• Energy Research
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Abstract In developed countries, buildings are involved in almost 50% of total energy use and 30% of global green-house gas emissions. Buildings' operational energy is highly dependent on various building physical, operational, and functional characteristics, as well as meteorological and temporal properties. Besides physics-based building energy modeling, machine learning techniques can provide faster and higher accuracy estimates, given buildings' historic energy consumption data. Looking beyond individual building levels, forecasting buildings’ energy performance helps city and community managers have a better understanding of their future energy needs, and plan for satisfying them more efficiently. Focusing on an urban-scale, this study systematically reviews 70 journal articles, published in the field of building energy performance forecasting between 2015 and 2018. The recent literature have been categorized according to five criteria: 1. Learning Method, 2. Building Type, 3. Energy Type, 4. Input Data, and 5. Time-scale. The scarcity of building energy performance forecasting studies in urban-scale versus individual level is considerable. There is no study incorporating building functionality in terms of space functionality share percentages, nor assessing the effects of climate change on urban buildings energy performance using machine learning approaches and future weather scenarios. There is no optimal criteria combination for achieving the most accurate machine learning-based forecast, as there is no universal measure able to provide such global comparison. Accuracy levels are highly correlated with the characteristics of forecasting problems. The goal is to provide a comprehensive status of machine learning applications in urban building energy performance forecasting, during 2015–2018.

• Premise of the study: Climate change is associated with phenological shifts in an increasing number of organisms worldwide. However, accurate estimates of these shifts are dependent on long‐term data sets that include phenological observations from before annual average temperatures began to rise.• Methods: We compared the first flowering times of native prairie plants between 2007 and 2010 with historical data recorded by O. A. Stevens from 1910 to 1961. By merging climate variable data from the same time period, it also was possible to correlate first flowering dates with associated climate variables.• Key results: Over the past 100 years, spring temperatures in the Red River Valley near Fargo, North Dakota, USA, have increased, and growing seasons have lengthened significantly. Seventy‐five percent of the 178 species observed by Stevens had flowering times that were sensitive to at least one variable related to temperature or precipitation. Over the past 4 yr, 5% to 17% of the species observed have significantly shifted their first flowering time either earlier or later relative to the previous century.• Conclusions: The results of this study indicate that as spring temperatures in the northern Great Plains have increased and the growing season has lengthened, some spring flowering species have advanced their first flowering time, some fall species have delayed their first flowering, and some species have not changed. Given the importance of flowering timing for reproductive success, the changing climate in the Great Plains is expected to have long‐term ecological and evolutionary consequences for native plant species.

AbstractMany countries foresee the ability of offshore geologic settings to meet anticipated long-term national CO2 storage needs. Although the costs of operating offshore are significant, they may be offset by reduced issues of risk, property ownership and long-term liability. Continued interest in offshore CCS (e.g., EU, Australia, Japan, and U.S.A.) has focused on aspects familiar to onshore work: storage capacity, risk, monitoring, and containment assurance. Over the last decade, the Gulf Coast Carbon Center at the Texas Bureau of Economic Geology conducted numerous studies of storage potential along the eastern seaboard of the U.S. and the northern Gulf of Mexico (GOM). Much of the work focused on regional geologic characterization, capacity assessment, and identification of potential risks and migration pathways. In general, capacity estimates indicate that the offshore storage resource has been under- appreciated as a national resource in the United States, but is receiving more attention. Current research efforts focus on the offshore northwestern GOM where the bulk of potential storage capacity resides. The work has also focused attention on the significant role of regional structural compartmentalization on storage capacity, pressure evolution, potential migration pathways, and long-term containment.

AbstractThe adoption of a bioenergy crop is affected by various factors, including but not limited to the characteristics of farmers, farm economics, market forces, and physical environment. This study develops a spatially explicit agent‐based model for ascertaining the adoption rate of carinata (Brassica carinata) among the farmers in the Little River Experimental Watershed located in the southern state of Georgia in the United States. Each farmer's adoption behavior is modeled using the profitability difference between traditional crop rotations (with and without carinata at different contract prices), the adoption rate of neighboring farmers, and their land allocation decisions from managing a risky portfolio of enterprises. Carinata production in the winter season once every 3 years has no conflict with the most profitable and popular traditional row crop rotations, such as cotton‐cotton‐cotton and cotton‐cotton‐peanut, to a larger extent. The results show that 28% and 85% of farmers in the watershed will adopt carinata after 33 years at a contract price of $13/bushel (bu) under two different assumptions of low (2.5%) and high (5%) initial neighborhood adoption rates. The proportions of land allocated to carinata to the total farmland under field crops are 38% and 85% after 33 years under the same low and high neighborhood adoption rates, respectively. Our results suggest that fixing the appropriate contract price of carinata will bring additional profits to farmers without any significant foreseeable agronomic risks, thereby increasing the adoption rate of carinata at a regional level.

