Granular Scale Research Articles

Surveilling the gamers: Privacy impacts of the video game industry

With many million users across all age groups and income levels, video games have become the world’s leading entertainment industry. Behind the fun experience they provide, it goes largely unnoticed that modern game devices pose a serious threat to consumer privacy. To illustrate the industry’s potential for illegitimate surveillance and user profiling, this article offers a classification of data types commonly gathered by video games. Drawing from patents and literature of diverse disciplines, it is also discussed how patterns and correlations in collected gameplay data may leak additional information in ways not easily understood or anticipated by the user. This includes inferences about a user’s biometric identity, age, gender, emotions, skills, interests, consumption habits, and personality traits. Based on these findings, it is argued that video games need to be brought into the focus of privacy research and discourse. Considering the granularity and enormous scale of the data collection taking place, this industry deserves the same level of scrutiny as other digital services, such as search engines, dating apps, or social media platforms. The knowledge compiled in this article can serve as a basis for privacy impact assessments, consumer education, and further research into the societal impact of video games.

Association of Optical Coherence Tomography–Measured Fibrovascular Ridge Thickness and Clinical Disease Stage in Retinopathy of Prematurity

Accurate diagnosis of retinopathy of prematurity (ROP) is essential to provide timely treatment and reduce the risk of blindness. However, the components of an ROP examination are subjective and qualitative. To evaluate whether optical coherence tomography (OCT)-derived retinal thickness measurements at the vascular-avascular junction are associated with clinical diagnosis of ROP stage. This cross-sectional longitudinal study compared OCT-based ridge thickness calculated from OCT B-scans by a masked examiner to the clinical diagnosis of 2 masked examiners using both traditional stage classifications and a more granular continuous scale at the neonatal intensive care unit (NICU) of Oregon Health & Science University (OHSU) Hospital. Infants who met ROP screening criteria in the OHSU NICU between June 2021 and April 2022 and had guardian consent were included. One OCT volume and en face image per patient per eye showing at least 1 to 2 clock hours of ridge were included in the final analysis. Comparison of OCT-derived ridge thickness to the clinical diagnosis of ROP stage using an ordinal and continuous scale. Repeatability was assessed using 20 repeated examinations from the same visit and compared using intraclass correlation coefficient (ICC) and coefficient of variation (CV). Comparison of ridge thickness with ordinal categories was performed using generalized estimating equations and with continuous stage using Spearman correlation. A total of 128 separate OCT eye examinations from 50 eyes of 25 patients were analyzed. The ICC was 0.87 with a CV of 7.0%. Higher ordinal disease classification was associated with higher axial ridge thickness on OCT, with mean (SD) thickness measurements of 264.2 (11.2) μm (P < .001), 334.2 (11.4) μm (P < .001), and 495.0 (32.2) μm (P < .001) for stages 1, 2, and 3, respectively and with continuous stage labels (ρ = 0.739, P < .001). These results suggest that OCT-based quantification of peripheral stage in ROP may be an objective and quantitative biomarker that may be useful for clinical diagnosis and longitudinal monitoring and may have implications for disease classification in the future.

Weather stressors correlate with Escherichia coli and Salmonella enterica persister formation rates in the phyllosphere: a mathematical modeling study

Enteric pathogens can enter a persister state in which they survive exposure to antibiotics and physicochemical stresses. Subpopulations of such phenotypic dormant variants have been detected in vivo and in planta in the laboratory, but their formation in the natural environment remains largely unexplored. We applied a mathematical model predicting the switch rate to persister cell in the phyllosphere to identify weather-related stressors associated with E. coli and S. enterica persister formation on plants based on their population dynamics in published field studies from the USA and Spain. Model outputs accurately depicted the bi-phasic decay of bacterial population sizes measured in the lettuce and spinach phyllosphere in these studies. Predicted E. coli persister switch rate on leaves was positively and negatively correlated with solar radiation intensity and wind velocity, respectively. Likewise, predicted S. enterica persister switch rate correlated positively with solar radiation intensity; however, a negative correlation was observed with air temperature, relative humidity, and dew point, factors involved in water deposition onto the phylloplane. These findings suggest that specific environmental factors may enrich for dormant bacterial cells on plants. Our model quantifiably links persister cell subpopulations in the plant habitat with broader physical conditions, spanning processes at different granular scales.

