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Information on urban built-up infrastructure is essential to understand the role of cities in shaping environmental, economic, and social outcomes. The lack of data on built-up heights over large areas has limited our ability to characterize urban infrastructure and its spatial variations across the world. Here, we developed a global atlas of urban built-up heights circa 2015 at 500-m resolution from the Sentinel-1 Ground Range Detected satellite data. Results show extreme gaps in per capita urban built-up infrastructure in the Global South compared with the global average, and even larger gaps compared with the average levels in the Global North. Per capita urban built-up infrastructures in some countries in the Global North are more than 30 times higher than those in the Global South. The results also show that the built-up infrastructure in 45 countries in the Global North combined, with ~ 16% of the global population, is roughly equivalent to that of 114 countries in the Global South, with ~74% of the global population. The inequality in urban built-up infrastructure, as measured by an inequality index, is large in most countries, but the largest in the Global South compared with the Global North. Our analysis reveals the scale of infrastructure demand in the Global South that is required in order to meet sustainable development goals. [ABSTRACT FROM AUTHOR]

Near-surface air temperature (Ta) is a key variable in global climate studies. A global gridded dataset of daily maximum and minimum Ta (Tmax and Tmin) is particularly valuable and critically needed in the scientific and policy communities, but is still not available. In this paper, we developed a global dataset of daily Tmax and Tmin dataset at 1-km resolution from 2003 to 2020 through the combined use of station-based ground Ta measurements and satellite observations (i.e., digital elevation model, and land surface temperature) via a state-of-the-art statistical method named Spatially Varying Coefficient Models with Sign Preservation (SVCM-SP). The root mean square errors of our estimates ranged from 1.20 to 2.44 for Tmax and 1.69 to 2.39 °C for Tmin. We found that the accuracies were affected primarily by land cover types, elevation ranges, and climate backgrounds. Our dataset correctly represents the negative and positive relationships between Ta with elevation or land surface temperature; it captured spatial and temporal patterns of Ta realistically. This global 1-km gridded daily Tmax and Tmin dataset is the first of its kind and we expect it to be of great value to global studies such as urban heat island phenomenon, hydrological modeling, and epidemic forecasting. The data are available at Iowa State University's DataShare at https://doi.org/10.25380/iastate.c.6005185 (Zhang and Zhou, 2022). [ABSTRACT FROM AUTHOR]

Dynamics of the urban extent at fine spatial and temporal resolutions over large areas are crucial for developing urban growth models and achieving sustainable development goals. However, there are limited practices of mapping urban dynamics with these two merits combined. In this study, we proposed a new method to map urban dynamics from Landsat time series data using the Google Earth Engine (GEE) platform and developed a national dataset of annual urban extent (1985–2015) at a fine spatial resolution (30 m) in the conterminous United States (US). First, we derived the change information of urbanized years in four periods that were determined from the National Land Cover Database (NLCD), using a temporal segmentation approach. Then, we classified urban extents in the beginning (1985) and ending (2015) years at the cluster level through implementing a change vector analysis (CVA) based approach. We developed a hierarchical strategy to apply the CVA based approach due to the spatially explicit urban sprawl over large areas. The overall accuracy of mapped urbanized years is around 90 % with the one-year tolerance strategy. The mapped urbanized areas in the beginning and ending years are reliable, with overall accuracies of 96 % and 88 %, respectively. Our results reveal that the total urban area increased by about 20 % during the period 1985–2015, and the annual urban area growth is not linear over the years. Overall, the growth pattern of urban extent in most coastal states is plateaued over the past three decades while the states in the Midwestern US show an accelerated growth pattern. The derived annual urban extents are of great use for relevant urban studies such as urban area projection and urban sprawl modeling in large areas. Moreover, the proposed mapping framework is transferable for developing annual dynamics of urban extent in other regions and even globally. [ABSTRACT FROM AUTHOR]

