The evolution of carbon footprint in the yangtze river delta city cluster during economic transition 2012-2015

China has been undergoing an industrial transformation, shifting from an energy-intensive growth pattern. As the most developed region in China, the Yangtze River Delta (YRD) city cluster is leading the industrial trans- formation. However, the impact of the industrial transformation on carbon footprints in the YRD cities is unclear. By a city-level environmentally extended input-output model, we quantify the carbon footprint of 41 cities in the YRD city cluster for 2012 and 2015 and capture the socioeconomic driving forces of the change by structural decomposition analysis (SDA). The results show that the carbon footprint in 41 YRD cities increased from 1179.4 Mt (14.8% of China’s total) to 1329.6 Mt (16.6%) over the period. More than 60% of the footprint concentrated on the 10 largest cities, and the construction sector made the largest contribution, especially in service-based megacities. The change of production structure drove down carbon footprints in YRD cities, except light in- dustry cities and service-based cities. The industrial transfers from the coastal to inland regions result in carbon leakage, where one-third of the carbon footprint is embodied in the trade. We also find the economic recession during the transition period decreased carbon emissions by 154.2 Mt in the YRD city cluster, where the value- added rate in the YRD cities declined over the transition period, especially in service-based cities. The study highlights the positive effects of industrial transformation on low carbon transition, despite being highly heterogenous for cities

Quantification and scenario analysis of CO2 emissions from the central heating supply system in China from 2006 to 2025

Policies associated with the central heating supply system affect the livelihoods of people in China. With the extensive consumption of energy for central heating, large quantities of CO2 emissions are produced each year. Coal-fired heating boiler plants are the primary source of emissions; however, thermal power plants are becoming much more prevalent, and gas-fired heating boiler plants remain uncommon. This study quantified the amount of CO2 emitted from the central heating supply system in China using a mass balance method with updated emission factors from the IPCC. Emissions increased from 189.04 Tg to 319.39 Tg between 2006 and 2015. From a spatial perspective, regions with larger central heating areas, durations and coverages produced more CO2 emissions. The central heating method depends on the level of electric power consumption, policies and regulations, and resource reserves at the local scale. Compared with the use of only coal-fired heating boiler plants to provide central heating, using thermal power plants and gas-fired heating boiler plants reduced CO2 emissions by 98.19 Tg in 2015 in China. A comparison of the CO2 emissions under various central heating scenarios showed that emissions will be 520.97 Tg, 308.79 Tg and 191.86 Tg for business as usual, positive and optimal scenarios through 2025, respectively. China has acknowledged the considerable potential for reducing central heating and will make efforts to pursue improved heating strategies in the future

Spatiotemporal evolution and drivers of carbon inequalities in urban agglomeration:An MLD-IDA inequality indicator decomposition

Increasing countries are articulating ambitious goals of carbon neutrality. However, large inequalities in regional emissions within a country may hinder progress toward a carbon–neutral future, as the unequal distribution of reduction responsibilities among regions could impair just transition and exacerbate uneven development, which necessitates an in-depth understanding of the mechanism of multi-scale carbon inequalities within country, region, and city. Yet, the evolution of carbon inequalities within urban agglomerations and the differences between adjacent or distant urban agglomerations have not been well understood, especially in countries undergoing rapid urbanization. Using the data of 89 cities in China’s Yangtze River Economic Belt (YREB) during 2006–2021, this paper quantifies carbon emissions inequality (CEI) at different scales in a systematic regional-urban agglomeration-city hierarchical structure. Then, under the integrated mean logarithmic deviation-logarithmic mean Divisia index (MLD-LMDI) decomposition framework, multi-scale CEIs are perfectly decomposed into six interrelated drivers, i.e., industrial emission structure, energy emission intensity, industrial energy mix, energy intensity, industrial structure, and economic development. The results show that economic development, energy intensity, and industrial energy mix disparities are the main determinants accounting for CEIs at different scales. The decreasing CEI in YREB is mainly due to the changes in industrial structure and economic development, while the energy intensity effect partially hinders the mitigation of CEI. In the upper reaches of the YREB, the energy intensity effect accounts for over 94% growth of CEI during 2006–2021, while the decline in CEIs in middle and lower reaches is primarily caused by the effects of industrial energy mix and industrial structure, respectively. Further spatial decomposition analysis reveals more refined city-level heterogeneous effects and emphasizes the prioritized emission reduction direction for each city. This paper offers implications for reducing carbon inequality and insights into coordinated carbon emissions mitigation at the regional level for a carbon–neutral future

