Carbon accounting results for the same city can differ due to differences in protocols, methods and data sources. A critical review of these differences and the connection among them can help to bridge our knowledge between university-based researchers and protocol practitioners in accounting and taking further mitigation actions. The purpose of this study is to provide a review of published research and protocols related to city carbon accounting paying attention to both their science and practical actions. To begin with, the most cited articles in this field are identified and analysed by employing a citation network analysis to illustrate the development of city-level carbon accounting from three perspectives. We also reveal the relationship between research methods and accounting protocols. Furthermore, a timeline of relevant organizations, protocols and projects is provided to demonstrate the applications of city carbon accounting in practice. The citation networks indicate that the field is dominated by pure-geographic production-based and community infrastructure-based accounting, however, emerging models that combine economic system analysis from a consumption-based perspective are leading to new trends in the field. The emissions accounted for by various research methods consist essentially of the scope 1-3 as defined in accounting protocols. The latest accounting protocols include consumption-based accounting but most cities still limit their accounting and reporting from a pure-geographic production-based and community infrastructure-based perspective. In concluding, we argue that protocol practitioners require support in conducting carbon accounting so as to explore the potential in mitigation and adaption from a number of perspectives. This should also be a priority for future studies.

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Summary We explore the population‐scaling and gross domestic product (GDP)‐scaling relationships of material and energy flow (MEF) parameters in different city types based on economic structure. Using migration‐corrected population data, we classify 233 Chinese city propers (Shiqu) as “highly industrial” (share of secondary GDP exceeds 63.9%), “highly commercial” (share of tertiary GDP exceeds 52.6%), and “mixed‐economy” (the remaining cities). We find that, first, the GDP population‐scaling factors differ in the different city types. Highly commercial and mixed‐economy cities exhibit superlinear GDP population‐scaling factors greater than 1, whereas highly industrial cities are sublinear. Second, GDP scaling better correlates with city‐wide MEF parameters in Chinese cities; these scaling relationships also show differences by city typology. Third, highly commercial cities are significantly different from others in demonstrating greater average per capita household income creation relative to per capita GDP. Further, highly industrial cities show an apparent cap in population. This also translates to lower densities in highly industrial cities compared to other types, showing a size effect on urban population density. Finally, a multiple variable regression of total household electricity showed significant and positive correlation with population, income effect, and urban form effect. With such multivariate modeling, the apparent superlinearity of household electricity use with respect to population is no longer observed. Our study enhances understanding of MEFs associated with Chinese cities and provides new insights into the patterns of scaling observed in different city types by economic structure. Results recommend dual scaling by GDP and by population for MEF parameters and suggest caution in applying universal scaling factors to all cities in a country.

Cities seek nuanced understanding of intraurban inequality in energy use, addressing both income and race, to inform equitable investment in climate actions. However, nationwide energy consumption surveys are limited (<6,000 samples in the United States), and utility-provided data are highly aggregated. Limited prior analyses suggest disparity in energy use intensity (EUI) by income is ∼25%, while racial disparities are not quantified nor unpacked from income. This paper, using new empirical fine spatial scale data covering all 200,000 households in two US cities, along with separating temperature-sensitive EUI, reveals intraurban EUI disparities up to a factor of five greater than previously known. We find 1) annual EUI disparity ratios of 1.27 and 1.66, comparing lowest- versus highest-income block groups (i.e., 27 and 66% higher), while previous literature indicated only ∼25% difference; 2) a racial effect distinct from income, wherein non-White block groups (highest quintile non-White percentage) in the lowest-income stratum reported up to a further ∼40% higher annual EUI than less diverse block groups, providing an empirical estimate of racial disparities; 3) separating temperature-sensitive EUI unmasked larger disparities, with heating–cooling electricity EUI of lowest-income block groups up to 2.67 times (167% greater) that of highest income, and high racial disparity within lowest-income strata wherein high non-White (>75%) population block groups report EUI up to 2.56 times (156% larger) that of majority White block groups; and 4) spatial scales of data aggregation impact inequality measures. Quadrant analyses are developed to guide spatial prioritization of energy investment for carbon mitigation and equity. These methods are potentially translatable to other cities and utilities.