Absolute Metrics Research Articles

A supervised learning approach to estimating the urban traffic state based on floating car data

Intelligent transportation systems aim to increase the efficiency of mobility through the use of new information and communication technologies. This paper presents a supervised learning approach to estimate the traffic state in the urban road networks. The data used in this work were collected from the GPS sensors installed on the connected vehicles. We validate the proposed model using the k-fold cross-validation method and we evaluate the prediction accuracy with the root mean square error and the mean absolute error metrics. The proposed model was evaluated by numerical simulation. The results obtained by estimating the mean travel time in the road network using the proposed method showed good accuracy with a ratio of the connected vehicles not exceeding 15%.

The application of a neural network-based ragweed pollen forecast by the Ragweed Pollen Alarm System in the Pannonian biogeographical region

Ragweed Pollen Alarm System (R-PAS) has been running since 2014 to provide pollen information for countries in the Pannonian biogeographical region (PBR). The aim of this study was to develop forecast models of the representative aerobiological monitoring stations, identified by analysis based on a neural network computation. Monitoring stations with 7-day Hirst-type pollen trap having 10-year long validated data set of ragweed pollen were selected for the study from the PBR. Variables including forecasted meteorological data, pollen data of the previous days and nearby monitoring stations were used as input of the model. We used the multilayer perceptron model to forecast the pollen concentration. The multilayer perceptron (MLP) is a feedforward artificial neural network. MLP is a data-driven method to forecast the behaviour of complex systems. In our case, it has three layers, one of which is hidden. MLP utilizes a supervised learning technique called backpropagation for training to get better performance. By testing the neural network, we selected different sets of variables to predict pollen levels for the next 3 days in each of the monitoring stations. The predicted pollen level categories (low–medium–high–very high) are shown on isarithmic map. We used the mean square error, mean absolute error and correlation coefficient metrics to show the forecasting system’s performance. The average of the Pearson correlations is around 0.6 but shows big variability (0.13–0.88) among different locations. Model uncertainty is mainly caused by the limitation of the available input data and the variability in ragweed season patterns. Visualization of the results of the neural network forecast on isarithmic maps is a good tool to communicate pollen information to general public in the PBR.

Forecasting IT security vulnerabilities – An empirical analysis

Today, organizations must deal with a plethora of IT security threats and to ensure smooth and uninterrupted business operations, firms are challenged to predict the volume of IT security vulnerabilities and allocate resources for fixing them. This challenge requires decision makers to assess which system or software packages are prone to vulnerabilities, how many post-release vulnerabilities can be expected to occur during a certain period of time, and what impact exploits might have. Substantial research has been dedicated to techniques that analyze source code and detect security vulnerabilities. However, only limited research has focused on forecasting security vulnerabilities that are detected and reported after the release of software. To address this shortcoming, we apply established methodologies which are capable of forecasting events exhibiting specific time series characteristics of security vulnerabilities, i.e., rareness of occurrence, volatility, non-stationarity, and seasonality. Based on a dataset taken from the National Vulnerability Database (NVD), we use the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) to measure the forecasting accuracy of single, double, and triple exponential smoothing methodologies, Croston’s methodology, ARIMA, and a neural network-based approach. We analyze the impact of the applied forecasting methodology on the prediction accuracy with regard to its robustness along the dimensions of the examined system and software package “operating systems”, “browsers” and “office solutions” and the applied metrics. To the best of our knowledge, this study is the first to analyze the effect of forecasting methodologies and to apply metrics that are suitable in this context. Our results show that the optimal forecasting methodology depends on the software or system package, as some methodologies perform poorly in the context of IT security vulnerabilities, that absolute metrics can cover the actual prediction error precisely, and that the prediction accuracy is robust within the two applied forecasting-error metrics.

