To reduce flood disasters and optimize of the comprehensive benefit of the water basin, the allocation of regional flood drainage rights is of great significance. Using the “top-down” allocation mode, we consider the influence of the social, economic, and ecological environments, flood drainage demand and efficiency, and other factors on the allocation of flood drainage rights. A bi-level multi-objective programming model from the perspective of fairness and efficiency is established for the allocation. The Sunan Canal is taken as a typical case study. The model is solved by the multi-objective optimal allocation method and the master–slave hierarchical interactive iteration algorithm. After three iterations of the initial solution, the allocation of flood drainage rights in six flood control regions finally reach an effective state. The results of the model were compared with results based on historical allocation principles, showing that the bi-level multi-objective programming model, based on the principles of fairness and efficiency, is more in line with the current social and economic development of the canal. In view of the institutional background of water resources management in China and the flood drainage pressure faced by various regions, the allocation of flood drainage rights should be comprehensively considered in combination with various factors, and the market mechanism should be utilized to optimize the allocation.
Purpose -The purpose of this paper is to analyze the effects of public investment in agricultural R&D and extension on broadacre farming productivity in Australia. Design/methodology/approach -An autoregressive integrated moving average (ARIMA) regression model is applied to estimate the effects of public investment in agricultural R&D and extension on Australian braodacre productivity. Findings -The study reveals that public investment in agricultural R&D and extension has contributed almost two-thirds of average annual broadacre productivity growth between 1952-1953 and 2006-2007, the average internal rate of return to public investment in agricultural R&D and extension was 28.4 and 47.5 per cent a year, respectively, and overseas spill-ins is an important source of domestic agricultural productivity growth. Practical implications -Policy implications: the findings suggest that increasing public investment in agricultural R&D and extension and maintaining agricultural R&D policy stability are equally important to have a sustained long-term agricultural productivity growth, and maintaining an open trade and investment regime is important to benefit from foreign knowledge spillovers which is especially important for developing countries. Originality/value -This paper contributes to the existing literature by employing more sophisticated econometric techniques with an extended data set for the period from 1952-1953 to 2006-2007. The study separates the contribution of public R&D investment and the extension investment, and also takes into account the contribution of overseas public investment on the TFP growth in the Australian broadacre sector.
To minimize losses caused by flooding of areas in a river basin, flood risk management may sacrifice the interests of some areas. Because of regional differences in natural and urban conditions, rankings of the urgencies of flood drainage rights allocations in different regions are of great practical significance to the realization of optimal allocations and reduction of damages. Based on the “pressure–state–response” (PSR) framework, this study designed an index system of flood drainage rights allocations in river basins for the comprehensive consideration of the different attributes of regional societies, environments, and technologies, as well as the differences in the quality of technical management and in the levels of social and economic development. A Pythagoras fuzzy TOPSIS method was used to evaluate the urgencies and determine the management of allocations in different areas. Eight cities in Jiangsu Province in the Huai River Basin were selected as the research objects. The results showed that pressure factors played dominant roles in the degrees of urgency. Among the cities, Nantong had the highest degree, followed by Taizhou, whereas Lianyungang had the lowest. The degrees in the central region of Jiangsu were higher than in the northern region.
Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS) data has the shortcomings of discontinuous and pixel saturation effect. It was also incompatible with the Soumi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP/VIIRS) data. In view those shortcomings, this research put forward the WorldPop and the enhanced vegetation index (EVI) adjusted nighttime light (WEANTL) using EVI and WorldPop data to achieve intercalibration and saturation correction of DMSP/OLS data. A long time series of nighttime light images of china from 2001 to 2018 was constructed by fitting the DMSP/OLS data and NPP/VIIRS data. Corrected nighttime light images were examined to discuss the estimation ability of gross domestic product (GDP) and electric power consumption (EPC) on national and provincial scales, respectively. The results indicated that, (1) after correction, the nighttime light (NTL) data can guarantee the growth trend on national and regional scales, and the interannual volatility of the corrected NTL data is lower than that of the uncorrected NTL data; (2) on the national scale, compared with the established model of NTL data and GDP data (NTL-GDP), the determination coefficient (R2) and the mean absolute relative error (MARE) are 0.981 and 8.518%. The R2 and MARE of the established model of NTL data and EPC data (NTL-EPC) were 0.990 and 4.655%; (3) on the provincial scale, the R2 and MARE of NTL-GDP model under the provincial units are 0.7386 and 38.599%. The R2 value and MARE of NTL-EPC model are 0.8927 and 29.319%; (4) on the provincial scale, the R2 and MARE of NTL-GDP model on time series are 0.9667 and 10.877%. The R2 and MARE of NTL-GDP model on time series are 0.9720 and 6.435%; the established TNL-GDP and TNL-EPC models with 30 provinces data all passed the F-test at the 0.001 level; (5) the prediction accuracy of GDP and EPC on time series was nearly 100%. Therefore, the correction method provided in this research can be applied in estimating the GDP and EPC on multiple scales reliably and accurately.
In B2C sharing economy, businesses and individual customers temporally trade product-based services through sharing platform. Life cycle management provides a good insight to assess the environmental benefits in such sharing environment. This paper discusses how information is generated during consumption and how information sharing facilitates life cycle value co-creation to enrich environmental benefits eventually. An in-depth case study is conducted to examine Mobike’s practices as a bike sharing case in China. It is concluded that customers join with businesses in value co-creation for service sharing in B2C sharing economy. Through well-established platforms, various types of information are co-created during consumption can be shared and serve as valuable input for life cycle management to co-create more values. Ultimately, environmental benefits can be expanded and enriched.
Abstract-Build multi-period stochastic inventory model of tobacco commercial enterprise and determine optimal inventory stock and order point under maximum expected profit. Case studies have shown that this model can optimize tobacco commercial enterprise inventory, so as to achieve the effect of inventory cost optimization.
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