4 therms. More of these farmers likely would have updated their homes but the funding to incentivize them fell short. In spite of the shortfall in incentive funds, some farmers still updated their homes as they saw the value in making these changes to their home.« less

Defining ‘total energy use’ in economic studies: does the aggregation approach matter?

Large-scale statistical studies on the gap between the real and regulatory energy use in residential buildings in Europe have shown that the regulatory calculation overestimated the real energy use, inflated true energy savings and undermined national energy policy making. Using data from 122,680 Flemish existing single-family houses, this research builds further on existing studies by contributing results for Flanders. The study also examines to what extent available aggregated variables explain the real annual total building energy use using statistical linear models and addresses the problem of multicollinearity and the importance of bootstrapped confidence intervals for model quality control. The overestimation of the real total energy use (and potential energy savings) by the Flemish regulatory method is exceedingly large compared to studies from other EU countries. The Flemish labels prove very poor indicators of the real energy use. Statistical linear models explain up to 46.6% of all variability and indicate that a significant extent of multicollinearity had to be corrected. Half of the variability has been left unexplained and has to be attributed to variables that were not available and the fact that the data were insufficiently accurate. Future analysis will explore whether more complex models identify more evidence.

Multi-objective parametric optimization of the composite external shading for the classroom based on lighting, energy consumption, and visual comfort

This paper applies a customised post-occupancy building performance evaluation (BPE) approach to evaluate the actual performance of a Platinum-certified green office building in the extreme hot dry climate of India from both technical and occupants’ perspectives. The in-use energy and environmental performance of the office building was examined using energy data on consumption and generation, monitoring of environmental conditions (temperature, relative humidity, CO2), along with occupant satisfaction survey. Total annual energy use of the building was measured as 100 kWh/m2/year with photovoltaic generation contributing 18kWh/m2/year. Although total annual energy use was found to be 42% less than the typical office benchmark, about 6% more energy was used than predicted. Indoor air quality was perceived to still but fresh and neither too dry nor too humid. Despite this, some spaces had relative humidity measurement over 70% for more than 50% of occupied hours. Daylight factor was consistently below 2% in many areas due to internal partitions; however, BUS survey (105 responses) results showed that 71% of occupants were satisfied with lighting quality, overall comfort and other variables. Such differences between measured and perceived indoor environment indicate the role of occupant adaptation in building performance.

A simplified analysis method to predict the impact of shape on annual energy use for office buildings

Climate change and global warming are two major concerns over the globe today, and the rapid change in climate became more sensible as it impacts diverse industries and disciplines. Buildings share of the global energy use exceeds 40%, and buildings’ Green House Gas (GHG) emissions resembles one-third of global GHG emissions; hence, Building Energy Simulation (BES) process became an essential step during building design, to study and minimize whole buildings’ energy use, and consequently minimize buildings’ carbon footprint. A significant and underestimated input to the BES process is the weather data; BES process normally uses historical weather data in the form of a weather file to simulate the building’s outdoor conditions, those outdoor conditions impacts the amount of thermal load on the building, therefore they significantly impact building’s estimated thermal comfort and annual energy use. With the rapid change in the outdoor weather conditions year after year, using those historical weather data files must be questioned, as they impact the estimated energy use, and consequently put what’s deemed as the “optimum energy use” in question. Using those historical weather data is hence expected to increase BES degree of uncertainty. This paper studies and quantifies the impact of weather data accuracy on building’s annual energy performance. The paper follows an empirical research approach by simulating an office building located in Cairo, Egypt. The study relies on using a typical weather data file and real weather files to run and compare two simulations for the same case study. Finally, the paper quantifies the impact of weather data’s accuracy on the total annual energy use by comparing both simulation results.

Building energy use in China: Ceiling and scenario

Agricultural activities emit about 10-12% greenhouse gases each year. Moreover, the total amount is on the rise, which can significantly contribute to global warming. To get governments to pay more attention to greenhouse gas emissions from agricultural activities, so that the necessary regulations can be implemented. This paper studies the relationship between agricultural land expansion and greenhouse gas emissions. My initial hypothesis was that ceteris paribus, the agricultural land expansion would cause a rise in greenhouse gas emissions. I obtained 25 years of data for 12 countries from the World Development Indicators Database of the World Bank. And created a panel data frame. The variables exist in it are: Total greenhouse gas emissions (kt of CO2 equivalent), Energy use (kg of oil equivalent per capita), Alternative and nuclear energy (% of total energy use), Agricultural land (% of land area), GDP per capita (current US$). In order to cope with the possible heteroscedasticity problem in the regression, several of the variables were processed logarithms. This paper used a Two-way Fixed effect model which controls fixed effects from both cross section and time. The explained variable is log (GHG emissions), and the explanatory variables are log (energy use per capita), log (GDP per capita), clean energy using rate, and percentage of agricultural land in total land. However due to the presence of multicollinearity, log (GDP per capita) was removed. This paper also tested the autocorrelation and used the Cochrane-Orcutt Iterative Process to estimate the autocorrelation coefficient to transform all variables so that solve the autocorrelation issue. The final regression model is as follows: ln(Greenhouse Gases emission)= + 1.06*ln(Energy use per capita) – 0.009*(Alternative and nuclear energy % of total energy use) + 0.0074*(Agricultural land % of land area). = . The overall F-test of this model is significant, and the individual t-tests of the coefficients of each variable are all significant. R2=92.18, adjR2=91.04. This shows that the explanatory power of the model is strong. This outcome is consistent with my initial hypothesis that agricultural land expansion does lead to an increase in greenhouse gas emissions. The other two coefficients are also consistent with our common sense. This result can help governments to decide on the planning of land use. If the current amount of energy used and the types of energy used do not change, a country develops agriculture, it will inevitably lead to more greenhouse gas emissions. More Importantly, it is not like industrial manufacture, normally there are few regulations and policies on agricultural GHG emissions. But the quantitative results of the model tell us that as agriculture expands, it is necessary for the government to implement regulations and policies on it.