Combining a Big Data Analytics Resource System with Artificial Intelligence Algorithms to Identify Clinically Actionable Radiation Dose Thresholds for Dysphagia in Head and Neck Patients

We combined clinical practice changes and standardizations with informatics technologies to automate aggregation, integration and harmonization of comprehensive patient data from the multiple source systems used in clinical practice into a big data analytics resource system (BDARS). This system supports the development of artificial intelligence (AI) algorithms for clinically actionable analytics to guide decision frameworks. Our purpose was to combine the BDARS with AI to identify additional default planning constraints from our historic data to further reduce dysphagia risk. The BDARS harmonized data for over 22,000 patients, from which we identified 135 of 496 recent patients treated for head and neck cancer demonstrating physician graded dysphagia toxicity scores worsening from baseline to a maximum ≥ 2. Our method used both physical and biologically corrected (α/β =2.5) DVH curves from the BDARS to study both absolute and percentage volume-based DVH metrics for evidence for predicting dysphagia. The method combined a statistical categorization algorithm (SCA) with machine learning (ML) to identify DVH metrics with stronger mutual evidence and more extensive detailing of evidence than either approach alone. SCA-ML ranking was used to cull the large set of DVH metric candidates to a reduced set for input to a predictive ML model (PMLM), reducing risk of overfitting. The PMLM was iteratively constructed, minimizing the number of DVH metric inputs according to their ability to improve the model sensitivity. Seven swallowing structures producing 738 candidate DVH metrics were examined with the SCA-ML. Structures included superior constrictor muscle (SCM), inferior constrictor muscle (ICM), larynx (L), esophagus (E), ipsilateral and contra lateral parotids (IP, CP) and submandibular glands (ISG, CSG). Structures not contoured on at least 90% of the plans were noted for evidence but excluded from the predictive model. High SCA-ML scores were identified for SCM: D20%[EQD2Gy] ≤47.7, D25%[Gy]≤50.4, ISG: D35%[Gy]≤61.7 and E: D2cc [Gy]≤22.6.ICM:D55%[EQD2Gy]≤10.8 had a low SCA-ML score. High SCA-ML scores were obtained for L: D25%[Gy] ≤21.2 and CSG: D45%[Gy]≤28.8 but were excluded in the model due to lacking contours. Sensitivity (0.88 ±0.13) and AUC (0.74 ± 0.17) of the baseline PMLM was not significantly (p>0.05) different from the PMLM using SCM: D20%[EQD2Gy] and ISG: D35%[Gy], with SCM: D20%[EQD2Gy] being the dominant factor. Using the physical dose metric SCM: D25%[Gy] instead of the bio-corrected alternative did not significantly degrade the model. The SCA-ML based AI approach combined with the BDARS identified SCM:D25% [Gy] ≤50.4 as an additional mid priority metric, that together with historic constraints ICM:Mean [Gy] ≤20, L:Mean [Gy] ≤20, and SCM:Mean [Gy] ≤50 could improve outcomes. This study provided practical demonstration of how big data evidence combined with AI could advance iterative clinical learning paradigms.

Assessing spectral measures of post-harvest forest recovery with field plot data

Information regarding the nature and rate of forest recovery is required to inform forest management, monitoring, and reporting activities. Delayed establishment or return of forests has implications to harvest rotations and carbon uptake, among others, creating a need for spatially-explicit, large-area, characterizations of forest recovery. Landsat time series (LTS) has been demonstrated as a means to quantitatively relate forest recovery, noting that there are gaps in our understanding of the linkage between spectral measures of forest recovery and manifestations of forest structure and composition. Field plots provide a means to better understand the linkage between forest characteristics and spectral recovery indices. As such, from a large set of existing field plots, we considered the conditions present for the year in which the co-located pixel was considered spectrally recovered using the Years to Recovery (Y2R) metric. Y2R is a long-term metric of spectral recovery that indicates the number of years required for a pixel to return to 80% of its pre-disturbance Normalized Burn Ratio value. Absolute and relative metrics of recovery at 5 years post-disturbance were also considered. We used these three spectral recovery metrics to predict the stand development class assigned by the field crew for 284 seedling plots with an overall accuracy of 73.59%, with advanced seedling stands more accurately discriminated (omission error, OE = 15.74%) than young seedling stands (OE = 49.84%). We then used field-measured attributes (e.g. height, stem density, dominant species) from the seedling plots to classify the plots into three spectral recovery groups, which were defined using the Y2R metric: spectral recovery in (1) 1–5 years, (2) 6–10 years, or (3) 11–15 years. Overall accuracy for spectral recovery groups was 61.06%. Recovery groups 1 and 3 were discriminated with greater accuracy (producer’s and user’s accuracies > 66%) than recovery group 2 (< 50%). The top field-measured predictors of spectral recovery were mean height, dominant species, and percentage of stems in the plot that were deciduous. Variability in stand establishment and condition make it challenging to accurately discriminate among recovery rates within 10 years post-harvest. Our results indicate that the long-term metric Y2R relates to forest structure and composition attributes measured in the field and that spectral development post-disturbance corresponds with expectations of structural development, particularly height, for different species, site types, and deciduous abundance. These results confirm the utility of spectral recovery measures derived from LTS data to augment landscape-level assessments of post-disturbance recovery.