Medium-resolution satellite observations show great potential for characterizing seasonal and annual dynamics of vegetation phenology in urban domains from local to regional and global scales. However, most previous studies were conducted using coarse-resolution data, which are inadequate for characterizing the spatiotemporal dynamics of vegetation phenology in urban domains. In this study, we produced an annual vegetation phenology dataset in urban ecosystems for the conterminous United States (US), using all available Landsat images on the Google Earth Engine (GEE) platform. First, we characterized the long-term mean seasonal pattern of phenology indicators of the start of season (SOS) and the end of season (EOS), using a double logistic model. Then, we identified the annual variability of these two phenology indicators by measuring the difference of dates when the vegetation index in a specific year reaches the same magnitude as its long-term mean. The derived phenology indicators agree well with in situ observations from the PhenoCam network and Harvard Forest. Comparing with results derived from the moderate-resolution imaging spectroradiometer (MODIS) data, our Landsat-derived phenology indicators can provide more spatial details. Also, we found the temporal trends of phenology indicators (e.g., SOS) derived from Landsat and MODIS are consistent overall, but the Landsat-derived results from 1985 offer a longer temporal span compared to MODIS from 2001 to present. In general, there is a spatially explicit pattern of phenology indicators from the north to the south in cities in the conterminous US, with an overall advanced SOS in the past 3 decades. The derived phenology product in the US urban domains at the national level is of great use for urban ecology studies for its medium spatial resolution (30 m) and long temporal span (30 years). The data are available at https://doi.org/10.6084/m9.figshare.7685645.v5. [ABSTRACT FROM AUTHOR]

Although the scale of impending urbanization is well-acknowledged, we have a limited understanding of how urban forms will change and what their impact will be on building energy use. Using both top-down and bottom-up approaches and scenarios, we examine building energy use for heating and cooling. Globally, the energy use for heating and cooling by the middle of the century will be between 45 and 59 exajoules per year (corresponding to an increase of 7–40% since 2010). Most of this variability is due to the uncertainty in future urban densities of rapidly growing cities in Asia and particularly China. Dense urban development leads to less urban energy use overall. Waiting to retrofit the existing built environment until markets are ready in about 5 years to widely deploy the most advanced renovation technologies leads to more savings in building energy use. Potential for savings in energy use is greatest in China when coupled with efficiency gains. Advanced efficiency makes the least difference compared with the business-as-usual scenario in South Asia and Sub-Saharan Africa but significantly contributes to energy savings in North America and Europe. Systemic efforts that focus on both urban form, of which urban density is an indicator, and energy-efficient technologies, but that also account for potential co-benefits and trade-offs with human well-being can contribute to both local and global sustainability. Particularly in growing cities in the developing world, such efforts can improve the well-being of billions of urban residents and contribute to mitigating climate change by reducing energy use in urban areas. [ABSTRACT FROM AUTHOR]

The Defense Meteorological Satellite Program (DMSP)/Operational Linescan System (OLS) stable nighttime light (NTL) data provide a wide range of potentials for studying global and regional dynamics, such as urban sprawl and electricity consumption. However, due to the lack of on-board calibration, it requires inter-annual calibration for these practical applications. In this study, we proposed a stepwise calibration approach to generate a temporally consistent NTL time series from 1992 to 2013. First, the temporal inconsistencies in the original NTL time series were identified. Then, a stepwise calibration scheme was developed to systematically improve the over- and underestimation of NTL images derived from particular satellites and years, by making full use of the temporally neighbored image as a reference for calibration. After the stepwise calibration, the raw NTL series were improved with a temporally more consistent trend. Meanwhile, the magnitude of the global sum of NTL is maximally maintained in our results, as compared to the raw data, which outperforms previous conventional calibration approaches. The normalized difference index indicates that our approach can achieve a good agreement between two satellites in the same year. In addition, the analysis between the calibrated NTL time series and other socioeconomic indicators (e.g., gross domestic product and electricity consumption) confirms the good performance of the proposed stepwise calibration. The calibrated NTL time series can serve as useful inputs for NTL related dynamic studies, such as global urban extent change and energy consumption. [ABSTRACT FROM AUTHOR]

The Defense Meteorological Satellite Program's Operational Linescan System (DMSP-OLS) nighttime lights imagery has proven to be a powerful remote sensing tool to monitor urbanization and assess socioeconomic activities at large scales. However, the existence of incompatible digital number (DN) values and geometric errors severely limit application of nighttime light image data on multiyear quantitative research. In this paper, we extend and improve previous studies on intercalibrating nighttime lights image data to obtain more compatible and reliable nighttime lights time-series (NLT) image data for China and the U.S. through four steps, namely, intercalibration, geometric correction, steady-increase adjustment, and population data correction. We then use gross domestic product (GDP) data to test the processed NLT image data indirectly and find that sum light (summed DN value of pixels in a nighttime light image) maintains apparent increase trends with relatively large GDP growth rates but does not increase or decrease with relatively small GDP growth rates. As nighttime light is a sensitive indicator for economic activity, the temporally consistent trends between sum light and GDP growth rate imply that brightness of nighttime lights on the ground is correctly represented by the processed NLT image data. Finally, through analyzing the corrected NLT image data from 1992 to 2008, we find that China experienced apparent nighttime lights development in 1992-1997 and 2001-2008, respectively, and the U.S. showed nighttime lights decay in large areas after 2001. [ABSTRACT FROM PUBLISHER]

