Double Counting Research Articles

A Passenger Detection and Action Recognition System for Public Transport Vehicles

In the past few years, advances of autonomous driving technologies have made significant progress. One successful deployment with great potential is for the self-driving public transportation. Consequently, the safety of passengers under unmanned operations is an important issue for the future development. In this paper, we propose a vision system for the detection, counting and action recognition of passengers on board a bus. To have a complete coverage of the passenger area, a pair of cameras mounted on the ceiling is adopted for image acquisition. A new cross-camera CNN structure is proposed for passenger pose recognition and action classification. To deal with the double counting problem caused by the overlap of multiple viewpoints, a region prediction algorithm is developed for matching validation. The experiments carried out on a minibus have demonstrated the feasibility of our proposed techniques.

Quarkonium polarization in medium from open quantum systems and chromomagnetic correlators

We study the spin-dependent in-medium dynamics of quarkonia by using the potential nonrelativistic QCD (pNRQCD) and the open quantum system framework. We consider the pNRQCD Lagrangian valid up to the order rM0=r and r0M=1M in the double power counting. By considering the Markovian condition and applying the Wigner transformation upon the diagonal spin components of the quarkonium density matrix with the semiclassical expansion, we systematically derive the Boltzmann transport equation for quarkonia with polarization dependence in the quantum optical limit. Unlike the spin-independent collision terms governed by certain chromoelectric field correlators, new gauge invariant correlators of chromomagnetic fields determine the recombination and dissociation terms with polarization dependence at the order we are working. We also derive a Lindblad equation describing the in-medium transitions between spin-singlet and spin-triplet heavy quark-antiquark pairs in the quantum Brownian motion limit. The Lindblad equation is governed by new transport coefficients defined in terms of the chromomagnetic field correlators. Our formalism is generic and valid for both weakly coupled and strongly coupled quark gluon plasmas. It can be further applied to study spin alignment of vector quarkonia in heavy ion collisions. Published by the American Physical Society 2024

Developing a nationwide dataset of UK veterans seeking help from sector charities

Abstract Background The assistance to military veterans and their families in the UK is provided by both the NHS and over 1800 military charities. These charities count services using different definitions and reporting systems, so to date a national registry of service usage does not exist. The aim of the MONARCH study is to build a standardised registry of service usage data for the military charity sector, in order to identify patterns of use, gain insight on possible risk factors, and guide the allocation of resources. Methods Data is anonymised and a unique identifier is generated by adopting a Secure Hashing Algorithm, allowing both privacy protection and avoiding double counts. Data is standardised, and linked with an automated process to create an aggregated dataset. The dataset describes the population, using both a-priori and machine learning approaches (K-means clustering) to unveil different usage patterns. In addition, it will be linked to an online interactive dashboard. Results To date 5 national charities have shared data, for a total of 42,509 veterans with 128,423 needs. The mean age of beneficiaries was 60.1 years (SD 20.5), and 90% were male. 65% were receiving some other form of statutory benefit, 5% was homeless and 1% was imprisoned. 65% of the needs recorded concerned social wellbeing. 40% of veterans were helped at least in two different years. The k-means clustering approach based on the number of accesses, number of needs and repetition of need returned 4 subgroups of use that were identical to those created using a priori knowledge. Conclusions The dataset is the most comprehensive source of charity usage data in the UK to date. Service usage is generally homogenous among subgroups, but some differences were highlighted indicating that younger, non-officer veterans may be more at risk of presenting with more complex needs. These first useful insights can help allocate resources to build an effective preventive strategy for more complex cases. Key messages • The MONARCH dataset is the first comprehensive nationwide registry in the UK of military charity data. • The understanding of usage patterns can lead to the design of targeted preventive strategies.