Development of metrics and an external benchmarking program for healthcare facilities

This paper describes the development of a benchmarking program for healthcare projects. Healthcare construction projects tend to be unique due to varied healthcare facilities and offered services, thus requiring a platform to study distinct project management approaches for the healthcare industry. The idea of continuous improvement through competitive benchmarking is already well established. However, highly targeted comparisons are not possible without industry-specific metrics. The ability to measure project performance allows for credible industry norms and continuous improvement. As a first step, a comprehensive study focused on identifying specific metrics to measure healthcare capital project-performance in key areas such as cost, schedule, change, and best practice implementation was concluded with input from a team comprised of healthcare owners, contractors, and architects. In addition, space/dimensional and program-specific metrics were also identified for this external benchmarking program. This paper presents the development of a framework for different healthcare capital facility-types using absolute and relative metrics for the healthcare industry. The findings include resources for the healthcare community in the form of healthcare facility metrics and project database that enables the process of quantifying the effectiveness and efficiency of processes specific to the healthcare industry of completed and ongoing projects.

Development of an artificial neural network based virtual sensing platform for the simultaneous prediction of emission-performance-stability parameters of a diesel engine operating in dual fuel mode with port injected methanol

The present work explores the potential of an artificial neural network platform to emulate the performance, emissions and stability indices of an existing single cylinder diesel engine operating in dual-fuel mode with methanol port injection under varying fuel injection pressure. This investigation is further augmented by hydrous methanol injection strategies. Brake power, fuel injection pressure, diesel specific fuel consumption, methanol specific fuel consumption, air flow rate, exhaust oxygen and temperature have been chosen as the model inputs while oxides of nitrogen, unburned hydrocarbon, carbon monoxide, carbon dioxide, soot have been chosen as the emission responses to be modelled along with equivalent brake specific fuel consumption as the performance response and coefficient of variance of indicated mean effective pressure as the stability parameter to be estimated. Absolute, relative and percentage-based statistical error metrics have been employed for model evaluation. The developed model shows an excellent agreement with the experimental data as evident from its extremely low normalized mean square error, symmetric mean absolute percentage error, Normalized root mean square error, mean squared relative error footprint coupled with high coefficient of determination which was observed to be within a range of 0.983–0.9999 and a corresponding Nash Sutcliffe coefficient of efficiency of 85%–99.6%. Furthermore, low Theil uncertainty evaluation and Kullback-Leibler Divergence values imparted a commendable credence of robustness to the estimation capability of the developed model. The present study manifests a computationally efficient and reliable virtual sensing platform to simultaneously emulate the emission-performance and stability parameters of a diesel-methanol partially premixed dual fuel operational paradigms in real time engine control strategies.

Efficiently Improving and Quantifying Robot Accuracy In Situ.

