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Urban areas consume more than 66% of the world's energy and generate more than 70% of global greenhouse gas (GHG) emissions. With the world's population expected to reach 10 billion by 2100, and with nearly 90% of people living in urban areas, a critical question for planetary sustainability is how the size of cities affects energy use and carbon dioxide (CO2) emissions. Are urban agglomerations more energy and emissions efficient than smaller cities? Does urban agglomeration exhibit gains from economies of scale concerning emissions? Here, we examine the relationship between urban agglomeration and CO2 emissions for urban agglomeration in the Yangtze River Delta in China using a STIRPAT (Stochastic Impacts by Regression on Population, Affluence and Technology) model considering the spatial effects. Also, it examines the influence of economic development, industrial structure, opening-up level, and technology on carbon emissions by exploring the spatial agglomeration and spillover effects. Our major finding is that urban size has had a negative correlation to carbon emissions, demonstrating that urban agglomeration is more emissions efficient. In addition, our results showed that carbon emission driving factors, such as technology progress, opening-up, population, have spatial dependence and spatial agglomeration effects. Technology progress, opening-up level, and population have a spatial spillover effect on carbon emissions. It means a city's carbon emissions are not only influenced by its own factors but also have an impact on neighboring cities. Therefore, cross-city or urban agglomeration policy, and actions of reducing carbon emissions, are necessary, whilst also developing a low-carbon economy by increasing the proportion of high-tech industry through technological progress and developing vigorous resource-saving and an environmentally friendly tertiary industry.

Background: Telemedicine use has expanded substantially in recent years. Studies evaluating the impact of telemedicine modalities on downstream office visits have demonstrated mixed results. Introduction: We evaluated insurance claims of a large commercial payer, Blue Cross Blue Shield of Michigan (BCBSM), to assess the frequency of follow-up visits following encounters initiated via telemedicine versus in-person. Materials and Methods: We used the BCBSM claim-level data set (2011)(2012)(2013)(2014)(2015)(2016)(2017) to assess encounters in the following places of service: hospital outpatient, doctor's office, patient's home, or psychiatric daycare facility. We identified the primary diagnostic category for 30-day episodes of care using clinical classifications software (CCS) and multilevel clinical classifications software (ML-CCS). Our intervention group consisted of episodes initiated via telemedicine; our control group consisted of episodes initiated in-person. Our primary outcome was the percentage of 30-day episodes with a related visit (encounters occurring within the same period and CCS categories) across CCS categories. Our secondary outcome was the mean related visit rate. Results: The final data set included 4,982,456 patients and 68,148,070 claims, of which 53,853 were telemedicine related. Many episodes did not have related visits (the mean related visit rate was 16%). Telemedicine visits had a higher frequency of related visits across all CCS categories. Discussion: Episodes of care initiated via telemedicine more frequently generate related visits within a 30-day period. This increased health care utilization could represent excessive care or could reflect expanded access to care. Conclusion: Further research should explore the cause of this increased utilization and potential unintended consequences.