The information of global spatially explicit urban extents under scenarios is important to mitigate future environmental risks caused by global urbanization and climate change. Although future dynamics of urban extent were commonly modelled with conversion from non-urban to urban using cellular automata (CA) based models, gradual changes of impervious surface area (ISA) at the pixel level were limitedly explored in previous studies. In this paper, we developed a global dataset of urban fractional changes at a 1km resolution from 2020 to 2100 (5-year interval), under eight scenarios of socioeconomic pathways and climate change. First, to quantify the gradual change of ISA within the pixel, we characterized ISA growth patterns over past decades (i.e., 1985-2015) using a sigmoid growth model and annual global artificial impervious area (GAIA) data. Then, by incorporating the ISA-based growth mechanism with the CA model, we calibrated the statespecific urban CA model with quantitative evaluation at the global scale. Finally, we projected future urban fractional changes at 1km resolution under eight development pathways based on the harmonized urban growth demand from Land Use Harmonization2 (LUH2). The evaluation results show that the ISA-based urban CA model performs well globally, with an overall R2 of 0.9 and the Root Mean Square Error (RMSE) of 0.08 between modeled and observed ISAs in 2015. With the inclusion of temporal contexts of urban sprawl gained from GAIA, the dataset of global urban fractional change shows good agreement with 30-year historical observations from satellites. The dataset can capture spatially explicit variations of ISA and gradual ISA change within pixels. The dataset of global urban fractional change is of great use in supporting quantitative analysis of urbanization-induced ecological and environmental change at a fine scale, such as urban heat islands, energy consumption, and human-nature interactions in the urban system. The developed dataset of global urban fractional change is available at https://doi.org/10.6084/m9.figshare.20391117. v2 (He et al., 2022). [ABSTRACT FROM AUTHOR]

In this study, we develop an updated global estimate of onshore wind energy potential using reanalysis wind speed data, along with updated wind turbine technology performance, land suitability factors, cost assumptions, and explicit consideration of transmission distance in the calculation of transmission costs. We find that wind has the potential to supply a significant portion of the world energy needs, although this potential varies substantially by region and with assumptions such as on what types of land can be used to site wind farms. Total global economic wind potential under central assumptions, that is, intermediate between optimistic and pessimistic, is estimated to be approximately 119.5 petawatt hours per year (13.6 TW) at less than 9 cents/kWh. A sensitivity analysis of eight key parameters is presented. Wind potential is sensitive to a number of input parameters, particularly wind speed (varying by -70% to +450% at less than 9 cents/kWh), land suitability (by -55% to +25%), turbine density (by -60% to +80%), and cost and financing options (by -20% to +200%), many of which have important policy implications. As a result of sensitivities studied here we suggest that further research intended to inform wind supply curve development focus not purely on physical science, such as better resolved wind maps, but also on these less well-defined factors, such as land-suitability, that will also have an impact on the long-term role of wind power. [ABSTRACT FROM AUTHOR]

distributed object-based rainfall–runoff simulation (DORS) model with incorporation of detailed impervious surface-area (ISA) data, derived from digital true-color orthophotography data with high spatial resolution, was developed. This physically based model simulates hydrologic processes of precipitation interception, infiltration, runoff, evapotranspiration, change of soil moisture, change of water-table depth, runoff routing, groundwater routing, and channel-flow routing. The modeling processes take objects based on land-cover types as fundamental spatial units in order to reduce data volume, increase computational efficiency, strengthen representation of watersheds, and utilize the data in variable scales. US Geological Survey stream-gaging data were used to validate the temporal variation of simulated discharge within two watersheds in Rhode Island State. The ratio of absolute error to the mean and the Nash coefficient in the validation period are 7.2% and 0.90 for the first watershed, and 8.0% and 0.77 for the second watershed, respectively. The results indicate that the DORS model is able to capture the relationship between rainfall and runoff in the study area, and that it is applicable in the further study of ISA impacts on the water cycle and associated pollution problems. The results also demonstrate that the performance of the hydrologic simulation is improved with ISA data with high spatial resolution. [ABSTRACT FROM AUTHOR]