The advancement of simulation-assisted robot programming, automation of high-tolerance assembly operations, and improvement of real-world performance engender a need for positionally accurate robots. Despite tight machining tolerances, good mechanical design, and careful assembly, robotic arms typically exhibit average Cartesian positioning errors of several millimeters. Fortunately, the vast majority of this error can be removed in software by proper calibration of the so-called "zero-offsets" of a robot's joints. This research developed an automated, inexpensive, highly portable, in situ calibration method that fine tunes these kinematic parameters, thereby, improving a robot's average positioning accuracy four-fold throughout its workspace. In particular, a prospective low-cost motion capture system and a benchmark laser tracker were used as reference sensors for robot calibration. Bayesian inference produced optimized zero-offset parameters alongside their uncertainty for data from both reference sensors. Relative and absolute accuracy metrics were proposed and applied for quantifying robot positioning accuracy. Uncertainty analysis of a validated, probabilistic robot model quantified the absolute positioning accuracy throughout its entire workspace. Altogether, three measures of accuracy conclusively revealed multi-fold improvement in the positioning accuracy of the robotic arm. Bayesian inference on motion capture data yielded zero-offsets and accuracy calculations comparable to those derived from laser tracker data, ultimately proving this method's viability towards robot calibration.

Benchmarking low-cost inertial measurement units for indoor localisation and navigation of AGVs

In the last decade, the landscape of distribution and warehousing has drastically changed and is increasingly dominated by automated guided vehicles (AGVs) transporting palletised goods. The benefits that AGVs brought to the factory floor among which the decreasing labour costs, increasing safety, accuracy and productivity were also noticed by other sectors such as manufacturing, retail, e-commerce and, even military and healthcare. Nevertheless, the high initial investment and maintenance costs of these systems still remain bottlenecks for small and medium-sized enterprises (SMEs), slowing down their transition towards Industry 4.0. This demands for cost-effective AGVs equipped with low-cost exteroceptive and proprioceptive sensors for indoor navigation and localisation in highly dynamic environments. Therefore, this work presents the benchmarking of three low-cost and one medium-cost inertial measurement units (IMUs) characterised by an oft-recurring static analysis extended with a dynamic analysis. The former is based on the Overlapping Allan Variance (OAVAR) method and compares various sensor metrics such as white noise, bias offset and bias instability. The latter compares step and frequency response parameters of the IMUs, while being subjected to frequency ranges similar to those observed during AGV operation. In addition, these low and medium-cost IMUs are mounted on a mobile platform and applied in an indoor use case with a view to implement simultaneous localisation and mapping (SLAM) for AGVs in production sites. The influence of these IMUs on SLAM is investigated based on absolute and relative map metrics. Finally, it can be stated that with regard to AGV operation, the low-cost IMUs perform equally well compared to the medium-cost IMU.

Empirical cross sensor comparison of Sentinel-2A and 2B MSI, Landsat-8 OLI, and Landsat-7 ETM+ top of atmosphere spectral characteristics over the conterminous United States

Remote sensing landscape monitoring approaches frequently benefit from a dense time series of observations. To enhance these time series, data from multiple satellite systems need to be integrated. Landsat image data is a valuable 30-meter resolution source of spatial information to assess forest conditions over time. Together both operational Landsat satellites—7 and 8—provide a revisit frequency of 8 days at the equator. This moderate temporal frequency provides essential information to detect annual large area abrupt land cover changes. However, the ability to measure subtle and short lived intraseasonal changes is challenged by gaps in Landsat imagery at key points in time. The first Sentinel-2 satellite mission was launched by the European Space Agency in 2015. This moderate resolution data stream provides an opportunity to supplement the Landsat data record. The objective of this study is to assess the potential for integrating top of atmosphere Landsat and Sentinel 2 image data archived in the Google Earth Engine compute environment. In this paper we assess absolute and proportional differences in near-contemporaneous observations for six bands with comparable spectral response functions and spatial resolution between the Sentinel-2 Multi Spectral Instrument and Landsat Operational Land Imager and Enhanced Thematic Mapper Plus imagery. We assessed differences using absolute difference metrics and major axis linear regression between over 10,000 image pairs across the conterminous United States and present cross sensor transformation models. Major axis linear regression results indicate that Sentinel MSI data are as spectrally comparable to the two types of Landsat image data as the Landsat sensors are with each other. Root-mean-square deviation (RMSD) values ranging from 0.0121 to 0.0398 were obtained between MSI and Landsat spectral values, and RMSD values ranging from 0.0124 and 0.0372 were obtained between OLI and ETM+. Despite differences in their spatial, spectral, and temporal characteristics, integration of these datasets appears to be feasible through the application of bandwise linear regression corrections.

