A grey systems analysis of water quantity allocation and quality protection in Xiamen, China

This study was designed to introduce the concepts of grey systems theory into water resources management as a means for accounting for uncertainty, and to conduct a grey systems analysis of the tradeoffs between meeting water quantity/quality objectives and maximizing economic income in the specific case of Xiamen, China. The literature on water resource systems analysis was reviewed to arrive at an understanding of how water quantity and quality problems were analyzed and incorporated, how uncertainty was accounted for, and what cases have been studied in water quantity and quality management. The literature revealed that ( 1) previous studies of water quantity and quality management were related to river or lake basins, and none was about a canal basin with strict water quality requirements; (2) none of the studies in China combined both quantity and quality problems in an optimization framework; and (3) no previous study attempted to communicate uncertain messages directly into optimization processes and solutions. This study has developed a grey linear programming (GLP) model for water quantity allocation and quality planning, and advanced a new solving approach which can effectively incorporate uncertain messages into the optimization framework. This method has been applied to water quantity and quality management in a water delivery canal in Xiamen, China. Results of the case study indicate that the derived decision schemes are feasible for the study area. When the canal water quality has precedence, the scheme for lower limit of objective function has to be adopted. Under this alternative, less cropping area, manure application and livestock numbers, and no fertilizer application are programmed. When agricultural income has precedence, the scheme for upper limit of objective function can be adopted. Under this alternative, more cropping areas, manure application and livestock numbers, and some fertilizer application are programmed. Therefore, decision makers can adjust the grey decision variables (including cropping area, manure and ferti1izer applications and livestock numbers) within their grey intervals according to the detailed situations. Reliability of the method has been proved through sensitivity tests of the impacts of pollutant loss constraints on agricultural income, the costs of reducing pollutant losses, the impacts of water quantity constraints on agricultural income, and the effects of grey inputs on grey outputs

A Risk-Based Interval Two-Stage Programming Model for Agricultural System Management under Uncertainty

Nonpoint source (NPS) pollution caused by agricultural activities is main reason that water quality in watershed becomes worse, even leading to deterioration. Moreover, pollution control is accompanied with revenue's fall for agricultural system. How to design and generate a cost-effective and environmentally friendly agricultural production pattern is a critical issue for local managers. In this study, a risk-based interval two-stage programming model (RBITSP) was developed. Compared to general ITSP model, significant contribution made by RBITSP model was that it emphasized importance of financial risk under various probabilistic levels, rather than only being concentrated on expected economic benefit, where risk is expressed as the probability of not meeting target profit under each individual scenario realization. This way effectively avoided solutions' inaccuracy caused by traditional expected objective function and generated a variety of solutions through adjusting weight coefficients, which reflected trade-off between system economy and reliability. A case study of agricultural production management with the Tai Lake watershed was used to demonstrate superiority of proposed model. Obtained results could be a base for designing land-structure adjustment patterns and farmland retirement schemes and realizing balance of system benefit, system-failure risk, and water-body protection

A Semi-Infinite Interval-Stochastic Risk Management Model for River Water Pollution Control under Uncertainty

In this study, a semi-infinite interval-stochastic risk management (SIRM) model is developed for river water pollution control, where various policy scenarios are explored in response to economic penalties due to randomness and functional intervals. SIRM can also control the variability of the recourse cost as well as capture the notion of risk in stochastic programming. Then, the SIRM model is applied to water pollution control of the Xiangxihe watershed. Tradeoffs between risks and benefits are evaluated, indicating any change in the targeted benefit and risk level would yield varied expected benefits. Results disclose that the uncertainty of system components and risk preference of decision makers have significant effects on the watershed's production generation pattern and pollutant control schemes as well as system benefit. Decision makers with risk-aversive attitude would accept a lower system benefit (with lower production level and pollutant discharge); a policy based on risk-neutral attitude would lead to a higher system benefit (with higher production level and pollutant discharge). The findings can facilitate the decision makers in identifying desired product generation plans in association with financial risk minimization and pollution mitigation.National Key Research Development Program of China (2016YFA0601502 and 2016YFC0502800), and the 111 Project (B14008

Spatiotemporal Changes of China's Carbon Emissions

Spatiotemporal changes in China's carbon emissions during the 11th and 12th Five‐Year Plan periods are quantified for the first time through a reconstructed nationwide high‐resolution gridded data set. The hot spots of carbon emissions in China have expanded by 28.5% (toward the west) in the north and shrunk by 18.7% in the south; meanwhile, the emission densities in North and South China have increased by 15.7% and 49.9%, respectively. This suggests a clear transition to a more intensive economic growth model in South China as a result of the energy conservation and emission reduction policies, while the expanded carbon hot spots in North China are mainly dominated by the Grand Western Development Program. The results also show that China's carbon emissions exhibit a typical spatially intensive, high‐emission pattern, which has undergone a slight relaxation (up to 3%) from 2007 to 2012 due to a typical urbanization process