Abstract Kenya has made considerable policy efforts to expand its renewable energy portfolio to meet energy demand and mitigate greenhouse gas (GHG) emissions. Development of proper policies requires a robust framework for analyzing the benefits of renewable energy investments. Towards this end, this study applied a choice experiment analysis to determine how attributes (type of energy, ownership, impact on environment, distance and visibility, community job creation, and yearly renewable energy tax) impact the public willingness to pay for renewable energy development in Kenya. A nationwide survey of 1020 households was conducted in nine counties using conditional logit (MNL) and random parameter logit (RPL) frameworks. The results reveal that the Kenyan public places a high value on environmental impact, followed by type of renewable energy and community job creation, respectively. On the other hand, respondents do not place much emphasis on ownership or distance and visibility. Policy simulation suggests that while renewable energy adoption is highly valued by households, the total willingness to pay is not enough to cover the higher capital cost for the development of various renewable energy technologies.

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.

AbstractIn Laurentian Great Lakes coastal wetlands (GLCWs), dominant emergent invasive plants are expanding their ranges and compromising the unique habitat and ecosystem service values that these ecosystems provide. Herbiciding and burning to control invasive plants have not been effective in part because neither strategy addresses the most common root cause of invasion, nutrient enrichment. Mechanical harvesting is an alternative approach that removes tissue‐bound phosphorus and nitrogen and can increase wetland plant diversity and aquatic connectivity between wetland and lacustrine systems. In this study, we used data from three years of Great Lakes‐wide wetland plant surveys, published literature, and bioenergy analyses to quantify the overall areal extent of GLCWs, the extent and biomass of the three most dominant invasive plants, the pools of nitrogen and phosphorus contained within their biomass, and the potential for harvesting this biomass to remediate nutrient runoff and produce renewable energy. Of the approximately 212,000 ha of GLCWs, three invasive plants (invasive cattail, common reed, and reed canary grass) dominated 76,825 ha (36%). The coastal wetlands of Lake Ontario exhibited the highest proportion of invasive dominance (57%) of any of the Great Lakes, primarily from cattail. A single growing season's biomass of these invasive plants across all GLCWs was estimated at 659,545 metric tons: 163,228 metric tons of reed canary grass, 270,474 metric tons of common reed, and 225,843 metric tons of invasive cattail, and estimated to contain 10,805 and 1144 metric tons of nitrogen and phosphorus, respectively. A one‐time harvest and utilization for energy of this biomass would provide the gross equivalent of 1.8 million barrels of oil if combusted, or 0.9 million barrels of oil if converted to biogas in an anaerobic digester. We discuss the potential for mitigating non‐point source nutrient pollution with invasive wetland plant removal, and other potential uses for the harvested biomass, including compost and direct application to agricultural soils. Finally, we describe the research and adaptive management program we have built around this concept, and point to current limitations to the implementation of large‐scale invasive plant harvesting.

Abstract Real-world vehicle operating mode data (2.5 million 1 Hz records), collected by instrumenting the vehicles of 82 volunteer drivers with OBD datalogger and GPS while they drove their routine travel routes, were analyzed to quantify vehicle emissions estimate errors due to road grade and driving style in rural, hilly Vermont. Data were collected in winter and summer for MY 1996 and newer passenger cars and trucks only. EPA MOVES2010b was used to estimate running exhaust emissions associated with measured vehicle activity. Changes in vehicle specific power (VSP) and MOVES operating mode (OpMode) due to proper accounting for real-world road grade indicated emission rate errors between 10% and 48%, depending on pollutant, chiefly because grade-related changes in VSP could shift activity by as many as six OpModes, depending on road type. The correct MOVES OpMode assignment was made only 33–55% of the time when road grade was not included in the VSP calculation. Driving style of individual drivers was difficult to assess due to unknown traffic operations data, but the largest differences between individual drivers were observed on rural restricted roads, where traffic conditions and control have minimal impact. The results suggest the importance of (1) measuring and incorporating real-world road grade in order to correctly assign MOVES emission rates; and (2) developing a driving style typology to account for differences in the MOVES emissions estimates due to driver variability.

Abstract Process heaters are typically located outside and subject to the weather. Although heaters are typically tuned at a given set of conditions, actual operating conditions vary significantly from season to season and sometimes even within a given day. Unfortunately, most heaters are not properly adjusted for actual operating conditions. Ambient air temperature, pressure and humidity all significantly impact process heater efficiency. This paper shows how changing ambient conditions can reduce efficiency if proper adjustments are not made. Combustion efficiency is related to air:fuel ratio and to air–fuel mixing. A general industry rule-of-thumb is that operating at 2–3% excess O 2 (dry basis) results in the best combination of efficiency and flexibility. At higher O 2 levels, efficiency is reduced because the additional O 2 and N 2 absorb heat, much of which exits the exhaust stack. At lower O 2 levels, efficiency can be substantially reduced because some fuel is uncombusted. Low O 2 levels can also lead to soot and coke buildup on process tubes reducing heat transfer to the process fluid and reducing efficiency. Several examples demonstrate how ambient conditions affect heater efficiency. Calculations and graphs for a wide range of operating conditions demonstrate how efficiency can be affected by changes in ambient conditions for process heaters.