On-Tree Mango Fruit Count Using Live Video- Split Image Dataset to Predict Better Yield at Pre-Harvesting Stage

This study introduces a method for fruit counting in agricultural settings using video capture and the YOLOv7 object detection model. By splitting captured videos into frames and strategically selecting representative frames, the approach aims to accurately estimate fruit counts while minimizing the risk of double counting. YOLOv7, known for its efficiency and accuracy in object detection, is employed to analyze selected frames and detect fruits on trees. Demonstrated the method's effectiveness through its ability to provide farmers with precise yield estimations, optimize resource management, and facilitate early detection of orchard issues such as pest infestations or nutrient deficiencies. This technological integration reduces labor costs and supports sustainable agricultural practices by improving productivity and decision-making capabilities. The scalability of the approach makes it suitable for diverse orchard sizes and types, offering a promising tool for enhancing agricultural efficiency and profitability. The researcher compared YOLOv5n, YOLOv5s, YOLOv7, and YOLOv7-tiny with eight-sided imaging techniques around the tree. The experimental results of YOLOv7 with the eight-sided technique performed best and achieved a count accuracy of 97.7% on a single tree in just 17.112 ms of average inference time. On multiple trees, it is 95.48% in just 17 ms of average inference time, with the help of an eight- sided method on tree images.

Exploring the effect of Guarantees of Origin on the decarbonization of corporate electricity procurement: A case study of Germany and Norway

AbstractIncreasing social, economic, and political pressure causes many companies to pledge to decarbonize. A common measure involves the use of Energy Attribute Certificates (EAC), such as the European Guarantees of Origin (GO), to reduce emissions from electricity procurement (Scope 2). However, previous studies find no effect on additional renewable energy capacity. Focusing on Norway and Germany as dominant contributors to net GO exports and imports, this study examines the GO trade alongside corporate carbon accounting data to answer the research question: Does the decarbonization of corporate electricity procurement using Guarantees of Origin contribute to the expansion of renewable electricity generation capacity in Norway and Germany? The analysis of CDP and Association of Issuing Bodies data reveals Norway's consumption mix is more carbon intensive than Germany's because Norway exports GO and imports fossil electricity attributes. German companies report predominantly market‐based approach, mainly using green tariffs and GO for zero emission claims, while Norwegian companies favor the location‐based approach. The largest share of GO issued in Norway comes from hydropower plants aged 41 to 70 years. The results highlight the urgency to revise corporate carbon accounting standards. GO lack additionality due to double counting of renewable attributes. Potential solutions include additionality criteria, GO trade restrictions based on physical capacities, and age limits for eligible power plants. This study's novelty is linking CDP data with GO trade, to assess the integrity of corporate decarbonization strategies. It contributes valuable insights to ongoing discourse on the role of EAC.

Single- and two-pollutant Concentration-Response Functions for PM2.5 and NO2 for Quantifying Mortality Burden in Health Impact Assessments

ObjectiveHealth Impact Assessments (HIAs) for air pollutant mixtures are challenging because risk estimates are primarily derived from single-pollutant models. Combining risk estimates from multiple pollutants requires new approaches, as a simple addition of single pollutant risk estimates from correlated air pollutants may result in double counting. We investigated approaches applying concentration-response functions (CRFs) from single- and two-pollutant models in HIAs, focusing on long-term exposure to particulate matter with a diameter less than 2.5 micrometers (PM2.5) and nitrogen dioxide (NO2) and their associations with all-cause mortality. MethodsA systematic literature search of MEDLINE and EMBASE identified cohort studies employing single- and two-pollutant models of long-term exposure to PM2.5 and NO2 with all-cause mortality. Pooled CRFs were calculated through random-effects meta-analyses of risk estimates from single- and two-pollutant models. Coefficient differences were calculated by comparing single- and two-pollutant model estimates. Four approaches to estimating population-attributable fractions (PAFs) were compared: PM2.5 or NO2 single-pollutant models to represent the mixture, the sum of single-pollutant models, the sum of two-pollutant models and the sum of single-pollutant models from a larger body of evidence adjusted by coefficient difference. ResultsSeventeen papers reported both single and two-pollutant estimates. Pooled hazard ratios (HRs) for mortality from single- and two-pollutant models were 1.053 (95% confidence interval: 1.034-1.071) and 1.035 (1.014-1.057), respectively, for a 5 μg/m3 increase in PM2.5. HRs for a 10 μg/m3 increase in NO2 were 1.032 (1.014-1.049) and 1.024 (1.000-1.049) for single- and two-pollutant models, respectively. The average coefficient difference between single- and two-pollutant models was 0.017 for PM2.5 and 0.007 for NO2. Combined PAFs for the PM2.5-NO2 mixture using joint HRs from single- and two-pollutant model CRFs were 0.09 and 0.06, respectively. ConclusionUtilizing CRFs from two-pollutant models or applying the coefficient difference to a more extensive evidence base seems to mitigate the potential overestimation of mixture health impacts from adding single-pollutant CRFs.

