Search

Abstract: In the last decade, two scientific tools have been extensively used worldwide to measure the human impact on nature: ecological footprint (EF) and emergy analysis (EA). Papers trying to combine the strong points of EF and EA, and obtain more accurate results have appeared in scientific literature, in which Zhao’s et al. (2005) approach is an important one. Unfortunately, some weak points of the original methods still remain on the new approaches proposed. The aim of this present work is to discuss some weak points found in Zhao’s approach, trying to overcome them through a new approach called emergetic ecological footprint (EEF). The main difference between Zhao’s approach and EEF is that the last one accounted for the internal storage of capital natural in the biocapacity calculation. Besides that, soil loss and water for human consume were considered as additional categories in the footprint calculation. After discussing it through comparisons with other approaches, EEF was used to assess Peru as a case study, resulting in a biocapacity of 51.76ghacapita−1 and a footprint of 12.23ghacapita−1, with 2004 data; that resulted in an ecological surplus of 39.53ghacapita−1. The load capacity factor obtained was 4.23, meaning that Peru can support a population 4.23 times bigger considering the life style of 2004. The main limitations of the EEF are: (i) it is impossible to make comparisons between the biocapacity and footprint for each category; (ii) a need for a handbook with emergy intensity factors with good quality. On the other hand, the main positive points are: (i) its easiness of application in global and national scales; (ii) its final indicators account for all the previous energy (or emergy) used to make something; (iii) internal natural capital storage was accounted for in the biocapacity calculation, which can be a valid step towards the evaluation and assess of services provided by nature. [Copyright &y& Elsevier]

• A predicting model for the long-term epidemic trend of COVID-19 by using LSTM with rolling update mechanism is proposed. • The 150-days ahead epidemic trend of COVID-19 in Russia, Peru and Iran are estimated by our proposed model. • The results provide that the epidemic of Peru will end in early December. • The number of daily cases in Russia and Iran is expected to fall below 2000 and 1000 by mid-November and early December. • By introducing diffusion index, the effectiveness of preventive measures taken by the government are analyzed. The COVID-19 outbreak in late December 2019 is still spreading rapidly in many countries and regions around the world. It is thus urgent to predict the development and spread of the epidemic. In this paper, we have developed a forecasting model of COVID-19 by using a deep learning method with rolling update mechanism based on the epidemical data provided by Johns Hopkins University. First, as traditional epidemical models use the accumulative confirmed cases for training, it can only predict a rising trend of the epidemic and cannot predict when the epidemic will decline or end, an improved model is built based on long short-term memory (LSTM) with daily confirmed cases training set. Second, considering the existing forecasting model based on LSTM can only predict the epidemic trend within the next 30 days accurately, the rolling update mechanism is embedded with LSTM for long-term projections. Third, by introducing Diffusion Index (DI), the effectiveness of preventive measures like social isolation and lockdown on the spread of COVID-19 is analyzed in our novel research. The trends of the epidemic in 150 days ahead are modeled for Russia, Peru and Iran, three countries on different continents. Under our estimation, the current epidemic in Peru is predicted to continue until November 2020. The number of positive cases per day in Iran is expected to fall below 1000 by mid-November, with a gradual downward trend expected after several smaller peaks from July to September, while there will still be more than 2000 increase by early December in Russia. Moreover, our study highlights the importance of preventive measures which have been taken by the government, which shows that the strict controlment can significantly reduce the spread of COVID-19. [ABSTRACT FROM AUTHOR]