TASM: technocrat ARIMA and SVR model for workload prediction of web applications in cloud

Workload patterns of cloud applications are changing regularly. The workload prediction model is key for auto-scaling of resources in a cloud environment. It is helping with cost reduction and efficient resource utilization. The workload for the web applications is usually mixed for different application at different time span. The single prediction model is not able to predict different kinds of workload pattern of cloud applications. In this paper, an adaptive prediction model has been proposed using linear regression, ARIMA, and support vector regression for web applications. Workload classifier has been proposed to select the model as per workload features. Further the model parameters are selected through a heuristic approach. We have used real trace files to evaluate the proposed model with existing state-of-the-art models. The experiment results describe the significant improvement in root-mean-squared error and mean absolute percentage error metrics, and improve the quality of service of web applications in a cloud environment.

Temporal Trends in Absolute and Relative Extreme Temperature Events Across North America

AbstractIn this research, we define extreme temperature events using a recently defined excess heat factor, based on the exceedance of apparent temperature beyond the 95th percentile along with an acclimatization factor, to define extreme heat events (EHE). We extend the calculation to assess cold and develop relative metrics to complement the absolute metrics, where extremeness is based on conditions relative to season. We thus examine extreme cold events (ECE), relative extreme heat events, and relative extreme cold events in addition to EHE. We present a climatology of these variables for North America, followed by analyses of trends from 1980 to 2016. While EHE and ECE are found in the core of summer and winter, respectively, relative events tend to have a broader seasonality. Trends in relative extreme heat events and EHE are upward, and relative extreme cold events and ECE are downward; the relative events are changing more rapidly than the absolute events.

A discursive question: Supporting student‐authored multiple choice questions through peer‐learning software in non‐STEMM disciplines

AbstractPeer‐learning that engages students in multiple choice question (MCQ) formulation promotes higher task engagement and deeper learning than simply answering MCQ’s in summative assessment. Yet presently, the literature detailing deployments of student‐authored MCQ software is biased towards accounts from Science, Technology, Engineering, Maths and Medicine (STEMM) subjects, rather than discursive subjects or disciplines where content may contain fewer absolute facts and objective metrics and more nuance. We report on qualitative and quantitative findings from a semester‐long deployment of a peer‐learning software package (PeerWise) in a 140‐student course on Interaction Design. PeerWise enables students to author, rate and comment upon their peers’ MCQ questions. The platform was enthusiastically adopted as a revision aid, yet overall question quality was poor and students expressed difficulty in translating the discursive nature of the course content into MCQs with only one correct answer. In addressing these shortcomings, this paper offers specific recommendations to instructors of more discursive subjects using student‐led MCQ authoring platforms, and further, how platforms such as PeerWise may be adapted to better suit disciplines characterised by discursive content. We propose alternative approaches to moderation and two suggestions for potential amendments to the software itself.

Reframing Future Risks of Extreme Heat in the United States.

The goal of this study is to reframe the analysis and discussion of extreme heat projections to improve communication of future extreme heat risks in the United States. We combine existing data from 31 of the Coupled Model Intercomparison Project Phase 5 models to examine future exposure to extreme heat for global average temperatures of 1.5, 2, 3, and 4 °C above a preindustrial baseline. We find that throughout the United States, historically rare extreme heat events become increasingly common in the future as global temperatures rise and that the depiction of exposure depends in large part on whether extreme heat is defined by absolute or relative metrics. For example, for a 4 °C global temperature rise, parts of the country may never see summertime temperatures in excess of 100 °F, but virtually all of the country is projected to experience more than 4 weeks per summer with temperatures exceeding their historical summertime maximum. All of the extreme temperature metrics we explored become more severe with increasing global average temperatures. However, a moderate climate scenario delays the impacts projected for a 3 °C world by almost a generation relative to the higher scenario and prevents the most extreme impacts projected for a 4 °C world.

