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The operation of a building connects the building, its performance and its end-users. When a mismatch occurs between users' expectations and operations processes, then complaints can arise. The use of enhanced complaint handling processes is investigated to ascertain whether this can help diagnose performance problems. Two LEED Platinum-rated office buildings form the basis for a case study of the components that make up an enhanced complaint handling process. Some of the social dynamics of complaints in buildings are consequently discussed. It is found that an enhanced complaint handling approach could contribute to a form of ongoing commissioning that goes beyond primarily reactive or dismissive treatments of complaints. Le fonctionnement d'un immeuble etablit des rapports entre l'immeuble, ses performances et ses utilisateurs finaux. Lorsqu'un decalage se produit entre les attentes des utilisateurs et les processus d'exploitation, des plaintes peuvent alors s'elever. L'utilisation de processus ameliore...

Global wind resources greatly exceed current electricity demand and the levelized cost of energy from wind turbines has shown precipitous declines. Accordingly, the installed capacity of wind turbines grew at an annualized rate of about 14% during the last two decades and wind turbines now provide ~6–7% of the global electricity supply. This renewable electricity generation source is thus already playing a role in reducing greenhouse gas emissions from the energy sector. Here we document trends within the industry, examine projections of future installed capacity increases and compute the associated climate change mitigation potential at the global and regional levels. Key countries (the USA, UK and China) and regions (e.g., EU27) have developed ambitious plans to expand wind energy penetration as core aspects of their net-zero emissions strategies. The projected climate change mitigation from wind energy by 2100 ranges from 0.3–0.8 °C depending on the precise socio-economic pathway and wind energy expansion scenario followed. The rapid expansion of annual increments to wind energy installed capacity by approximately two times current rates can greatly delay the passing of the 2 °C warming threshold relative to pre-industrial levels. To achieve the required expansion of this cost-effective, low-carbon energy source, there is a need for electrification of the energy system and for expansion of manufacturing and installation capacity.

We assessed the physical, chemical and toxicological characteristics of particulate emissions from four light-duty gasoline direct injection vehicles when operated over the LA92 driving cycle. Our results showed that particle mass and number emissions increased markedly during accelerations. For three of the four vehicles tested, particulate matter (PM) mass and particle number emissions were markedly higher during cold-start and the first few accelerations following the cold-start period than during the hot running and hot-start segments of the LA92 cycle. For one vehicle (which had the highest emissions overall) the hot-start and cold-start PM emissions were similar. Black carbon emissions were also much higher during the cold-start conditions, indicating severe fuel wetting leading to slow evaporation and pool burning, and subsequent soot formation. Particle number concentrations and black carbon emissions showed large reductions during the urban and hot-start phases of the test cycle. The oxidative potential of PM was quantified with both a chemical and a biological assay, and the gene expression impacts of the PM in a macrophage model with PCR (polymerase chain reaction) and ELISA (enzyme-linked immunosorbent assay) analyses. Inter- and intra-vehicle variability in oxidative potential per milligram of PM emitted was relatively low for both oxidative assays, suggesting that real-world emissions and exposure can be estimated with distance-normalized emission factors. The PCR response from signaling markers for oxidative stress (e.g., NOX1) was greater than from inflammatory, AhR (aryl hydrocarbon receptor), or MAPK (mitogen-activated protein kinase) signaling. Protein production associated with inflammation (tumor necrosis factor alpha-TNFα) and oxidative stress (HMOX-1) were quantified and displayed relatively high inter-vehicle variability, suggesting that these pathways may be activated by different PM components. Correlation of trace metal concentrations and oxidative potential suggests a role for small, insoluble particles in inducing oxidative stress.

A statement in this recently published paper makes a point that is largely at odds with the main point of the paper that is cited. Stating that higher air temperatures lead to greater evapotranspiration is an oversimplification; the true story is more complex. Although this is by no means central to the conclusions of the paper being commented on, we have demonstrated the danger in taking too literally the idea that air temperature determines potential evapotranspiration.

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.

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.

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.