Human activity and mobility data reveal disparities in exposure risk reduction indicators among socially vulnerable populations during COVID-19 for five U.S. metropolitan cities

Non-pharmacologic interventions (NPIs) promote protective actions to lessen exposure risk to COVID-19 by reducing mobility patterns. However, there is a limited understanding of the underlying mechanisms associated with reducing mobility patterns especially for socially vulnerable populations. The research examines two datasets at a granular scale for five urban locations. Through exploratory analysis of networks, statistics, and spatial clustering, the research extensively investigates the exposure risk reduction after the implementation of NPIs to socially vulnerable populations, specifically lower income and non-white populations. The mobility dataset tracks population movement across ZIP codes for an origin–destination (O–D) network analysis. The population activity dataset uses the visits from census block groups (cbg) to points-of-interest (POIs) for network analysis of population-facilities interactions. The mobility dataset originates from a collaboration with StreetLight Data, a company focusing on transportation analytics, whereas the population activity dataset originates from a collaboration with SafeGraph, a company focusing on POI data. Both datasets indicated that low-income and non-white populations faced higher exposure risk. These findings can assist emergency planners and public health officials in comprehending how different populations are able to implement protective actions and it can inform more equitable and data-driven NPI policies for future epidemics.

Developing a granular scale environmental burden index (EBI) for diverse land cover types across the contiguous United States.

Critical to identifying the risk of environmentally driven disease is an understanding of the cumulative impact of environmental conditions on human health. Here we describe the methodology used to develop an environmental burden index (EBI). The EBI is calculated at U.S. census tract level, a finer scale than many similar national-level tools. EBI scores are also stratified by tract land cover type as per the National Land Cover Database (NLCD), controlling for urbanicity. The EBI was developed over the course of four stages: 1) literature review to identify potential indicators, 2) data source acquisition and indicator variable construction, 3) index creation, and 4) stratification by land cover type. For each potential indicator, data sources were assessed for completeness, update frequency, and availability. These indicators were: (1) particulate matter (PM2.5), (2) ozone, (3) Superfund National Priority List (NPL) locations, (4) Toxics Release Inventory (TRI) facilities, (5) Treatment, Storage, and Disposal (TSD) facilities, (6) recreational parks, (7) railways, (8) highways, (9) airports, and (10) impaired water sources. Indicators were statistically normalized and checked for collinearity. For each indicator, we computed and summed percentile ranking scores to create an overall ranking for each tract. Tracts having the same plurality of land cover type form a 'peer' group. We re-ranked the tracts into percentiles within each peer group for each indicator. The percentile scores were combined for each tract to obtain a stratified EBI. A higher score reveals a tract with increased environmental burden relative to other tracts of the same peer group. We compared our results to those of related indices, finding good convergent validity between the overall EBI and CalEnviroScreen 4.0. The EBI has many potential applications for research and use as a tool to develop public health interventions at a granular scale.

Data-Driven Clustering of Functional Signals Reveals Gradients in Processing Both within the Anterior Hippocampus and across Its Long Axis.

A particularly elusive puzzle concerning the hippocampus is how the structural differences along its long anteroposterior axis might beget meaningful functional differences, particularly in terms of the granularity of information processing. One measure posits to quantify this granularity by calculating the average statistical independence of the BOLD signal across neighboring voxels, or intervoxel similarity (IVS), and has shown the anterior hippocampus to process coarser-grained information than the posterior hippocampus. This measure, however, has yielded opposing results in studies of developmental and healthy aging samples, which also varied in fMRI acquisition parameters and hippocampal parcellation methods. To reconcile these findings, we measured IVS across two separate resting-state fMRI acquisitions and compared the results across many of the most widely used parcellation methods in a large young-adult sample of male and female humans (Acquisition 1, N = 233; Acquisition 2, N = 176). Finding conflicting results across acquisitions and parcellations, we reasoned that a data-driven approach to hippocampal parcellation is necessary. To this end, we implemented a group masked independent components analysis to identify functional subunits of the hippocampus, most notably separating the anterior hippocampus into separate anterior-medial, anterior-lateral, and posteroanterior-lateral components. Measuring IVS across these components revealed a decrease in IVS along the medial-lateral axis of the anterior hippocampus but an increase from anterior to posterior. We conclude that intervoxel similarity is deeply affected by parcellation and that grounding one's parcellation in a functionally informed approach might allow for a more complex and reliable characterization of the hippocampus.SIGNIFICANCE STATEMENT Processing information along hierarchical scales of granularity is critical for many of the feats of cognition considered most human. Recently, the changes in structure, cortical connectivity, and apparent functional properties across parcels of the hippocampal long axis have been hypothesized to underlie this hierarchical gradient in information processing. We show here, however, that the choice of parcellation method itself drastically affects one particular measure of granularity across the hippocampus and that a functionally informed approach to parcellation reveals gradients both within the anterior hippocampus and in nonlinear form across the long axis. These results point to the issue of parcellation as a critical one in the study of the hippocampus and reorient interpretation of existing results.

