This chapter reviews developments associated with globalization—among them, dramatic changes in patterns of trade and the location of economic activities—and how these developments have affected the environmental footprints of cities and states or regions in which cities are located. It also discusses ways in which urban environmental footprints can be lightened. A critical part of any successful program with such an objective involves providing new and maintaining existing urban infrastructure systems that deliver food, energy, and water to cities and facilitate interurban—hence interregional and international—trade. The chapter thus concludes with a discussion of how one might provide analytical support for managing changes in urban infrastructure systems to lighten environmental footprints of cities, their hinterlands, and more distant settlements with which they trade.

This chapter presents a framework for estimating black carbon (BC) emissions from heavy-duty diesel vehicles (HDDV) and trains engaged in transporting freight in the Midwestern and Northeastern United States between 1977 and 2007. The estimates produced are comparable to other existing emissions inventories. This framework is employed in attempting to answer two questions: (1) What were the trends in BC emissions from HDDV and rail transportation sources over this period and what were the major factors that drove these trends? (2) What economic sectors dominated BC emissions and what major changes in sectoral behavior occurred over this period? The framework presented allows for the direct estimation of future BC emissions under a variety of economic, technological, and regulatory scenarios through changes in transportation patterns and emission factors.

The previous chapter of this volume discussed the generation of spatial time-series data on interstate inter-industry trade flows for the Midwestern and Northeastern states of the United States and the rest of the country over the period of 1977 to 2007. This chapter provides an analysis of these data and detailed commentary on changes in aggregate volumes of shipments (reflecting the increasing transport intensity of production and consumption), changes in patterns of intra-industry shipments (reflecting changes in economic geography), and changes in patterns of associated black carbon emissions that result from the movement of goods.

This chapter presents and demonstrates a methodology for generating spatial time series on interregional (interstate) inter-industry sales (or commodity flows). The methodology embodies an approach to benchmarking and estimating a dynamic multiregional econometric input–output model (or REIM) with annual data, backing out annual interregional inter-industry sales coefficients from the estimated model, and using the coefficients to generate annual observations on commodity flows. The application of this methodology is demonstrated with a REIM that has been estimated for 13 Midwestern, New England, and North Atlantic states and the rest of the United States and 13 industries using time-series data published by the US Bureau of Economic Analysis and the US Bureau of Labor Statistics. The chapter concludes with brief observations on patterns of change in interregional inter-industry commodity flows and sales coefficients.

This chapter presents the operationalization and econometric estimation of a modified version of the dynamic continuous-time structural-equation model of commodity flows elaborated in the previous chapter. The objectives of this exercise are threefold: (1) to estimate an empirically based dynamic model that can accommodate the stylized facts of globalization noted earlier in this volume; (2) to determine whether or not a model that embodies a New Economic Geography formulation of production is supported by the data, and (3) develop and make available for other scholars regional economic data that supplement the commodity-flow data whose derivation and analysis have been discussed in Chaps. 3 – 5 .

This book presents extensions to current commodity-flow models to analyze the economic and environmental impacts of recent structural changes.

We test three methods for ozone prediction in the El Paso (ELP) and Houston-Galveston-Brazoria (HGB) regions of Texas from 2005–2019: (1) a Generalized Additive Model (GAMs) approach; (2) a GAM approach with the addition of the Synthetic Minority Over-sampling TEchnique (SMOTE) and (3) a tail dependence modeling approach based in extreme value theory (EVT). We also compare the feature selection capabilities of the tail dependence approach to other feature selection methods. We find that the GAM+SMOTE model outperformed the GAM-only model when predicting ozone values for the root mean square error metric, particularly with regard to the above-threshold ozone values, which may be of particularly useful for extreme ozone event prediction. In addition, we find that the improvement of above-threshold MDA8 O3 prediction for the GAM+SMOTE method tends to come at the cost of below-threshold prediction, which is particularly important if MDA8 O3 trends are of interest. We also find that the tail dependence approach is capable of predicting extreme ozone events, but algorithmic stability and configuration complexity can make this approach difficult to operationalize on a broad scale and that the selection of the threshold needs to be carefully considered. Finally, the feature selection via the tail dependence method performs comparably to other forms of machine learning-based feature selection and we find that there are multiple parameter sets that can predict MDA8 O3 with equal success.

This chapter presents projections from 2008 to 2030 of point-source emissions of three of the US EPA’s criteria pollutants—carbon monoxide (CO), nitrogen oxide (NOx), and sulfur dioxide (SO2)—and volatile organic compounds (VOC) from industrial production in the nine states or multistate groupings considered in the previous chapter and nonpoint-source emissions of the criteria pollutants and VOC plus black carbon (BC) from commodity flows along the routes connecting centroids of these states and multistate groupings. This chapter also presents calculations of environmental (emissions) footprints of both industrial production and consumption (final demand) by industry, pollutant, and location. This study is one of the first to make such calculations, taking into account interindustry sales that constitute commodity flows, and demonstrates a methodology for doing so.