GPU parallel implementation of a finite volume lattice Boltzmann method for incompressible flows

This work presents a graphics processing units (GPU) parallel algorithm of a cell-centered finite volume lattice Boltzmann method (FVLBM) on unstructured meshes. In the present GPU parallel algorithm, the parallelization is performed in the physical space. To reduce the frequency of GPU memory accesses, this algorithm develops coalesced access to GPU memory. In addition, to avoid the race for resources leading to data anomalies, such as dirty read or phantom read etc., and the double counting for flux calculation, the efficient face-based data structure often used for flux calculation in cells in the central processing unit (CPU) version of FVLBM is modified into a face-based data structure used for the fluxes on all faces, followed by a cell-based loop for the final residuals in all cells. Therefore, the proposed GPU parallel algorithm does not need to use the resource lock and retains the high efficiency of the face-based data structure in the fluxes computation to enhance its’ parallel efficiency. Additionally, to demonstrate the computational efficiency of the proposed GPU parallel algorithm, various benchmark studies are performed in this work by the proposed parallel scheme on a double precision NVIDIA GeForce RTX 3090Ti GPU card, including (a) the lid-driven flow in a two-dimensional (2D) square cavity, (b) a 2D flow past a cylinder, and (c) the lid-driven flow in a three-dimensional (3D) cubic cavity. The numerical results show that the proposed GPU parallel algorithm can be as accurate as the original CPU serial scheme with 1 to 2 orders of speedup.

A review of current analytical methods for the determination of dietary fiber in food

Fiber benefits health and is an essential requirement in the body's daily diet. The definition of fiber has been evolving over the past 70 years. Initially, the definition of fiber focused only on plant cell walls (cellulose, hemicellulose, pectin, cutin, glycoprotein) and intracellular storage and secretion substances (gum, mucilage). More recently, the world has focused on resistant starch and oligosaccharides. The CODEX definition of dietary fiber includes all non-digestible carbohydrate polymers with a degree of polymerization of 3 or higher, provided that they benefit health. Fiber has many components, requiring the development of different methods to analyze components such as fructo-oligosaccharide, galacto-oligosaccharide, polydextrose, resistant maltodextrin, resistant starch, etc. A series of AOAC methods have been developed to explore different types of fiber, and these methods are still valid and widely used. For the analysis of total fiber content, the enzymemass method according to Prosky (AOAC 985.29) was considered the gold standard when it was first published. However, this method only determines a part of the fiber. When adding up the specific fiber components calculated from different methods, some types of fiber are double counting. Therefore, the method for determining total fiber is constantly updated and revised. The Enzymatic-Gravimetric-Liquid Chromatography method has been developed and can fully determine the components of dietary fiber; the newly published updated version is AOAC 2022.01.

Shrimp Larvae Counting Based on Improved YOLOv5 Model with Regional Segmentation

Counting shrimp larvae is an essential part of shrimp farming. Due to their tiny size and high density, this task is exceedingly difficult. Thus, we introduce an algorithm for counting densely packed shrimp larvae utilizing an enhanced You Only Look Once version 5 (YOLOv5) model through a regional segmentation approach. First, the C2f and convolutional block attention modules are used to improve the capabilities of YOLOv5 in recognizing the small shrimp. Moreover, employing a regional segmentation technique can decrease the receptive field area, thereby enhancing the shrimp counter’s detection performance. Finally, a strategy for stitching and deduplication is implemented to tackle the problem of double counting across various segments. The findings from the experiments indicate that the suggested algorithm surpasses several other shrimp counting techniques in terms of accuracy. Notably, for high-density shrimp larvae in large quantities, this algorithm attained an accuracy exceeding 98%.

