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Abstract: For urban economies, an ecological endowment embodiment analysis has to be supported by endowment intensities at both the international and domestic scales to reflect the international and domestic imports of increasing importance. A three-scale input–output modeling for an urban economy to give nine categories of embodiment fluxes is presented in this paper by a case study on the carbon dioxide emissions by the Beijing economy in 2007, based on the carbon intensities for the average world and national economies. The total direct emissions are estimated at 1.03E+08t, in which 91.61% is energy-related emissions. By the modeling, emissions embodied in fixed capital formation amount to 7.20E+07t, emissions embodied in household consumption are 1.58 times those in government consumption, and emissions in gross capital formation are 14.93% more than those in gross consumption. As a net exporter of carbon emissions, Beijing exports 5.21E+08t carbon embodied in foreign imported commodities and 1.06E+08t in domestic imported commodities, while emissions embodied in foreign and domestic imported commodities are 3.34E+07 and 1.75E+08t respectively. The algorithm presented in this study is applicable to the embodiment analysis of other environmental resources for regional economies characteristic of multi-scales. [Copyright &y& Elsevier]

Abstract: Cities as superorganisms confront disturbances from their metabolic processes, including large metabolic fluxes, low stocks of resources and products, and a low efficiency of the urban material metabolism. Based on ecological thermodynamics, an indicator system is established in this paper to evaluate the fluxes, stocks, and effiency of the urban material metabolism using emergy analysis. Also, a new model for the urban material metabolism is proposed to define the production possibility curve using a wealth index (WI) and an ecological effiency index (EEI). Then, six large Chinese cities including Beijing, Shanghai, Tianjin, Chongqing, Guangzhou, and Shenzhen are selected as typical cases to validate the proposed model. The results show that Shenzhen has the highest metabolic capacity, followed by Beijing, Shanghai, Guangzhou, Tianjin, and Chongqing. It is also reflected that different urban material metabolisms of the six cities are resulted from the varied regional metabolic capacities, thus providing insights into how cities improve their metabolic capacities. Meanwhile, Shenzhen has the highest WI and EEI, Chongqing has the lowest WI and EEI, and Beijing, Shanghai, Tianjin, and Guangzhou has low WI and high EEI. It is also shown that the sustainable operation of the urban material metabolism reflects the mutualism and symbiosis between socioeconomic development and ecological environment protection. [Copyright &y& Elsevier]

Abstract: Due to the increasingly serious ecological degradation of river systems, the river health research has attracted more and more attention of the researchers and decision-makers. This paper aims to integrate the fuzzy assessment method with analytic hierarchy process to establish the integrative fuzzy hierarchical assessment model, thus combing qualitative analysis and quantitative assessment and overcoming the disadvantages of subjectivity of the previous evaluation methods. The results show that the proposed assessment method is able to reveal how the river system is disturbed by human activities. Finally, the integrative fuzzy hierarchical method is validated and applied to the case study of Yong River in Ningbo City, China. [Copyright &y& Elsevier]

• Mathematical model for coronavirus disease that fits well the spread in China. • New θ -SEIHRD model taking into account undetected infections. • Validation of the model with the reported data on China. • Estimation of errors when identifying parameters at early stages of the pandemic. • Different scenarios to show the impact of undetected cases on the pandemic. In this paper we develop a mathematical model for the spread of the coronavirus disease 2019 (COVID-19). It is a new θ -SEIHRD model (not a SIR, SEIR or other general purpose model), which takes into account the known special characteristics of this disease, as the existence of infectious undetected cases and the different sanitary and infectiousness conditions of hospitalized people. In particular, it includes a novel approach that considers the fraction θ of detected cases over the real total infected cases, which allows to study the importance of this ratio on the impact of COVID-19. The model is also able to estimate the needs of beds in hospitals. It is complex enough to capture the most important effects, but also simple enough to allow an affordable identification of its parameters, using the data that authorities report on this pandemic. We study the particular case of China (including Chinese Mainland, Macao, Hong-Kong and Taiwan, as done by the World Health Organization in its reports on COVID-19), the country spreading the disease, and use its reported data to identify the model parameters, which can be of interest for estimating the spread of COVID-19 in other countries. We show a good agreement between the reported data and the estimations given by our model. We also study the behavior of the outputs returned by our model when considering incomplete reported data (by truncating them at some dates before and after the peak of daily reported cases). By comparing those results, we can estimate the error produced by the model when identifying the parameters at early stages of the pandemic. Finally, taking into account the advantages of the novelties introduced by our model, we study different scenarios to show how different values of the percentage of detected cases would have changed the global magnitude of COVID-19 in China, which can be of interest for policy makers. [ABSTRACT FROM AUTHOR]