Impervious surface area (ISA) has emerged as a key indicator to explain and predict ecosystem health in relationship to watershed development. In this study, we extracted the information of ISA for the state of Rhode Island using 1-m spatial resolution true-color digital orthophotography data. We employed an object-oriented algorithm of multiple-agent segmentation and classification (MASC) that we developed for ISA information extraction. The result indicates that, as of 2004, 10% of the state land has been covered by ISA. The major population centers and historical cities, such as Providence, Woonsocket, and Newport, have ISA over 30%. The heavily settled suburban communities have ISA between 10 and 30%. Only 17 out of 39 towns in the state have less than 10% ISA. The average ISA for the coastal towns is 14%. Because most stream-quality indicators are predicted to decline when watershed ISA exceeds 10%, the results from this study serve as an alarming indicator for managing the state's watershed and coastal ecosystems. The tested MASC model could be extended to coastal Massachusetts and Connecticut to provide a more comprehensive indication of the impacts of human-induced land-cover change on southern New England's coast. [ABSTRACT FROM AUTHOR]

Dust loading in West and South Asia has been a major environmental issue due to its negative effects on air quality, food security, energy supply and public health, as well as on regional and global weather and climate. Yet a robust understanding of its recent changes and future projection remains unclear. On the basis of several high-quality remote sensing products, we detect a consistently decreasing trend of dust loading in West and South Asia over the last two decades. In contrast to previous studies emphasizing the role of local land use changes, here, we attribute the regional dust decline to the continuous intensification of Arctic amplification driven by anthropogenic global warming. Arctic amplification results in anomalous mid-latitude atmospheric circulation, particularly a deepened trough stretching from West Siberia to Northeast India, which inhibits both dust emissions and their downstream transports. Large ensemble climate model simulations further support the dominant role of greenhouse gases induced Arctic amplification in modulating dust loading over West and South Asia. Future projections under different emission scenarios imply potential adverse effects of carbon neutrality in leading to higher regional dust loading and thus highlight the importance of stronger anti-desertification counter-actions such as reforestation and irrigation management. [ABSTRACT FROM AUTHOR]

Understanding the spatiotemporal dynamics of global urbanization over a long time series is increasingly important for sustainable development goals. The harmonized time-series nighttime light (NTL) composites by fusing multi-source NTL observations provide a long and consistent record of the nightscape for characterizing and understanding the global urban dynamics. In this study, we generated a global dataset of annual urban extents (1992-2020) using consistent NTL observations and analyzed the spatiotemporal patterns of global urban dynamics over nearly 30 years. The urbanized areas associated with locally high- intensity human activities were mapped from the time-series global NTL imagery using a new stepwise-partitioning framework. This framework includes three components: (1) clustering of NTL signals to generate potential urban clusters; (2) identification of optimal thresholds to delineate annual urban extents; and (3) check of temporal consistency to correct pixel-level urban dynamics. We found that the global urban land area percentage to the Earth's land surface raised from 0.22 % to 0.69 % in 1992 and 2020, respectively. Urban dynamics over the past three decades at the continent, country, and city levels exhibit various spatiotemporal patterns. Our resulting global urban extents (1992-2020) were evaluated using other urban remote sensing products and socioeconomic data. The evaluations indicate that this dataset is reliable for characterizing spatial extents associated with intensive human settlement and high-intensity socioeconomic activities. The dataset of global urban extents from this study can provide unique information to capture the historical and future trajectories of urbanization, and understand and tackle the urbanization impacts on food security, biodiversity, climate change, and public well-being and health. This dataset can be downloaded from https://doi.org/10.6084/m9.figshare.16 602224.v1 (Zhao et al., 2021). [ABSTRACT FROM AUTHOR]