Developing a nationwide registry of UK veterans seeking help from sector charities—a machine learning approach to stratification

Abstract The assistance to veterans in the UK is provided by the National Health Service and over 1800 military charities. These charities count services using different definitions and reporting systems, so to date a national registry of service usage does not exist. The aim of the Map Of Need Aggregation ResearCH study is to build a standardized registry of service usage data for the military charity sector. Data are completely anonymized by adopting a Secure Hashing Algorithm. A unique anonymous identifier is generated allowing both privacy protection and avoiding double counts. Data are standardized and linked with an automated process to create an aggregated dataset. The dataset describes the population, using both a priori and machine learning approaches. To date a total of 42 509 veterans with 128 423 needs are included. The mean age was 60.1 years, and 90% were male. 65% were receiving other benefits, 5% were homeless and 1% were in prison. 65% of the needs recorded concerned social wellbeing. 40% of veterans received assistance in at least two different years. The k-means clustering approach returned 4 subgroups of use that were identical to those created using a priori knowledge. The dataset is the most comprehensive source of veteran charity usage data in the UK to date. Service usage is generally homogenous among subgroups, but some differences were highlighted indicating that younger, non-officer veterans may be more at risk of presenting with more complex needs. These first useful insights can help allocate resources to build an effective preventive strategy for more complex cases.

Wild bird mass mortalities in eastern Canada associated with the Highly Pathogenic Avian Influenza A(H5N1) virus, 2022

AbstractIn 2022, a severe outbreak of disease caused by clade 2.3.4.4b Highly Pathogenic Avian Influenza (HPAI) H5N1 virus resulted in unprecedented mortality among wild birds in eastern Canada. Tens of thousands of birds were reported sick or dead, prompting a comprehensive assessment of mortality spanning the breeding season between April 1 and September 30, 2022. Mortality reports were collated from federal, Indigenous, provincial, and municipal agencies, the Canadian Wildlife Health Cooperative, and other nongovernmental organizations, universities, and citizen science platforms. A scenario analysis was conducted to refine mortality estimates, accounting for potential double counts from multiple sources under a range of spatial and temporal overlaps. Correcting for double counting, HPAI is estimated to have caused 40,391 wild bird mortalities in eastern Canada during the spring and summer of 2022; however, this figure underestimates total mortality as it excludes unreported deaths on land and at sea. Seabirds and sea ducks, long‐lived species that are slow to recover from perturbations, accounted for 98.7% of estimated mortalities. Our study provides estimates of bird mortality, with Northern Gannets (Morus bassanus; 25,669), Common Murres (Uria aalge; 8133), and American Common Eiders (Somateria mollissima dresseri; 1894) exhibiting the highest mortality figures. We then compare these mortality estimates with recent population estimates and trends and make an initial assessment of whether biologically meaningful population‐level impacts are possible. Specifically, we focus on the Northern Gannet, a species that has suffered significant global mortality, and two harvested species, Common Murre and American Common Eider, to inform management decisions. Our analysis suggests population‐level impacts in eastern Canada are possible for Northern Gannets and American Common Eiders, but are unlikely for Common Murres. This study demonstrates a comprehensive approach to assessing mortality and underscores the urgent need for further research to understand the broader ecological ramifications of the HPAI outbreak on wild bird populations.