Acute climate risks in the financial system: examining the utility of climate model projections

Efforts to assess risks to the financial system associated with climate change are growing. These commonly combine the use of integrated assessment models to obtain possible changes in global mean temperature (GMT) and then use coupled climate models to map those changes onto finer spatial scales to estimate changes in other variables. Other methods use data mined from ‘ensembles of opportunity’ such as the Coupled Model Intercomparison Project (CMIP). Several challenges with current approaches have been identified. Here, we focus on demonstrating the issues inherent in applying global ‘top-down’ climate scenarios to explore financial risks at geographical scales of relevance to financial institutions (e.g. city-scale). We use data mined from the CMIP to determine the degree to which estimates of GMT can be used to estimate changes in the annual extremes of temperature and rainfall, two compound events (heatwaves and drought, and extreme rain and strong winds), and whether the emission scenario provides insights into the change in the 20, 50 and 100 year return values for temperature and rainfall. We show that GMT provides little insight on how acute risks likely material to the financial sector (‘material extremes’) will change at a city-scale. We conclude that ‘top-down’ approaches are likely to be flawed when applied at a granular scale, and that there are risks in employing the approaches used by, for example, the Network of Central Banks and Supervisors for Greening the Financial System. Most fundamental, uncertainty associated with projections of future climate extremes must be propagated through to estimating risk. We strongly encourage a review of existing top-down approaches before they develop into de facto standards and note that existing approaches that use a ‘bottom-up’ strategy (e.g. catastrophe modelling and storylines) are more likely to enable a robust assessment of material risk.

A 2D Model for Coronal Bright Points: Association with Spicules, UV Bursts, Surges, and EUV Coronal Jets

Coronal bright points (CBPs) are ubiquitous structures in the solar atmosphere composed of hot small-scale loops observed in extreme-ultraviolet (EUV) or X-rays in the quiet Sun and coronal holes. They are key elements to understanding the heating of the corona; nonetheless, basic questions regarding their heating mechanisms, the chromosphere underneath, or the effects of flux emergence in these structures remain open. We have used the Bifrost code to carry out a 2D experiment in which a coronal-hole magnetic null-point configuration evolves perturbed by realistic granulation. To compare with observations, synthetic SDO/AIA, Solar Orbiter EUI-HRI, and IRIS images have been computed. The experiment shows the self-consistent creation of a CBP through the action of stochastic granular motions alone, mediated by magnetic reconnection in the corona. The reconnection is intermittent and oscillatory, and it leads to coronal and transition-region temperature loops that are identifiable in our EUV/UV observables. During the CBP lifetime, convergence and cancellation at the surface of its underlying opposite polarities takes place. The chromosphere below the CBP shows a number of peculiar features concerning its density and the spicules in it. The final stage of the CBP is eruptive: Magnetic flux emergence at the granular scale disrupts the CBP topology, leading to different ejections, such as UV bursts, surges, and EUV coronal jets. Apart from explaining observed CBP features, our results pave the way for further studies combining simulations and coordinated observations in different atmospheric layers.

Wastewater-Based Epidemiology for COVID-19: Handling qPCR Nondetects and Comparing Spatially Granular Wastewater and Clinical Data Trends.

Wastewater-based epidemiology (WBE) is a useful complement to clinical testing for managing COVID-19. While community-scale wastewater and clinical data frequently correlate, less is known about subcommunity relationships between the two data types. Moreover, nondetects in qPCR wastewater data are typically handled through methods known to bias results, overlooking perhaps better alternatives. We address these knowledge gaps using data collected from September 2020–June 2021 in Davis, California (USA). We hypothesize that coupling the expectation maximization (EM) algorithm with the Markov Chain Monte Carlo (MCMC) method could improve estimation of “missing” values in wastewater qPCR data. We test this hypothesis by applying EM-MCMC to city wastewater treatment plant data and comparing output to more conventional nondetect handling methods. Dissimilarities in results (i) underscore the importance of specifying nondetect handling method in reporting and (ii) suggest that using EM-MCMC may yield better agreement between community-scale clinical and wastewater data. We also present a novel framework for spatially aligning clinical data with wastewater data collected upstream of a treatment plant (i.e., distributed across a sewershed). Applying the framework to data from Davis reveals reasonable agreement between wastewater and clinical data at highly granular spatial scales—further underscoring the public-health value of WBE.