Urbanization has caused environmental changes, such as urban heat islands (UHIs), that affect terrestrial ecosystems. However, how and to what extent urbanization affects plant phenology remains relatively unexplored. Here, we investigated the changes in the satellite-derived start of season (SOS) and the covariation between SOS and temperature (RT) in 85 large cities across the conterminous United States for the period 2001-2014. We found that 1) the SOS came significantly earlier (6.1 ± 6.3 d) in 74 cities and RT was significantly weaker (0.03 ± 0.07) in 43 cities when compared with their surrounding rural areas (P < 0.05); 2) the decreased magnitude in RT mainly occurred in cities in relatively cold regions with an annual mean temperature <17.3 °C (e.g., Minnesota, Michigan, and Pennsylvania); and 3) the magnitude of urban-rural difference in both SOS and RT was primarily correlated with the intensity of UHI. Simulations of two phenology models further suggested that more and faster heat accumulation contributed to the earlier SOS, while a decrease in required chilling led to a decline in RT magnitude in urban areas. These findings provide observational evidence of a reduced covariation between temperature and SOS in major US cities, implying the response of spring phenology to warming conditions in nonurban environments may decline in the warming future. [ABSTRACT FROM AUTHOR]

Quantification of fossil fuel CO2 emissions at fine space and time resolution is emerging as a critical need in carbon cycle and climate change research. As atmospheric CO2 measurements expand with the advent of a dedicated remote sensing platform and denser in situ measurements, the ability to close the carbon budget at spatial scales of ∼100 km² and daily time scales requires fossil fuel CO2 inventories at commensurate resolution. Additionally, the growing interest in U.S. climate change policy measures are best served by emissions that are tied to the driving processes in space and time. Here we introduce a high resolution data product (the "Vulcan" inventory: www.purdue.edu/eas/carbon/vulcan/) that has quantified fossil fuel CO2 emissions for the contiguous U.S. at spatial scales less than 100 km² and temporal scales as small as hours. This data product, completed for the year 2002, includes detail on combustion technology and 48 fuel types through all sectors of the U.S. economy. The Vulcan inventory is built from the decades of local/regional air pollution monitoring and complements these data with census, traffic, and digital road data sets. The Vulcan inventory shows excellent agreement with national-level Department of Energy inventories, despite the different approach taken by the DOE to quantify U.S. fossil fuel CO2 emissions. Comparison to the global 1° × 1° fossil fuel CO2 inventory, used widely by the carbon cycle and climate change community prior to the construction of the Vulcan inventory, highlights the space/time biases inherent in the population-based approach. [ABSTRACT FROM AUTHOR]

The nighttime light (NTL) satellite data have been widely used to investigate urbanization process. The Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS) stable nighttime light data and Suomi National Polar-Orbiting Partnership-Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) nighttime light data are two widely used NTL datasets. However, the difference of their spatial resolutions and sensor design makes these two datasets can not be directly used together for a long-term analysis of urbanization. To solve this issue, an extended time-series (2000-2018) of NPP-VIIRS-like NTL data were proposed in this study through a cross-sensor calibration from DMSP-OLS NTL data (2000-2012) and a composition of monthly NPP-VIIRS NTL data (2013-2018). Compared with the annual composited NPP-VIIRS NTL data in 2012, our product of extended NPP-VIIRS-like NTL data shows a good consistency at the pixel and city levels with R2 of 0.87 and 0.95, respectively. We also found that our product has a good accuracy by comparing with DMSP-OLS radiance calibrated NTL (RNTL) data in 2000, 2004, 2006, and 2010. Generally, our extended NPP-VIIRS-like NTL data (2000-2018) have a good spatial pattern and temporal consistency, which are similar to the composited NPP-VIIRS NTL data. In addition, the resulting product could be easily updated and provide a useful proxy to monitor the dynamics of demographic and socio-economic activities for a longer time period compared to existing products. The extended time-series (2000-2018) of nighttime light data are freely accessible at https://doi.org/10.7910/DVN/YGIVCD (Chen et al., 2020). [ABSTRACT FROM AUTHOR]