Characteristics and Outcomes of Infants in Texas by Facility Children's Surgery Verification Status

IntroductionThe American College of Surgeons Children's Surgery Verification (CSV) status recognizes hospitals that deliver high-quality pediatric surgical care. Texas has 5 CSV centers in three cities, which may limit equitable access to care. We explored the characteristics and outcomes of infants admitted in Texas as a function of facility CSV status. Materials and methodsWe retrospectively reviewed a state-wide hospital discharge database (2013-2021). All patients <1 y of age were included. Patients transferred to an outside hospital were excluded to avoid double counting. Descriptive statistics and chi-square analysis were performed. ResultsWe analyzed 3,617,173 admissions, with 211,278 (6%) treated at CSV centers. CSV admissions were less likely to be inborn (46% versus 93%) and more likely to be transfers (16% versus 1%). CSV centers also had sicker patients (32% versus 13% extreme illness severity) with higher mortality rates (1% versus 0%), longer length of stay (9 ± 22 versus 4 ± 9), and higher operative rates (33% versus 20%). However, mortality was lower at CSV centers when matched for illness severity. Proportionately more patients from rural counties (9% versus 4%) and counties along the United States-Mexico border (13% versus 1%), as well as patients of Hispanic ethnicity (39% versus 33%), were treated at non-CSV centers. Meanwhile, proportionately more African Americans (21% versus 11%) were treated at CSV centers. ConclusionsCSV centers are associated with improved outcomes among patients with high illness severity. Population differences among patients treated at CSV centers compared to non-CSV centers may represent disparities in access to care and warrant further evaluation.

One Turán Type Problem on Uniform Hypergraphs

Let n,m,p,r∈N with p≥n≥r. For a hypergraph, if each edge has r vertices, then the hypergraph is called an r-graph. Define er(n,m;p) to be the maximum number of edges of an r-graph with p vertices in which every subgraph of n vertices has at most m edges. Researching this function constitutes a Turán type problem. In this paper, on the one hand, for fixed p, we present some results about the exact values of er(n,m;p) for small m compared to n; on the other hand, for sufficient large p, we use the combinatorial technique of double counting to give an upper bound of e(n,m;p) and obtain a lower bound of er(n,m;p) by applying the lower bound of the independent set of a hypergraph.

A note on Tesla's revised safety report crash rates

Between June 2018 and December 2023, Tesla released quarterly safety reports citing average miles between crashes for Tesla vehicles. Prior to March 2021, crash rates were categorized as (1) with their SAE Level 2 automated driving system Autopilot engaged, (2) without Autopilot but with active safety features such as automatic emergency braking, and (3) without Autopilot and without active safety features. In January 2023, Tesla revised past reports to reflect their new categories of with and without Autopilot engaged, in addition to making small adjustments based on recently discovered double counting of reports and excluding previously recorded crashes that did not meet their thresholds of airbag or active safety restraint activation. The revisions are heavily biased towards no-active-safety-features—a surprising result given prior research showing that drivers predominantly keep most active safety features enabled. As Tesla's safety reports represent the only national source of Level 2 advanced driver assistance system crash rates, clarification of their methods is essential for researchers and regulators. This note describes the changes and considers possible explanations for the discrepancies.

Urban Roadway in America: The Amount, Extent, and Value

Problem, research strategy, and findings We predicted the amount, share, and value of land dedicated to roadways within and across 316 U.S. primary metropolitan statistical areas. Despite the amount and value of land dedicated to roadways, our study provides the first such estimate across a broad range of metropolitan areas. Our basic approach was to estimate roadway widths using a 10% sample of widths provided by the Highway Performance Monitoring System and apply our estimates to the rest of the roadway system. Multiplying estimated widths by segment length and netting out double counting at intersections provided estimates of land area. We also matched roadway segments and areas to existing land value estimates and satellite-based measures of urbanized land. We found that a little less than a quarter of urbanized land—roughly the size of West Virginia—was dedicated to roadway. This land was worth around $4.1 trillion in 2016 and had an annualized value that was higher than the total variable costs of the trucking sector and the total annual federal, state, and local expenditures on roadways. Conducting a back-of-the-envelope cost–benefit analysis, we found that the country likely has too much land dedicated to urban roads. Takeaway for practice Federal, state, and local agencies dedicate substantial time, money, and resources to providing roadways. Even with relatively generous assumptions and no external costs from driving, however, we estimated that the average cost of expanding roadways exceeded the benefits by a factor of nearly three when accounting for land value. Policymakers should question policies focused on roadway expansion and consider options to reduce the amount of space dedicated to roadway in favor of more housing, offices, and other land uses. In addition to our findings, we provide a novel data set that academics and policymakers can use to draw their own conclusions about the state of America’s urban roadways.