Travel mode classification based on GNSS trajectories and open geospatial data

AbstractAccurate travel modes inferred from global navigation satellite system (GNSS) trajectory data can be instrumental to city development, such as travel planning and modeling and traffic flow prediction. With the advancement of mobile sensors, the Internet, and GNSS devices, massive GNSS trajectories have been recorded, laying a foundation for travel mode classification at a fine granular scale. However, the lack of discriminative features reduces the accuracy and robustness of travel mode classification. Thus, in this study, considering the relationship between trajectories and the surrounding transportation environment, a set of discriminative features extracted from geospatial data combined with various GNSS features generated from GNSS trajectories is proposed to better distinguish different travel modes. Based on this, we conduct a systematic comparison of a group of state‐of‐the‐art methods using GeoLife and OpenStreetMap (OSM) data, the results of which will provide guidance for properly selecting models for future travel mode classification‐related work. In addition, the comparison results show that adding GIS‐based domain expert features is robust in improving the classification accuracy of all classifiers in this study.

Four new species of polychromatic spiny-tailed iguanian lizards, genus Stenocercus (Iguania: Tropiduridae), from Peru.

The tropical Andes Mountains exhibit high levels of endemism and spatial turnover in the distribution of species. The lizard genus Stenocercus Dumril Bibron, 1837, contains 76 species and most of them occur in the tropical Andes, reaching elevations up to 4,000 m. We describe four new species of Stenocercus based on the examination of newly collected material from the Amazonian slopes of the Peruvian Andes. Stenocercus asenlignus sp. nov. inhabits the premontane forest of northern and central Peru, departments of Amazonas, San Martn and Hunuco, at elevations between 1,500 and 2,036 m, in the basins of the Mayo, Huayabamba, and Huallaga rivers. Stenocercus leybachi sp. nov. inhabits the premontane forest of the upper Huallaga River, Hunuco department in central Peru, at elevations between 824 and 1,270 m. Stenocercus qalaywasi sp. nov. was collected in a small village at the headwaters of the Mantaro River, Junn department in central Peru, at an elevation of 2,587 m. Finally, S. nigrocaudatus sp. nov. inhabits the montane forest from extreme northern Peru, Cajamarca department, at elevations of 1,700 and 1,892 m. These species are characterized by having granular scales on the posterior surface of the thighs, relatively short tail, caudals spinose, two caudal whorls per autotomic segment, and the ability to change coloration from green to brown or gray; they differ from other species of Stenocercus in scutellation features and color pattern.

Spatializing Social Networking Analysis to Capture Local Innovation Flows towards Inclusive Transition

The location of the local network of firms impacts, positively or negatively, their economic performance. The interactions between different sectors in a territory are still not easily observable. We test the complexity of the economic structure at a local level, given the availability of data at a very granular scale. This could greatly assist in observing sectors or/and locations that play a dominant role in the regional economy. Thus, in order to interpret the economic structure of a territory, we used cluster-based analysis. The analysis helps in evaluating the interconnections among sectors that constitute a cluster. A novel method of describing the territorial economic structure is presented by applying Social Network Analysis (SNA) within cluster-based analysis to characterize the importance of both location and economic interconnections. In this study, we focus on the industrial agglomerations in Calabria, Italy, to underpin the potential of the region’s industries by using social networking analysis metrics. This research put forward new interpretations of SNA metrics that describe regional economic compositions. Our findings reveal that territorial social networks are a potential instrument for understanding interactions in regional systems and economic clusters and might help in highlighting local industrial potentials. We believe that this study’s results could be considered as the initial steps for a pioneer data-driven place-based structural analysis model.