A quantitative security metric model for security controls: Secure virtual machine migration protocol as target of assessment

Quantitative security metrics are desirable for measuring the performance of information security controls. Security metrics help to make functional and business decisions for improving the performance and cost of the security controls. However, defining enterprise-level security metrics has already been listed as one of the hard problems in the In-foSec Research Council's hard problems list. Almost all the efforts in defining absolute security metrics for the enterprise security have not been proved fruitful. At the same time, with the maturity of the security industry, there has been a continuous emphasis from the regulatory bodies on establishing measurable security metrics. This paper addresses this need and proposes a relative security metric model that derives three quantitative security metrics named Attack Resiliency Measure (ARM), Performance Improvement Factor (PIF), and Cost/Benefit Measure (CBM) for measuring the performance of the security controls. For the effectiveness evaluation of the proposed security metrics, we took the secure virtual machine (VM) migration protocol as the target of assessment. The virtualization technologies are rapidly changing the landscape of the computing world. Devising security metrics for virtualized environment is even more challenging. As secure virtual machine migration is an evolving area and no standard protocol is available specifically for secure VM migration. This paper took the secure virtual machine migration protocol as the target of assessment and applied the proposed relative security metric model for measuring the Attack Resiliency Measure, Performance Improvement Factor, and Cost/Benefit Measure of the secure VM migration protocol.

Enhancing the performance of distributed big data processing systems using Hadoop and Polybase

The approach to improvement of performance of distributed information systems based on sharing technologies of the Hadoop cluster and component of SQL Server PolyBase was considered. It was shown that the relevance of the problem, solved in the research, relates to the need for processing Big Data with different way of representation, in accordance with solving diverse problems of business projects. An analysis of methods and technologies of creation of hybrid data warehouses based on different data of SQL and NoSQL types was performed. It was shown that at present, the most common is the technology of Big Data processing with the use of Hadoop distributed computation environment. The existing technologies of organization and access to the data in the Hadoop cluster with SQL-like DBMS by using connectors were analyzed. The comparative quantitative estimates of using Hive and Sqoop connectors during exporting data to the Hadoop warehouse were presented. An analysis of special features of Big Data processing in the architecture of Hadoop-based distributed cluster computations was carried out. The features of Polybase technology as a component of SQL Server for organizing a bridge between SQL Server and Hadoop data of the SQL and NoSQL types were presented and described. The composition of the model computer plant based on the virtual machine for implementation of joint setting of PolyBase and Hadoop for solving test tasks was described. A methodological toolset for the installation and configuration of Hadoop and PolyBase SQL Server software was developed with consideration of constraints on computing capacities. Queries for using PolyBase and data warehouse Hadoop when processing Big Data were considered. To assess the performance of the system, absolute and relative metrics were proposed. For large volume of test data, the results of the experiments were presented and analyzed, which illustrated an increase in productivity of the distributed information system – query execution time and magnitude of memory capacity of temporary tables, created in this case. A comparative analysis of the studied technology with existing connectors with Hadoop cluster, which showed the advantage of PolyBase over connectors of Sqoop and Hive was performed. The results of the research could be used in the course of scientific and training experiments of organization when implementing the most modern IT-technologies.

Predictive Modelling of Training Loads and Injury in Australian Football

Abstract To investigate whether training load monitoring data could be used to predict injuries in elite Australian football players, data were collected from athletes over 3 seasons at an Australian football club. Loads were quantified using GPS devices, accelerometers and player perceived exertion ratings. Absolute and relative training load metrics were calculated for each player each day. Injury prediction models (regularised logistic regression, generalised estimating equations, random forests and support vector machines) were built for non-contact, non-contact time-loss and hamstring specific injuries using the first two seasons of data. Injury predictions were then generated for the third season and evaluated using the area under the receiver operator characteristic (AUC). Predictive performance was only marginally better than chance for models of non-contact and non-contact time-loss injuries (AUC&lt;0.65). The best performing model was a multivariate logistic regression for hamstring injuries (best AUC=0.76). Injury prediction models built using training load data from a single club showed poor ability to predict injuries when tested on previously unseen data, suggesting limited application as a daily decision tool for practitioners. Focusing the modelling approach on specific injury types and increasing the amount of training observations may improve predictive models for injury prevention