Governance mechanisms for chronic disease diagnosis and treatment systems in the post-pandemic era

“Re-visits and drug renewal” is difficult for chronic disease patients during COVID-19 and will continue in the post-pandemic era. To overcome this dilemma, the scenario of chronic disease diagnosis and treatment systems was set, and an evolutionary game model participated by four stakeholder groups including physical medical institutions, medical service platforms, intelligent medical device providers, and chronic disease patients, was established. Ten possible evolutionary stabilization strategies (ESSs) with their mandatory conditions were found based on Lyapunov's first method. Taking cardiovascular and cerebrovascular diseases, the top 1 prevalent chronic disease, as a specific case context, and resorting to the MATLAB simulation, it is confirmed that several dual ESSs and four unique ESS circumstances exist, respectively, and the evolution direction is determined by initial conditions, while the evolution speed is determined by the values of the conditions based on the quantitative relations of benefits, costs, etc. Accordingly, four governance mechanisms were proposed. By their adjustment, the conditions along with their values can be interfered, and then the chronic disease diagnosis and treatment systems can be guided toward the desired direction, that is, toward the direction of countermeasure against the pandemic, government guidance, global trends of medical industry development, social welfare, and lifestyle innovation. The dilemma of “Re-visits and drug renewal” actually reflects the uneven distribution problem of qualified medical resources and the poor impact resistance capability of social medical service systems under mass public emergency. Human lifestyle even the way of working all over the world will get a spiral upgrade after experiencing COVID-19, such as consumption, and meeting, while medical habits react not so rapidly, especially for mid or aged chronic disease patients. We believe that telemedicine empowered by intelligent medical devices can benefit them and will be a global trend, governments and the four key stakeholders should act according to the governance mechanisms suggested here simultaneously toward novel social medical ecosystems for the post-pandemic era

Value of Immediate Heart Rate Alteration From Supine to Upright in Differential Diagnosis Between Vasovagal Syncope and Postural Tachycardia Syndrome in Children

Objectives: To explore the predictive value of immediate heart rate alteration from supine to upright in the differential diagnosis between vasovagal syncope (VVS) and postural tachycardia syndrome (POTS) in children.Materials and Methods: A total of 76 pediatric outpatients or inpatients who visited the Peking University First Hospital from July 2016 to November 2017 were recruited in the study. Among them, 52 patients were diagnosed with VVS and 24 patients were diagnosed with POTS. The differential diagnostic value of acceleration index (AI) and 30/15 ratio was evaluated by the receiver operating characteristic (ROC) curve. An external validation test was performed in another 46 patients.Results: Compared with the cases in the VVS group, patients in the POTS group had a significantly increased AI but a decreased 30/15 ratio (33.495 ± 8.472 vs. 23.440 ± 8.693, p < 0.001; 0.962 ± 0.067 vs. 1.025 ± 0.084, p = 0.002; respectively). The ROC curves showed that AI and 30/15 ratio were useful for differentiating POTS from VVS. A cut-off value of AI set at 28.180 yielded a sensitivity of 79.2% and a specificity of 73.1%. A cut-off value of 30/15 ratio set at 1.025 yielded a sensitivity of 87.5% and a specificity of 61.5%. A combined use of these two indices improved the sensitivity to 95.8% when either AI or 30/15 was used, and specificity to 80.8% with the use of both AI and 30/15 at the same diagnosis. The external validation test showed that the positive and negative predictive values of the AI and 30/15 ratio were 77.3 and 79.2%, and 72.0 and 81.0%, respectively. The positive predictive value increased to 87.5% when both the AI and 30/15 ratio cut-off values were used together.Conclusions: The AI and 30/15 ratio, which are easy to perform and non-invasive, have proper sensitivity and specificity to differentiate patients with POTS from those with VVS. The combination of these two indices significantly improves the predictive value