Accounting for dependencies among performance shaping factors in SPAR-H using a regularized autoencoder and WINGS-AISM

The standardized plant analysis risk human reliability analysis (SPAR-H) method is widely used for human reliability analysis to adjust the nominal human error probability (HEP) by assigning different multipliers to the performance shaping factors (PSFs). Nevertheless, SPAR-H suffers from assuming PSFs to be independent without considering any overlaps and dependencies. Therefore, this study introduces a new systematic method to analyze the relationships among the PSFs in SPAR-H qualitatively and quantitatively to obtain more reasonable HEP estimation results. The proposed method comprises three primary aspects: 1) a regularized autoencoder for the denoising and feature extraction of expert evaluation results, 2) the weighted influence non-linear gauge system-based adversarial interpretive structure modeling (WINGS-AISM) method to analyze the relationships among the PSFs and construct their causal hierarchy, and 3) a new relative weighting system to modify the PSF multipliers based on this hierarchy. The results of experiments comparing the proposed method with conventional methods highlight that our method effectively reduces the double counting of overlapping PSFs in SPAR-H, providing more reasonable and accurate HEP estimation results.

The effect of fat model variation on muscle fat fraction quantification in a cross-sectional cohort.

Spectroscopic imaging, rooted in Dixon's two-echo spin sequence to distinguish water and fat, has evolved significantly in acquisition and processing. Yet precise fat quantification remains a persistent challenge in ongoing research. With adequate phase characterization and correction, the fat composition models will impact measurements of fatty tissue. However, the effect of the used fat model in low-fat regions such as healthy muscle is unknown. In this study, we investigate the effect of assumed fat composition, in terms of chain length and double bond count, on fat fraction quantification in healthy muscle, while addressing phase and relaxometry confounders. For this purpose, we acquired bilateral thigh datasets from 38 healthy volunteers. Fat fractions were estimated using the IDEAL algorithm employing three different fat models fitted with and without the initial phase constrained. After data processing and model fitting, we used a convolutional neural net to automatically segment all thigh muscles and subcutaneous fat to evaluate the fitted parameters. The fat composition was compared with those reported in the literature. Overall, all the observed estimated fat composition values fall within the range of previously reported fatty acid composition based on gas chromatography measurements. All methods and models revealed different estimates of the muscle fat fractions in various evaluated muscle groups. Lateral differences changed from 0.5% to 5.3% in the hamstring muscle groups depending on the chosen method. The lowest observed left-right differences in each muscle group were all for the fat model estimating the number of double bonds with the initial phase unconstrained. With this model, the left-right differences were 0.64% ± 0.31%, 0.50% ± 0.27%, and 0.50% ± 0.40% for the quadriceps, hamstrings, and adductors muscle groups, respectively. Our findings suggest that a fat model estimating double bond numbers while allowing separate phases for each chemical species, given some assumptions, yields the best fat fraction estimate for our dataset.

Adaptive stacked species distribution modelling: Novel approaches to large scale quantification of blue carbon to support marine management