Humans challenge the phenotypic, genetic, and cultural makeup of species by affecting the fitness landscapes on which they evolve. Recent studies show that cities might play a major role in contemporary evolution by accelerating phenotypic changes in wildlife, including animals, plants, fungi, and other organisms. Many studies of ecoevolutionary change have focused on anthropogenic drivers, but none of these studies has specifically examined the role that urbanization plays in ecoevolution or explicitly examined its mechanisms. This paper presents evidence on the mechanisms linking urban development patterns to rapid evolutionary changes for species that play important functional roles in communities and ecosystems. Through a metaanalysis of experimental and observational studies reporting more than 1,600 phenotypic changes in species across multiple regions, we ask whether we can discriminate an urban signature of phenotypic change beyond the established natural baselines and other anthropogenic signals. We then assess the relative impact of five types of urban disturbances including habitat modifications, biotic interactions, habitat heterogeneity, novel disturbances, and social interactions. Our study shows a clear urban signal; rates of phenotypic change are greater in urbanizing systems compared with natural and nonurban anthropogenic systems. By explicitly linking urban development to traits that affect ecosystem function, we can map potential ecoevolutionary implications of emerging patterns of urban agglomerations and uncover insights for maintaining key ecosystem functions upon which the sustainability of human well-being depends. [ABSTRACT FROM AUTHOR]

There is evidence that warming leads to greater evapotranspiration and surface drying, thus contributing to increasing intensity and duration of drought and implying that mitigation would reduce water stresses. However, understanding the overall impact of climate change mitigation on water resources requires accounting for the second part of the equation, i.e., the impact of mitigation-induced changes in water demands from human activities. By using integrated, high-resolution models of human and natural system processes to understand potential synergies and/or constraints within the climate–energy–water nexus, we show that in the United States, over the course of the 21st century and under one set of consistent socioeconomics, the reductions in water stress from slower rates of climate change resulting from emission mitigation are overwhelmed by the increased water stress from the emissions mitigation itself. The finding that the human dimension outpaces the benefits from mitigating climate change is contradictory to the general perception that climate change mitigation improves water conditions. This research shows the potential for unintended and negative consequences of climate change mitigation. [ABSTRACT FROM AUTHOR]

This paper presents an overview of the study design and the results of the EMF 24 U.S. Technology Scenarios. The EMF 24 U.S. Technology Scenarios engaged nine top energy-environment-economy models to examine the implications of technological improvements and technological availability for reducing U.S. greenhouse gas emissions by 50% and 80% by 2050. The study confirms that mitigation at the 50% or 80% level will require a dramatic transformation of the energy system over the next 40 years. The study also corroborates the result of previous studies that there is a large variation among models in terms of which energy strategy is considered most cost-effective. Technology assumptions are found to have a large influence on carbon prices and economic costs of mitigation. [ABSTRACT FROM AUTHOR]

Abstract: Integrated assessment models have been extensively used to examine long term energy and greenhouse emissions trajectories, but these models have rarely been examined with respect to their ability to capture historical dynamics. We evaluate the USA buildings component of the Global Change Assessment Model by calibrating the model for 1990 and running forward to 2095. Results for 1995, 2000, 2005 and 2010 are compared to historical estimates at the level of fuel, service, and US states. We highlight that the creation of a historical evaluation dataset is one of the foremost challenges in the evaluation process. This model set-up matches the reported growth of residential floorspace but slightly overestimates heating service demand and underestimates the growth in miscellaneous plug loads. While cooling service appears to be underestimated in 2005, we find little evidence for a systematic bias for other years. The apparently rapid growth of some demands in the commercial sector was also not captured. While these differences carry through into future projections, the overall character of these projections is not changed. These results highlight the difficulty of determining the relationship between energy service demand, which is an unobserved variable, and its drivers when there is limited historical data. [Copyright &y& Elsevier]

In order to advance the scientific understanding of carbon exchange with the land surface, build an effective carbon monitoring system, and contribute to quantitatively based U.S. climate change policy interests, fine spatial and temporal quantification of fossil fuel CO2 emissions, the primary greenhouse gas, is essential. Called the "Hestia Project", this research effort is the first to use bottom-up methods to quantify all fossil fuel CO2 emissions down to the scale of individual buildings, road segments, and industrial/electricity production facilities on an hourly basis for an entire urban landscape. Here, we describe the methods used to quantify the on-site fossil fuel CO2 emissions across the city of Indianapolis, IN. This effort combines a series of data sets and simulation tools such as a building energy simulation model, traffic data, power production reporting, and local air pollution reporting. The system is general enough to be applied to any large U.S. city and holds tremendous potential as a key component of a carbon-monitoring system in addition to enabling efficient greenhouse gas mitigation and planning. We compare the natural gas component of our fossil fuel CO2 emissions estimate to consumption data provided by the local gas utility. At the zip code level, we achieve a bias-adjusted Pearson r correlation value of 0.92 (p < 0.001). [ABSTRACT FROM AUTHOR]