A Risk-Based Interval Two-Stage Programming Model for Agricultural System Management under Uncertainty

Nonpoint source (NPS) pollution caused by agricultural activities is main reason that water quality in watershed becomes worse, even leading to deterioration. Moreover, pollution control is accompanied with revenue’s fall for agricultural system. How to design and generate a cost-effective and environmentally friendly agricultural production pattern is a critical issue for local managers. In this study, a risk-based interval two-stage programming model (RBITSP) was developed. Compared to general ITSP model, significant contribution made by RBITSP model was that it emphasized importance of financial risk under various probabilistic levels, rather than only being concentrated on expected economic benefit, where risk is expressed as the probability of not meeting target profit under each individual scenario realization. This way effectively avoided solutions’ inaccuracy caused by traditional expected objective function and generated a variety of solutions through adjusting weight coefficients, which reflected trade-off between system economy and reliability. A case study of agricultural production management with the Tai Lake watershed was used to demonstrate superiority of proposed model. Obtained results could be a base for designing land-structure adjustment patterns and farmland retirement schemes and realizing balance of system benefit, system-failure risk, and water-body protection

Valuation for an American Continuous-Installment Put Option on Bond under Vasicek Interest Rate Model

The valuation for an American continuous-installment put option on zero-coupon bond is considered by Kim's equations under a single factor model of the short-term interest rate, which follows the famous Vasicek model. In term of the price of this option, integral representations of both the optimal stopping and exercise boundaries are derived. A numerical method is used to approximate the optimal stopping and exercise boundaries by quadrature formulas. Numerical results and discussions are provided

Ecological network analysis for urban metabolism and carbon emissions based on input-output tables: A case study of Guangdong province

Global warming has received more and more attention in recent years for its inevitable influence on population, species, soil, ocean, water and so on. It is essential to investigate the urban metabolism of carbon emissions which is a main cause of global warming and most of it occurs in the process of production and living in urban areas. In this paper, a carbon emission metabolic network is established to explore the emission reduction strategies by modeling carbon dioxide flows and identifying the mutual relationships based on the input-output analysis. Specifically, Eff-Lorenz curve derived from the painting of Lorenz curve is developed to compare the efficiency of carbon emissions from different sectors. The newly developed method has been applied to Guangdong province to demonstrate its availability and benefit. It is revealed that carbon emissions mainly concentrated in the secondary and tertiary industries with electric power generation, manufacturing industry, domestic consumption and transportation ranking at the top. The competition relationship reveals good interactions in terms of emission reduction while a mutualism relationship provides effective pathways to mitigate carbon emissions between pairwise sectors simultaneously. In Guangdong province, upgrading the clean combustion technology in electric power generation and energy extraction sectors would drive other sectors to cut emissions and adjusting the production structure of the construction sector also contribute to achieve this goal. The results are expected to provide corresponding and holistic reference for decision makers to develop the mitigation policies

Modeling of Water Quality, Quantity, and Sustainability

This article has no abstract.Published versio

Planning an Agricultural Water Resources Management System: A Two-Stage Stochastic Fractional Programming Model

Irrigation water management is crucial for agricultural production and livelihood security in many regions and countries throughout the world. In this study, a two-stage stochastic fractional programming (TSFP) method is developed for planning an agricultural water resources management system under uncertainty. TSFP can provide an effective linkage between conflicting economic benefits and the associated penalties; it can also balance conflicting objectives and maximize the system marginal benefit with per unit of input under uncertainty. The developed TSFP method is applied to a real case of agricultural water resources management of the Zhangweinan River Basin China, which is one of the main food and cotton producing regions in north China and faces serious water shortage. The results demonstrate that the TSFP model is advantageous in balancing conflicting objectives and reflecting complicated relationships among multiple system factors. Results also indicate that, under the optimized irrigation target, the optimized water allocation rate of Minyou Channel and Zhangnan Channel are 57.3% and 42.7%, respectively, which adapts the changes in the actual agricultural water resources management problem. Compared with the inexact two-stage water management (ITSP) method, TSFP could more effectively address the sustainable water management problem, provide more information regarding tradeoffs between multiple input factors and system benefits, and help the water managers maintain sustainable water resources development of the Zhangweinan River Basin