Generating hourly electricity demand data for large-scale single-family buildings by a decomposition-recombination method

Household electricity demand has substantial impacts on local grid operation, energy storage and the energy performance of buildings. Hourly demand data at district or urban level helps stakeholders understand the demand patterns from a granular time scale and provides robust evidence in energy management. However, such type of data is often expensive and time-consuming to collect, process and integrate. Decisions built upon smart meter data have to deal with challenges of privacy and security in the whole process. Incomplete data due to confidentiality concerns or system failure can further increase the difficulty of modeling and optimization. In addition, methods using historical data to make predictions can largely vary depending on data quality, local building environment, and dynamic factors. Considering these challenges, this paper proposes a statistical method to generate hourly electricity demand data for large-scale single-family buildings by decomposing time series data and recombining them into synthetics. The proposed method used public data to capture seasonality and the distribution of residuals that fulfill statistical characteristics. A reference building was used to provide empirical parameter settings and validations for the studied buildings. An illustrative case in a city of Sweden using only annual total demand was presented for deploying the proposed method. The results showed that the proposed method can mimic reality well and represent a high level of similarity to the real data. The average monthly error for the best month reached 15.9% and the best one was below 10% among 11 tested months. Less than 0.6% improper synthetic values were found in the studied region.

Integrated skin sensitization assessment based on OECD methods (I): Deriving a point of departure for risk assessment_suppl2

Three guidelines covering key events in the skin sensitization adverse outcome pathway are endorsed by the Organisation for Economic Co-operation and Development (OECD). A recent guideline covers defined approaches (DA) to combine data from these tests for regulatory (sub)classification. The guideline methods provide continuous data that could characterize the sensitization potency on a more granular scale beyond (sub)classifications. We assembled a comprehensive database of in vitro and in vivo results from OECD guideline tests. Building on a previous approach using regression models, we provide quantitative models using input data from the kinetic direct peptide reactivity assay (kDPRA), the KeratinoSens™ (KS) assay, and the human cell line activation test (h-CLAT) to calculate a point of departure (PoD) in the form of a predicted local lymph node assay (LLNA) EC3 value for use in risk assessment. Predictive models include results from either two or all three assays. Detailed analysis versus in vivo data estimates redundancy between different tests and helps guide model selection. All models were tested on a set of case studies selected for their availability of multiple LLNA reference data in the OECD database. The predicted PoDs were within or close to the range of the variation of the historical LLNA data for most of these cases studies, and overall, the models predicted the in vivo value with a median fold-misprediction factor of around 2.5. The robustness of the models was characterized by comparing a comprehensive historical database versus the curated dataset provided by the OECD working group on DA.

Integrated skin sensitization assessment based on OECD methods (I): Deriving a point of departure for risk assessment_suppl4

Three guidelines covering key events in the skin sensitization adverse outcome pathway are endorsed by the Organisation for Economic Co-operation and Development (OECD). A recent guideline covers defined approaches (DA) to combine data from these tests for regulatory (sub)classification. The guideline methods provide continuous data that could characterize the sensitization potency on a more granular scale beyond (sub)classifications. We assembled a comprehensive database of in vitro and in vivo results from OECD guideline tests. Building on a previous approach using regression models, we provide quantitative models using input data from the kinetic direct peptide reactivity assay (kDPRA), the KeratinoSens™ (KS) assay, and the human cell line activation test (h-CLAT) to calculate a point of departure (PoD) in the form of a predicted local lymph node assay (LLNA) EC3 value for use in risk assessment. Predictive models include results from either two or all three assays. Detailed analysis versus in vivo data estimates redundancy between different tests and helps guide model selection. All models were tested on a set of case studies selected for their availability of multiple LLNA reference data in the OECD database. The predicted PoDs were within or close to the range of the variation of the historical LLNA data for most of these cases studies, and overall, the models predicted the in vivo value with a median fold-misprediction factor of around 2.5. The robustness of the models was characterized by comparing a comprehensive historical database versus the curated dataset provided by the OECD working group on DA.

Integrated skin sensitization assessment based on OECD methods (I): Deriving a point of departure for risk assessment.

Three guidelines covering key events in the skin sensitization adverse outcome pathway are endorsed by the Organisation for Economic Co-operation and Development (OECD). A recent guideline covers defined approaches (DA) to combine data from these tests for regulatory (sub)classification. The guideline methods provide continuous data that could charac-terize the sensitization potency on a more granular scale beyond (sub)classifications. We assembled a comprehensive database of in vitro and in vivo results from OECD guideline tests. Building on a previous approach using regression models, we provide quantitative models using input data from the kinetic direct peptide reactivity assay (kDPRA), the KeratinoSens™ (KS) assay, and the human cell line activation test (h-CLAT) to calculate a point of departure (PoD) in the form of a predicted local lymph node assay (LLNA) EC3 value for use in risk assessment. Predictive models include results from either two or all three assays. Detailed analysis versus in vivo data estimates redundancy between different tests and helps guide model selection. All models were tested on a set of case studies selected for their availability of multiple LLNA reference data in the OECD database. The predicted PoDs were within or close to the range of the variation of the historical LLNA data for most of these cases studies, and overall, the models predicted the in vivo value with a median fold-misprediction factor of around 2.5. The robustness of the models was characterized by comparing a comprehensive historical database versus the curated dataset provided by the OECD working group on DA.