Biomass burning is a major source of atmospheric particulate matter (PM) with impacts on health, climate, and air quality. The particles and vapors within biomass burning plumes undergo chemical and physical aging as they are transported downwind. Field measurements of the evolution of PM with plume age range from net decreases to net increases, with most showing little to no change. In contrast, laboratory studies tend to show significant mass increases on average. On the other hand, similar effects of aging on the average PM composition (e.g. oxygen-to-carbon ratio) are reported for lab and field studies. Currently, there is no consensus on the mechanisms that lead to these observed similarities and differences. This review summarizes available observations of aging-related biomass burning aerosol mass concentrations and composition markers, and discusses four broad hypotheses to explain variability within and between field and laboratory campaigns: (1) variability in emissions and chemistry, (2) differences in dilution/entrainment, (3) losses in chambers and lines, and (4) differences in the timing of the initial measurement, the baseline from which changes are estimated. We conclude with a concise set of research needs for advancing our understanding of the aging of biomass burning aerosol.

Fracking', or unconventional gas development via hydraulic fracturing (hereafter UGD'), has been closely tied to global climate change in academic discourse. Researchers have debated the life cycle emissions of shale gas versus coal, rates of methane leakage from wellhead production and transmission infrastructure, the extent to which coal would be displaced by gas as a source of energy, the appropriate time-scale for accounting for the global warming potentials of methane and carbon dioxide, surface versus airborne methane measurements, and the effect of lowered energy prices on gas consumption. Little research, however, has examined the degree to which these potential connections between UGD and climate change are relevant to the general public. This article presents two surveys, one of a representative national (US) sample and one of a representative sample of residents in the Marcellus Shale region of Pennsylvania and New York. It examines whether respondents associated UGD with climate change, and the relationship between this association and their support for, or opposition to, UGD. The results reveal that beliefs about many other potential impacts of UGD explain more variation in support and opposition than do beliefs about UGD's association with climate change. Furthermore, most other impacts of UGD are viewed as having more effect on quality of life if they were to occur, at least amongst the Marcellus Shale survey sample. The article concludes with implications of the findings for policy and communication on UGD.Key policy insights Public opinion about unconventional gas development (UGD or fracking') is affected less by beliefs about its impact on global climate change, than about several other more local factors.Communication tailored to increase awareness of UGD's impacts would likely be most effective when focusing on the local level, as opposed to national or global impacts.Messaging about UGD's relationship with carbon emissions would have more effect in national-level discourse, as opposed to messaging targeted at communities experiencing or potentially experiencing development.To maintain credibility and societal trust, communication on the global climate impacts of UGD needs to be informative but non-persuasive.

The detection of meteorological, chemical, or other signals in modeled or observed air quality data – such as an estimate of a temporal trend in surface ozone data, or an estimate of the mean ozone of a particular region during a particular season – is a critical component of modern atmospheric chemistry. However, the magnitude of a surface air quality signal is generally small compared to the magnitude of the underlying chemical, meteorological, and climatological variabilities (and their interactions) that exist both in space and in time, and which include variability in emissions and surface processes. This can present difficulties for both policymakers and researchers as they attempt to identify the influence or signal of climate trends (e.g., any pauses in warming trends), the impact of enacted emission reductions policies (e.g., United States NOx State Implementation Plans), or an estimate of the mean state of highly variable data (e.g., summertime ozone over the northeastern United States). Here we examine the scale dependence of the variability of simulated and observed surface ozone data within the United States and the likelihood that a particular choice of temporal or spatial averaging scales produce a misleading estimate of a particular ozone signal. Our main objective is to develop strategies that reduce the likelihood of overconfidence in simulated ozone estimates. We find that while increasing the extent of both temporal and spatial averaging can enhance signal detection capabilities by reducing the noise from variability, a strategic combination of particular temporal and spatial averaging scales can maximize signal detection capabilities over much of the continental US. For signals that are large compared to the meteorological variability (e.g., strong emissions reductions), shorter averaging periods and smaller spatial averaging regions may be sufficient, but for many signals that are smaller than or comparable in magnitude to the underlying meteorological variability, we recommend temporal averaging of 10–15 years combined with some level of spatial averaging (up to several hundred kilometers). If this level of averaging is not practical (e.g., the signal being examined is at a local scale), we recommend some exploration of the spatial and temporal variability to provide context and confidence in the robustness of the result. These results are consistent between simulated and observed data, as well as within a single model with different sets of parameters. The strategies selected in this study are not limited to surface ozone data and could potentially maximize signal detection capabilities within a broad array of climate and chemical observations or model output.

While state-of-the-art complex chemical mechanisms expand our understanding of atmospheric chemistry, their sheer size and computational requirements often limit simulations to short lengths or ensembles to only a few members. Here we present and compare three 25-year present-day offline simulations with chemical mechanisms of different levels of complexity using the Community Earth System Model (CESM) Version 1.2 CAM-chem (CAM4): the Model for Ozone and Related Chemical Tracers, version 4 (MOZART-4) mechanism, the Reduced Hydrocarbon mechanism, and the Super-Fast mechanism. We show that, for most regions and time periods, differences in simulated ozone chemistry between these three mechanisms are smaller than the model–observation differences themselves. The MOZART-4 mechanism and the Reduced Hydrocarbon are in close agreement in their representation of ozone throughout the troposphere during all time periods (annual, seasonal, and diurnal). While the Super-Fast mechanism tends to have higher simulated ozone variability and differs from the MOZART-4 mechanism over regions of high biogenic emissions, it is surprisingly capable of simulating ozone adequately given its simplicity. We explore the trade-offs between chemical mechanism complexity and computational cost by identifying regions where the simpler mechanisms are comparable to the MOZART-4 mechanism and regions where they are not. The Super-Fast mechanism is 3 times as fast as the MOZART-4 mechanism, which allows for longer simulations or ensembles with more members that may not be feasible with the MOZART-4 mechanism given limited computational resources.