Climate change : strategies for mitigation and adaptation

The sustainability of life on Earth is under increasing threat due to humaninduced climate change. This perilous change in the Earth's climate is caused by increases in carbon dioxide and other greenhouse gases in the atmosphere, primarily due to emissions associated with burning fossil fuels. Over the next two to three decades, the effects of climate change, such as heatwaves, wildfires, droughts, storms, and floods, are expected to worsen, posing greater risks to human health and global stability. These trends call for the implementation of mitigation and adaptation strategies. Pollution and environmental degradation exacerbate existing problems and make people and nature more susceptible to the effects of climate change. In this review, we examine the current state of global climate change from different perspectives. We summarize evidence of climate change in Earth’s spheres, discuss emission pathways and drivers of climate change, and analyze the impact of climate change on environmental and human health. We also explore strategies for climate change mitigation and adaptation and highlight key challenges for reversing and adapting to global climate change

Soil respiration of a Moso bamboo forest significantly affected by gross ecosystem productivity and leaf area index in an extreme drought event

Moso bamboo has large potential to alleviate global warming through carbon sequestration. Since soil respiration (R-s) is a major source of CO2 emissions, we analyzed the dynamics of soil respiration (R-s) and its relation to environmental factors in a Moso bamboo (Phllostachys heterocycla cv. pubescens) forest to identify the relative importance of biotic and abiotic drivers of respiration. Annual average R(s )was 44.07 t CO2 ha(-1) a(-1) R-s correlated significantly with soil temperature (P <0.01), which explained 69.7% of the variation in R-s at a diurnal scale. Soil moisture was correlated significantly with R-s on a daily scale except not during winter, indicating it affected R-s. A model including both soil temperature and soil moisture explained 93.6% of seasonal variations in R-s. The relationship between R-s and soil temperature during a day showed a clear hysteresis. R-s was significantly and positively (P <0.01) related to gross ecosystem productivity and leaf area index, demonstrating the significance of biotic factors as crucial drivers of R-s.Peer reviewe

Silencing of PRDX2 Inhibits the Proliferation and Invasion of Non-Small Cell Lung Cancer Cells

Peroxiredoxin 2 (PRDX2), a member of the peroxiredoxin family of antioxidant enzymes, has been revealed to be an important player in cancer progression. However, the biological role of PRDX2 in the progression of non-small cell lung cancer (NSCLC) is poor reported. In the present study, the loss-of-function experiments were performed to investigate the specific role of PRDX2 in the growth and invasion of NSCLC. The results revealed that knockdown of PRDX2 by siRNA interference significantly suppressed the proliferation, migration, and invasion of A549 and H1299 cells, as well as diminished the activity of MMP9. Additionally, the decrease in PRDX2 expression significantly promoted apoptosis in NSCLC cells by downregulating expression of Bcl-2 and upregulating the expression of Bax, cleaved caspase 3 and cleaved caspase 9, but had no significant effect on the apoptosis of normal lung epithelial cells BEAS-2B. Moreover, PRDX2 inhibitor also inhibited the proliferation, migration, and invasion of A549 cells and promoted apoptosis. Further, our data demonstrated that silencing of PRDX2 markedly reduced the phosphorylation of Akt and mTOR and expression of downstream proteins Cyclin D1 and p70S6k. In conclusion, our findings indicate that PRDX2 exerts a prooncogenic role in the progression of NSCLC and might be a potential therapeutic target for NSCLC treatment

Corrigendum to <'The evolution of carbon footprint in the yangtze river delta city cluster during economic transition 2012–2015′>

Resources, Conservation & Recycling 181 (2022) 106,266 The authors regret to inform the readers of an error in a figure and the Supporting Information not uploaded. The color of legends of “Carbon intensity per GDP” and “Value-added rate” in Fig. 5 are reversed. Here is a corrected version of Fig. 5 (only the color of legends of “Carbon intensity per GDP” and “Value-added rate” in Fig. 5 have been modified): The authors regret . The authors would like to apologise for any inconvenience caused. DOI of original article: < https://doi.org/10.1016/j.resconrec.2022.106266