This study introduces a novel concept of ‘Adaptively Stacked’ Species Distribution Models (AS-SDMs) to predict blue carbon habitat distribution, abundance, carbon stocks, and carbon sequestration potential in Orkney. AS-SDMs are built from Weighted Boosted Regression Trees (WBRTs) that adaptively stack blue carbon sediment thickness, sediment carbon content, and sequestration potential to predicted abundance. A novel method to describe substrate types by relative inputs of mud, sand, and gravel is detailed that better characterises the determining factors of seagrass, maerl, and horse mussel abundance. This study also introduces a novel use of indexes to mitigate double counting issues of mixed species distribution models.Seagrass, maerl, horse mussel, and mixed seagrass and maerl (SGM) habitats are estimated to cover a maximum area of 657 km2 in Orkney, have a total sediment carbon stock of 16 Mt. C, and sequester 6000 t C yr−1. Applying a conservative threshold of 50 % abundance to habitat predictions, six key potential areas of blue carbon offset projects are identified. These areas cover just over 9 km2, have a total carbon stock of 330,000 t C, and sequester 330 t C yr−1. When applied to UK carbon credit value, assuming integration with voluntary markets and compliance with accreditation criteria, the habitats in these areas have a potential value of £24.5 million. If applied as annual values, these areas have carbon stocks with a potential value of £0.93 million yr−1 and a carbon sequestration potential value of £24,000 yr−1.

Trade-offs in biomethane production by substrate mixture optimization under German market conditions

BackgroundNew regulations and market conditions in Germany affect the profitability of biomethane upgrading as a repowering option for existing biogas plants following on-site CHP utilization. These conditions present trade-off challenges between higher sustainability requirements, maintaining production capacity and new revenue opportunities. Optimization methods, such as linear programming (LP), are essential for determining the ideal substrate mixture and profitable solutions amidst multiple market conditions, plant-specific process constraints, and substrate properties.MethodsWe updated a substrate mixture optimization model within an assessment framework for the repowering of existing biogas plants (BGPs), which focuses on the operator’s perspective. By integrating multiple German biomethane markets for various BGPs, we assessed changes in the substrate mixture, GHG emissions, contribution margins, and constraint parameters to derive conclusions for operators and future framework design.ResultsIntegrating market revenues and constraints can increase contribution margins by 12–55%. Additional gains can be achieved by considering multiple markets simultaneously but limited to a few BGPs. The plant-specific LP solution space and used benchmark market are decisive. The former limits the potential of high substrate-specific contribution margins, which has a significantly higher impact than the relation between plant-specific characteristics and process constraints. The advanced fuel market is currently the lead market for biomethane, incentivizing GHG-emission extensive substrates, decreasing gas production and GHG emissions but increasing levelized cost of energy (LCOE) and partially CO2 abatement costs.ConclusionsThe key to improve profitability and to supply an increasing biomethane demand while fulfilling new requirements is a large LP solution space. Increasing market options, substrate availability, and digestion system capacity achieve this on the operator’s side. Policy makers could reduce normative requirements such as the maize cap or double counting of advanced fuels and favor high but uniform GHG requirements. Operators can prepare robustly for the future substrate mixture by adding digester volume and pre-treatment tech, ensuring long-term and diverse substrate availability, and contracts with flexible components. Although current market conditions can improve specific GHG emissions, they do not necessarily increase manure usage when other options, such as straw, are viable. Other regulatory support systems will be required to do so.

The greenhouse gas (GHG) reduction potential of ICT: A critical review of telecommunication companies’ GHG enablement assessments

AbstractFor about a decade, telecommunication network operators (TNOs) have explored the potential greenhouse gas (GHG) reductions their customers can achieve by using TNO services (e.g., by substituting physical travel with video conferencing), the so‐called GHG enablement. Some TNOs also calculate a GHG enablement factor, which is the ratio between the GHG enablement and their own GHG footprint. Since GHG enablements usually exceed the footprint, they create the narrative that TNOs contribute to GHG reductions across society. In this paper, we systematically analyze TNO GHG enablement claims and the underlying methodological approaches. We find several methodological shortcomings and inconsistencies, such as different sets of TNO services considered, inconsistent system boundaries, potential double counting of GHG reductions, and a disregard for rebound effects. Most importantly, TNO assessments focus exclusively on those services likely to yield GHG reductions, neglecting possible GHG‐increasing services. We conclude that current GHG enablement (factors) do not accurately and comprehensively represent TNOs’ overall GHG impacts and create a flawed picture. To provide a reliable decision basis to stakeholders such as TNOs themselves, customers, investors, and policymakers, we provide eight recommendations on how to substantially improve the methodological basis.