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China is undergoing rapid urbanization, enlarging the construction industry, greatly expanding built-up land, and generating substantial carbon emissions. We calculated both the direct and indirect carbon emissions from energy consumption (anthropogenic emissions) in the construction sector and analyzed built-up land expansion and carbon storage losses from the terrestrial ecosystem. According to our study, the total anthropogenic carbon emissions from the construction sector increased from 3,905×10(4) to 103,721.17×10(4) t from 1995 to 2010, representing 27.87%-34.31% of the total carbon emissions from energy consumption in China. Indirect carbon emissions from other industrial sectors induced by the construction sector represented approximately 97% of the total anthropogenic carbon emissions of the sector. These emissions were mainly concentrated in seven upstream industry sectors. Based on our assumptions, built-up land expansion caused 3704.84×10(4) t of carbon storage loss from vegetation between 1995 and 2010. Cropland was the main built-up land expansion type across all regions. The study shows great regional differences. Coastal regions showed dramatic built-up land expansion, greater carbon storage losses from vegetation, and greater anthropogenic carbon emissions. These regional differences were the most obvious in East China followed by Midsouth China. These regions are under pressure for strong carbon emissions reduction.

The coronavirus disease 2019 has exposed and, to some degree, exacerbated social inequity in the United States. This study reveals the correlation between demographic and socioeconomic variables and home-dwelling time records derived from large-scale mobile phone location tracking data at the U.S. census block group (CBG) level in the twelve most populated Metropolitan Statistical Areas (MSAs) and further investigates the contribution of these variables to the disparity in home-dwelling time that reflects the compliance with stay-at-home orders via machine learning approaches. We find statistically significant correlations between the increase in home-dwelling time (r HDT ) and variables that describe economic status in all MSAs, which is further confirmed by the optimized random forest models, because median household income and percentage of high income are the two most important variables in predicting r HDT : The partial dependence between median household income and r HDT reveals that the contribution of income to r HDT is place dependent, nonlinear, and different given varying income intervals. Our study reveals the luxury nature of stay-at-home orders with which lower income groups cannot afford to comply. Such disparity in responses under stay-at-home orders reflects the long-standing social inequity issues in the United States, potentially causing unequal exposure to COVID-19 that disproportionately affects vulnerable populations. We must confront systemic social inequity issues and call for a high-priority assessment of the long-term impact of COVID-19 on geographically and socially disadvantaged groups.

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In this study, we investigate the potential driving factors that lead to the disparity in the time-series of home dwell time in a data-driven manner, aiming to provide fundamental knowledge that benefits policy-making for better mitigation strategies of future pandemics. Taking Metro Atlanta as a study case, we perform a trend-driven analysis by conducting Kmeans time-series clustering using fine-grained home dwell time records from SafeGraph. Furthermore, we apply ANOVA (Analysis of Variance) coupled with post-hoc Tukey’s test to assess the statistical difference in sixteen recoded demographic/socioeconomic variables (from ACS 2014–2018 estimates) among the identified time-series clusters. We find that demographic/socioeconomic variables can explain the disparity in home dwell time in response to the stay-at-home order, which potentially leads to disparate exposures to the risk from the COVID-19. The results further suggest that socially disadvantaged groups are less likely to follow the order to stay at home, pointing out the extensive gaps in the effectiveness of social distancing measures that exist between socially disadvantaged groups and others. Our study reveals that the long-standing inequity issue in the U.S. stands in the way of the effective implementation of social distancing measures.

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Global warming, which is mainly caused by greenhouse gases, can greatly aggravate land degradation; therefore, the examination of the NEP (net ecosystem productivity) and the analysis of its response to climate change are very critical for understanding carbon cycling. Based on Moderate Resolution Imaging Spectroradiometer data, meteorological data, and soil organic carbon data, this study examined the NEP from 2000 to 2013 and investigated how ongoing climate change affects the NEP. The study results indicate that the terrestrial ecosystems in China generally act as net carbon sinks with increasing NEP values. The western inland region and part of northeast China mainly act as carbon sources, with the NEP exhibiting an increasing trend, whereas the other regions mainly act as carbon sinks, with the NEP showing a decreasing trend across large areas of southern China, where the most obvious land degradation occurs. Homogeneity and heterogeneity co‐occur. The general pattern is that ecosystems with high biomass usually have a high NEP value, acting as high carbon sinks in relatively wet and warm environments, but have a low value and even act as carbon sources in dry and cold environments. Both moderate precipitation and temperature are essential in increasing the NEP, whereas lower precipitation and temperatures might have negative effects. Heterogeneity also widely breaks up the general pattern. Temporally, more NEP grids were positively correlated with changes in temperature and showed stronger correlation coefficients with temperature than with precipitation, but the grids showing a significant correlation with these factors accounted for only a small proportion of the total for both precipitation and temperature.