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[en] Studies of voluntary carbon trading almost exclusively assume the additionality baselines are set by regulators who have either entirely perfect or imperfect information about the costs and emissions of projects. In practice, regulators are often less informed than project proponents; therefore, the baselines are more likely to be privately defined even for sectoral crediting. The primary concern with privately defined baselines is that baseline developers may exert their powers to manipulate the baselines, leading to increases in sectoral emission caps. This study models baseline manipulation behaviors in the context of adverse selection, where participants can self-select into the market. The theoretical results show that the extent to which the baseline is manipulated is highly dependent on who is assigned as the baseline developer. The more the baseline developer emits, the more likely the developer manipulates the baseline. The results are then further discussed in the context of the U.S. commercial building sector, where empirical methods are introduced to characterize cost and revenue functions. The empirical analysis reveals that, because of the notably low price elasticity of the offset supply, baselines are often positively biased even with third-party verifications. If that policymakers wish to allow baselines to be privately defined, they might be advised to implement baseline setting on an invitation-only basis to specific emitters that have relatively lower historical emissions. - Highlights: • This study models baseline manipulation behaviors in voluntary carbon offset programs. • The degree of manipulation is highly affected by the baseline developer's emission level. • The baselines for the building sector are often biased even with third-party verification. • The biased baselines can result in 700 million metric tons of carbon leakage each year. • Policymakers are advised to invite low emitters to be the baseline developers.
[en] Highlights: •Machine learning models were used to estimate commercial building energy consumption. •CBECS was used to train a US-wide model with five commonly available features. •Validation of the model on city-specific building data was performed for New York City. •The gradient boosting model performs best compared to Linear, SVM, and other methods. •Availability of more building features results in more accurate models. -- Abstract: Building energy consumption makes up 40% of the total energy consumption in the United States. Given that energy consumption in buildings is influenced by aspects of urban form such as density and floor-area-ratios (FAR), understanding the distribution of energy intensities is critical for city planners. This paper presents a novel technique for estimating commercial building energy consumption from a small number of building features by training machine learning models on national data from the Commercial Buildings Energy Consumption Survey (CBECS). Our results show that gradient boosting regression models perform the best at predicting commercial building energy consumption, and can make predictions that are on average within a factor of 2 from the true energy consumption values (with an score of 0.82). We validate our models using the New York City Local Law 84 energy consumption dataset, then apply them to the city of Atlanta to create aggregate energy consumption estimates. In general, the models developed only depend on five commonly accessible building and climate features, and can therefore be applied to diverse metropolitan areas in the United States and to other countries through replication of our methodology.
[en] Many domestic appliances and commercial equipment consume some electric power when they are switched off or not performing their primary purpose. The typical loss per appliance is low (from 1 to 25 W) but, when multiplied by the billions of appliances in houses and in commercial buildings, standby losses represent a significant fraction of total electricity use. Several initiatives to reduce standby losses have appeared in different parts of the world. One proposal, the 1-watt plan, seeks to harmonize these initiatives by establishing a single target for all appliances. This paper explains the background to the 1-watt plan, identifies some unresolved aspects, and gives some estimates of energy savings
[en] This work is about two microfossils fluvial units deposited by the Uruguay river during the Quaternary. These are San Salvador and Palmar formation (Plio-Pleistocene - Upper Pleistocene).The Palmar formation is a band of 4-15 km along the right bank of the Uruguay river outcropping from the eastern provinces of Corrientes and Entre Rios, to Concepcion del Uruguay
[en] The extensive use of anticoagulant rodenticides (ARs) results in widespread unintentional exposure of non-target rodents and secondary poisoning of predators despite regulatory measures to manage and reduce exposure risk. To elucidate on the potential vectoring of ARs into surrounding habitats by non-target small mammals, we determined bromadiolone prevalence and concentrations in rodents and shrews near bait boxes during an experimental application of the poison for 2 weeks. Overall, bromadiolone was detected in 12.6% of all small rodents and insectivores. Less than 20 m from bait boxes, 48.6% of small mammals had detectable levels of bromadiolone. The prevalence of poisoned small mammals decreased with distance to bait boxes, but bromadiolone concentration in the rodenticide positive individuals did not. Poisoned small mammals were trapped up to 89 m from bait boxes. Bromadiolone concentrations in yellow-necked mice (Apodemus flavicollis) were higher than concentrations in bank vole (Myodes glareolus), field vole (Microtus agrestis), harvest mouse (Micromys minutus), and common shrew (Sorex araneus). Our field trials documents that chemical rodent control results in widespread exposure of non-target small mammals and that AR poisoned small mammals disperse away from bating sites to become available to predators and scavengers in large areas of the landscape. The results suggest that the unintentional secondary exposure of predators and scavengers is an unavoidable consequence of chemical rodent control outside buildings and infrastructures.