Integrated skin sensitization assessment based on OECD methods (I): Deriving a point of departure for risk assessment_suppl1

Three guidelines covering key events in the skin sensitization adverse outcome pathway are endorsed by the Organisation for Economic Co-operation and Development (OECD). A recent guideline covers defined approaches (DA) to combine data from these tests for regulatory (sub)classification. The guideline methods provide continuous data that could characterize the sensitization potency on a more granular scale beyond (sub)classifications. We assembled a comprehensive database of in vitro and in vivo results from OECD guideline tests. Building on a previous approach using regression models, we provide quantitative models using input data from the kinetic direct peptide reactivity assay (kDPRA), the KeratinoSens™ (KS) assay, and the human cell line activation test (h-CLAT) to calculate a point of departure (PoD) in the form of a predicted local lymph node assay (LLNA) EC3 value for use in risk assessment. Predictive models include results from either two or all three assays. Detailed analysis versus in vivo data estimates redundancy between different tests and helps guide model selection. All models were tested on a set of case studies selected for their availability of multiple LLNA reference data in the OECD database. The predicted PoDs were within or close to the range of the variation of the historical LLNA data for most of these cases studies, and overall, the models predicted the in vivo value with a median fold-misprediction factor of around 2.5. The robustness of the models was characterized by comparing a comprehensive historical database versus the curated dataset provided by the OECD working group on DA.

Integrated skin sensitization assessment based on OECD methods (I): Deriving a point of departure for risk assessment_suppl3

Three guidelines covering key events in the skin sensitization adverse outcome pathway are endorsed by the Organisation for Economic Co-operation and Development (OECD). A recent guideline covers defined approaches (DA) to combine data from these tests for regulatory (sub)classification. The guideline methods provide continuous data that could characterize the sensitization potency on a more granular scale beyond (sub)classifications. We assembled a comprehensive database of in vitro and in vivo results from OECD guideline tests. Building on a previous approach using regression models, we provide quantitative models using input data from the kinetic direct peptide reactivity assay (kDPRA), the KeratinoSens™ (KS) assay, and the human cell line activation test (h-CLAT) to calculate a point of departure (PoD) in the form of a predicted local lymph node assay (LLNA) EC3 value for use in risk assessment. Predictive models include results from either two or all three assays. Detailed analysis versus in vivo data estimates redundancy between different tests and helps guide model selection. All models were tested on a set of case studies selected for their availability of multiple LLNA reference data in the OECD database. The predicted PoDs were within or close to the range of the variation of the historical LLNA data for most of these cases studies, and overall, the models predicted the in vivo value with a median fold-misprediction factor of around 2.5. The robustness of the models was characterized by comparing a comprehensive historical database versus the curated dataset provided by the OECD working group on DA.

Developing a Granular Scale Environmental Burden Index (Ebi) for Diverse Land Cover Types Across the Contiguous United States

Developing a Granular Scale Environmental Burden Index (Ebi) for Diverse Land Cover Types Across the Contiguous United States

Innovation in low-energy demand and its implications for policy

AbstractScenarios featuring low-energy demand (LED) have the potential to meet climate stabilization targets while also pursuing inclusive well-being and multiple Sustainable Development Goals. Recent papers have assembled detailed bottom-up estimates to inform integrated assessment modeling and show that LED outcomes are feasible and have beneficial effects across multiple goals. But realizing the full potential in LED depends on innovation—i.e. improvement in LED technology and widespread adoption of both LED technology and behavior—as well as policies supporting innovation. We review the literature to better understand the role of technological innovation in enabling LED scenarios and how policy interventions can stimulate those innovations. We structure our review using a policy analysis framework that includes specifying multiple LED policy goals, describing the distinct characteristics of LED technology to understand policy needs. The distinct characteristics of LED innovation include multiple attributes and new services; many heterogeneous adopters; small granular scale, many iterations; local system integration; and rebound effects, among others. We also consider five important drivers of change in LED innovation: higher living standards and preferences for clean environments, urbanization, digitalization, demand for novel services and the emergence of prosumers. The analysis in this review of the literature leads to nine LED policy design guidelines.