Ecosystem services in life cycle assessment - Part 2: Adaptations to regional and serviceshed information

Abstract Regionalized life cycle assessment (LCA) is receiving more attention among LCA practitioners due to spatial variation in process efficiency of technological systems and current status of ecological systems. However, the role of ecosystem services (ES) in supporting technological activities is still ignored. Techno-Ecological Synergies in Life Cycle Assessment (TES-LCA) is a methodology that captures the interactions between and within technological and ecological systems, along a product's life cycle. It accounts for local and absolute environmental sustainability by comparing the demand and supply of ES at multiple spatial scales. To facilitate its wider adoption, the basic computational framework has been proposed in Part 1, which includes technologies and ecosystems in an integrated manner. To handle the complications induced by explicitly considering ES, which operate within servicesheds at multiple spatial scales, the TES-LCA computational structure is modified to account for spatial variation in technological and ecological systems in Part 2. The regionalized TES-LCA framework is demonstrated through an expanded case study to show its capability to capture different scenarios of regionalization, including variation in process efficiency, ecological carrying capacity (CC), characterization factors (CF), and the scales at which ES operate. The approach is then proved to be general and able to subsume existing approaches, such as regionalized LCA, GIS-LCA and recent extensions based on normalizing CF by ecological CC to calculate absolute sustainability metrics. It is recommended that the developed computational structure should be implemented in LCA software with the functionality for handling geographical information. Also, the regionalized information about ES demand and supply needs to be made available in future versions of life cycle inventory databases.

Ecosystem Services in Life Cycle Assessment while Encouraging Techno‐Ecological Synergies

SummaryLife cycle assessment (LCA) has enabled consideration of environmental impacts beyond the narrow boundary of traditional engineering methods. This reduces the chance of shifting impacts outside the system boundary. However, sustainability also requires that supporting ecosystems are not adversely affected and remain capable of providing goods and services for supporting human activities. Conventional LCA does not account for this role of nature, and its metrics are best for comparing alternatives. These relative metrics do not provide information about absolute environmental sustainability, which requires comparison between the demand and supply of ecosystem services (ES). Techno‐ecological synergy (TES) is a framework to account for ES, and has been demonstrated by application to systems such as buildings and manufacturing activities that have narrow system boundaries.This article develops an approach for techno‐ecological synergy in life cycle assessment (TES‐LCA) by expanding the steps in conventional LCA to incorporate the demand and supply of ecosystem goods and services at multiple spatial scales. This enables calculation of absolute environmental sustainability metrics, and helps identify opportunities for improving a life cycle not just by reducing impacts, but also by restoring and protecting ecosystems. TES‐LCA of a biofuel life cycle demonstrates this approach by considering the ES of carbon sequestration, air quality regulation, and water provisioning. Results show that for the carbon sequestration ecosystem service, farming can be locally sustainable but unsustainable at the global or serviceshed scale. Air quality regulation is unsustainable at all scales, while water provisioning is sustainable at all scales for this study in the eastern part of the United States.

Relative deployment rates of renewable and nuclear power: A cautionary tale of two metrics

Which can more quickly displace fossil-fueled electricity generation—nuclear power or modern renewables? Contrary to a persistent myth based on erroneous methods, global data show that renewable electricity adds output and saves carbon faster than nuclear power does or ever has. However, some literature asserts the contrary, based on a peculiar per-capita metric—perhaps useful for comparing countries but not technologies —applied to selected countries while ignoring others with the opposite outcome. Further flaws include cherrypicked and incomplete data, restrictive redefinitions, inconsistent comparisons, and omitted institutional lead times and dry-hole risks. Careful dissection of the reasons for contradictory results (even within the same paper) from absolute and per-capita metrics of growth in carbon-free electricity generation reveals the need for care in calculating and assessing claims about which technologies can and do deploy most quickly.