[en] We present the Robert C. Byrd Green Bank Telescope discovery of the highly eccentric binary millisecond pulsar PSR J1835-3259A in the Fermi Large Area Telescope-detected globular cluster NGC 6652.
[en] A competing market model with a polyvariant profit function that assumes 'zeitnot' stock behavior of clients is formulated within the banking portfolio medium and then analyzed from the perspective of devising optimal strategies. An associated Markov process method for finding an optimal choice strategy for monovariant and bivariant profit functions is developed. Under certain conditions on the bank 'promotional' parameter with respect to the 'fee' for a missed share package transaction and at an asymptotically large enough portfolio volume, universal transcendental equations - determining the optimal share package choice among competing strategies with monovariant and bivariant profit functions - are obtained. (author)
[en] The literature on sustainable energy technology sees informational barriers as a major obstacle to technology adoption. In the case of solar home systems, recent studies report positive socio-economic effects on households, but technology adoption remains underwhelming. In collaboration with a local solar technology provider, we conduct a randomized controlled trial in 75 large villages in the state of Uttar Pradesh, India to examine the ability of village solar demonstrations to create markets for solar home systems. We find no effect of such demonstrations on technology sales, awareness, or perceptions of solar technology. Technology adopters report high levels of satisfaction with product quality and service, suggesting that the null finding cannot be attributed to poor technology. These findings suggest that lack of awareness is not a binding constraint on the growth of solar technology markets in the study area. Based on additional surveys, we find evidence suggesting that access to credit from rural banks is an important explanation for variation in sales across villages. These results do not prove that information and awareness are irrelevant in general, but they show that even carefully designed marketing campaigns cannot increase demand for new products in the presence of a binding credit constraint. - Highlights: • Randomized controlled trial on off-grid solar technology demonstrations in rural India. • Demonstrations did not increase product sales. • Main barrier to increased sales appears to be lack of access to credit. • Growth of India?s off-grid solar market requires policies that increase access to credit. • Rural banks should play a greater role in solar market creation.
[en] A local network of sensitive high-accuracy seismological stations has to be created for seismological observations near a hydrotechnical structure and on the banks of the storage reservoir. The layout of the network is chosen with allowance for the configuration of the reservoir and for the location of the seismogenerating zone relative to the structure
[en] Building energy benchmarking is a useful starting point for commercial building owners and operators to target energy savings opportunities. There are a number of tools and methods for benchmarking energy use. Benchmarking based on regional data can provides more relevant information for California buildings than national tools such as Energy Star. This paper discusses issues related to benchmarking commercial building energy use and the development of Cal-Arch, a building energy benchmarking database for California. Currently Cal-Arch uses existing survey data from California's Commercial End Use Survey (CEUS), a largely underutilized wealth of information collected by California's major utilities. Doe's Commercial Building Energy Consumption Survey (CBECS) is used by a similar tool, Arch, and by a number of other benchmarking tools. Future versions of Arch/Cal-Arch will utilize additional data sources including modeled data and individual buildings to expand the database