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Abstract The adoption rate and stringency of building energy standards in the U.S. have been increasing since the mid-1990s as a result of the Energy Policy Act mandate of 1992 (EPAct). Current evidence on the energy savings that accrue from commercial building energy standards is based on engineering simulations, which do not account for realized behaviour once a standard is actually adopted. This paper uses quasi-experimental variation in commercial building energy standard adoptions to estimate their effect on realized electricity consumption and cost-effectiveness. In states induced by EPAct to adopt an energy standard where all new nonresidential construction was erected under a commercial standard, electricity consumption per service worker is lower by about 12%, and total commercial electricity consumption is lower by 10%. Including early adopters and never-adopters to the analysis leads to a downward bias in the treatment effect. The realized electricity savings in the EPAct states represent three quarters of predicted simulated savings, and electricity saved in 2010 came at a cost of approximately 7.7 cents per kWh.

Abstract For 118 million residential housing units in the U.S., there is currently a gap between the potential energy savings that can be achieved through the use of existing energy efficiency technologies, and the actual level of energy savings realized, particularly for the 37% of housing units that are considered residential rental properties. Additional quantifiable benefits are needed beyond energy savings to help further motivate residential property owners to invest in energy efficiency upgrades. This research focuses on assessing the adoption of energy efficient upgrades in U.S. residential housing and the impact on rental prices. Ten U.S. cities are chosen for analysis; these cities vary in size across multiple climate zones, and represent a diverse set of housing market conditions. Data was collected for over 159,000 rental property listings, their characteristics, and their energy efficiency measures listed in rental housing postings across each city. Following an extensive data quality control process, over thirty different types energy efficient features were identified. The level of adoption was determined for each city, ranging from 5.3% to 21.6%. Efficient lighting and appliances were among the most common, with many features doubling as energy efficient and other desirable aesthetic or comfort improvements. Then using propensity score matching and conditional mean comparison methods, the relative impact on rent charged in each city was calculated, which ranged from a 6% to 14.1% increase in rent for properties with energy efficient features, demonstrating a positive economic impact of these features, particularly for property owners. This was further subdivided into five types of energy efficiency upgrade and three housing types. Single family homes generally demanded higher premiums with energy efficient features, however there was not a consistent pattern across the types of efficient upgrades. The results of this work demonstrate that investment in energy efficient technologies has quantifiable benefits for rental property owners in the U.S. beyond just energy savings. This methodology and results can also be used in other cities and by property owners, utility companies, or others, ultimately encouraging further investment and positive economic impact in residential energy efficiency and in turn improving energy and resource conservation in the building sector.

Low-salinity water injection emerges to be a cost-effective and environmentally friendly enhanced oil recovery technique. Furthermore, additives, such as the surfactant and nanoparticles in combina...

Abstract A new formulation for the evaporation, flashing, condensation processes taking place in the effects of thermal desalination systems which simulates the operation of both forward and parallel/cross configurations is coupled with an exergo-economic model based on the SPECO method. The thermo-economic model uses accurate properties for the seawater, brine, pure water and vapour and is solved with an equation solver which does not require the development of a specific solution algorithm as in most previous studies. This flexible model is used to analyze the influence of the number of effects N and the temperature difference ΔT e between effects on the technical and economic performance of multi-effect desalination systems with ejector vapour compression. In particular, it is shown that the performance calculated by an earlier black-box approach is not attainable by technically and economically realistic systems. It is also shown that for each feed configuration and a given number of effects there exists an optimum value of ΔT e which minimizes the cost of the produced potable water. This last result forms the basis of a procedure that combines black-box results with the optimum value of ΔT e and can be used to select the appropriate system for any specific application.

Abstract Conventional spouted beds have been extensively used in many real-life applications but are not suited for all types of materials, especially fine particles, which require internal devices to improve their motion in the spouted bed. However, unlike conventional spouted beds, there are almost no mechanistic or empirical models available for the design of spouted beds with internals. Given the availability of an extensive but not experimentally designed database, the main purpose of this study is to present an analysis of neural networks and empirical models in terms of their suitability to fit and predict average cycle times in conical spouted beds with and without draft tubes. The parameters investigated are particle size, density, contactor angle, gas inlet diameter, static bed height, and draft tube features. Although the amount of information is always a key factor when fitting models, the size of the database used in this study strongly affects the fitting performance of empirical models, whereas artificial neural networks are more influenced by how the data are scaled. Results of model verification show that both techniques are suitable for predicting average cycle times for data outside the range covered by the database.

The Nigerian government is committed to sustaining rice production to meet national demand. Nevertheless, political tension and climate-induced stressors remain crucial constraints in achieving policy targets. This study examines whether climate change and political instability significantly threaten rice production in Nigeria. First, we employed nonparametric methods to estimate the country's rainfall and temperature trends between 1980Q1 and 2015Q4. Second, we employed the autoregressive distributed lag (ARDL) technique to examine the effects of climate change and political instability on rice production. The results show that while temperature has an increasing pattern, rainfall exhibits no significant trend. The findings from the ARDL estimate reveal that rice production responds negatively to temperature changes but is less sensitive to changes in rainfall. In addition, political instability adversely affects rice production in Nigeria. We argue that Nigeria's slow growth in rice production can be traced back to the impact of climate change and political tension in rice farming areas. As a result, reducing the overall degree of conflict to ensure political stability is critical to boosting the country's self-sufficiency in rice production. We also recommend that farmers be supported and trained to adopt improved rice varieties less prone to extreme climate events while supporting them with irrigation facilities to facilitate rice production.

Alkylated furan derivatives, such as 2,5-dimethylfuran (2,5-DMF) and 2-methylfuran (2-MF), have shown, at laboratory scale, a relatively high tendency to form soot. However, soot emissions from die...