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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.

Integrated assessment models (IAMs) are playing an increasingly important role in long-run sustainability analysis. At their core is a set of global economic and environmental accounts which capture a complete set of inter-industry and inter-regional relationships in the global economy in a consistent manner. While much attention is focused on the raw data and parameterization required to expand or add sectoral detail to IAMs, only rarely is there discussion of how different matrix balancing methods (i.e. translating disparate raw data sources into the consistent database) affect modeling results. This article offers an in-depth look into the database–modeling nexus in IAMs, focusing on the electric power sector which is both a major source of CO2 emissions and a critical vehicle for climate change mitigation. Comparisons of the prevailing matrix balancing algorithms show how the choice of database reconciliation methodology affects modeling results using policy-relevant simulations in the context of the electric power sector. The resulting insights can be applied to the disaggregation of other, technology rich sectors in the economy. We conclude that appropriate selection of database reconciliation methodologies can reduce the deviation between bottom-up and top-down modeling.

Abstract To ensure long-term availability of mobility and to keep the effects of humanity on global climate limited, politics, research, and industry currently search for ways to reduce greenhouse gas (GHG) emissions caused by it. A potential reduction could be achieved by transitioning towards more efficient transport modes, optimally powered by renewable energy. Based on mobility (commutes), population, and weather data from Switzerland, we present a model to compute energy savings due to a (hypothetical) widespread deployment of electric bicycles. In different scenarios, we analyze the dependence of the commuting energy demand on users’ preferences about when to take the bike, such as an aversion to biking on cold and rainy days. Our study shows that GHG emission reductions of up to approx. 10% of the total emissions from diesel and gasoline are possible. In combination with a widespread deployment of electric vehicles, further savings of up to 17.5% could be achieved. In particular, the willingness to drive longer commutes by e-bike influences the potential GHG emission reductions, followed by the willingness to use the bicycle at temperatures below 10°C. Using these results, we identify the spatial energy and greenhouse gas savings potential and thus regions that are particularly suited for e-bike use. The identification of regions with high saving potentials allows for targeted marketing, transition-supporting incentives, or infrastructural changes to maximize the reduction of emissions.

Background: Behavioral studies using pharmacological manipulations that increase neuronal activity of the central nucleus of the amygdala (CeA) have implicated the CeA in enhancement of memory modulation. To date, however, there has been a dearth of studies investigating the effect of a drug that decreases CeA activity on memory modulation—a drug that inhibits the neuronal activity of the CeA might be expected to impair memory modulation. To determine whether ethanol inhibits CeA activity and, if so, whether decreased CeA activity is associated with impairment of memory modulation, this study investigated the effect of ethanol on spontaneous single‐unit activity of CeA neurons and retention in the passive‐avoidance task.Methods: The effect of ethanol (0.35, 0.75, 1.5, 2.5 g/kg) was determined on spontaneously firing neurons in the CeA in urethane‐anesthetized rats by use of standard in vivo single‐unit electrophysiological recording techniques. Additionally, the effect of ethanol when administered immediately after training in a standard passive‐avoidance task was determined on retention the following day.Results: Ethanol profoundly inhibited spontaneous CeA firing rates in urethane‐anesthetized rats at all doses tested. Maximal inhibition was related to dose. Each dose of ethanol significantly inhibited CeA activity within 15 min of administration; within 35 min of administration, 0.75 g/kg of ethanol inhibited CeA activity by 65.2%, and the highest dose (2.5 g/kg) produced nearly complete suppression of CeA activity (81.3%). Although ethanol markedly inhibited CeA activity, these same doses of ethanol failed to impair retention in the passive‐avoidance task: 0.35, 0.75, 1.5, and 2.5 g/kg of ethanol, administered immediately after training, failed to alter latency to step‐through the following day.Conclusions: These results show that ethanol profoundly inhibits spontaneous CeA activity and suggest that inhibition of the CeA is not sufficient to impair retention in the passive‐avoidance task.

In this paper we seek to understand the impact of expanded use of soybean oil biodiesel to address biofuel mandates on global vegetable oil markets, and in particular on the demand for palm oil. An open-economy equilibrium model is derived to investigate the market effects of biodiesel expansion on related energy and vegetable oil markets. The model is calibrated to represent the recent benchmark data in calendar year 2011. The simulation estimates suggest that the expanded use of soy oil for biodiesel in the US will have considerable impacts on world vegetable oils markets. The majority of the vegetable oil replacement is likely to occur through substitution of palm oil under a wide range of plausible elasticity values on the demand for vegetable oil and the demand substitution between soy oils and palm oils.

Three groups of rats were given avoidance training followed by extinction. One group received one acquisition session immediately followed by extinction. A second group received two acquisition sessions while in the non-drug state, followed by extinction. A third group received two acquisition sessions, one while under the effects of alcohol and the second under no-drug conditions, followed by extinction. The group trained under two drug states made significantly more responses than the other two groups, while the group which received two training sessions while undrugged also made more responses than the group which received a single session. The results indicated that an avoidance response trained under more than one drug state is more resistant to extinction than a response learned only in one drug state.

The upsurge in higher education is considered a key determinant for enhancing green growth. Moreover, ICT development is also the main catalyst of green growth. This research explores the role of higher education and ICT on green growth for China from 1995 to 2020. The study employs auto-regressive distributive lag (ARDL) approach for short-run and long-run estimates of green growth. The effect of higher education and ICT on green growth is significantly positive in the long run and short run. The outcomes of the empirical models reveal that financial inclusion is positively associated with green growth in both long run and short run. Moreover, renewable energy consumption is found to have a positive impact on green growth. The findings thus point to the need for policies that promote human capital and ICT infrastructure as a way of accelerating green growth.