Xueting Zeng

Modeling Water Trading under Uncertainty for Supporting Water Resources Management in an Arid Region

AbstractIn this study, a joint-probabilistic interval multistage programming (JIMP) method is developed for planning water resources management under uncertainty. The JIMP method can tackle uncertainties presented in terms of interval parameters in objective function and constraints, in addition to random variables in the left and right-hand sides of constraints. It can also reflect the dynamics in terms of decisions for water resources allocation through transactions at discrete points of a complete scenario set over a multistage context. Moreover, the JIMP method can be used for analyzing various policy scenarios that are associated with different levels of economic consequences when the promised water-allocation targets are violated. The JIMP method is applied to a real case of planning water trading for supporting the regional sustainable development of the Kaidu-Qongque River basin, which is one of the most arid regions of China. Monte Carlo simulation is introduced into the JIMP framework for evalua...

Population-production-pollution nexus based air pollution management model for alleviating the atmospheric crisis in Beijing, China

In recent years, increscent emissions in the city of Beijing due to expanded population, accelerated industrialization and inter-regional pollutant transportation have led to hazardous atmospheric pollution issues. Although a number of anthropogenic control measures have been put into use, frequent/severe haze events have still challenged regional governments. In this study, a hybrid population-production-pollution nexus model (PPP) is proposed for air pollution management and air quality planning (AMP) with the aim to coordinate human activities and environmental protection. A fuzzy-stochastic mixed quadratic programming method (FSQ) is developed and introduced into a PPP for tackling atmospheric pollution issues with uncertainties. Based on the contribution of an index of population-production-pollution, a hybrid PPP-based AMP model that considers employment structure, industrial layout pattern, production mode, pollutant purification efficiency and a pollution mitigation scheme have been applied in Beijing. Results of the adjustment of employment structure, pollution mitigation scheme, and green gross domestic product under various environmental regulation scenarios are obtained and analyzed. This study can facilitate the identification of optimized policies for alleviating population-production-emission conflict in the study region, as well as ameliorating the hazardous air pollution crisis at an urban level.

Modeling of Water Resources Allocation and Water Quality Management for Supporting Regional Sustainability under Uncertainty in an Arid Region

In this study, a scenario-based interval-stochastic fraticle optimization with Laplace criterion (SISFL) method is developed for sustainable water resources allocation and water quality management (WAQM) under multiple uncertainties. SISFL can tackle uncertainties presented as interval parameters and probability distributions; meanwhile, it can also quantify artificial fuzziness such as risk-averse attitude in a decision-making issue. Besides, it can reflect random scenario occurrence under the supposition of no data available. The developed method is applied to a real case of water resources allocation and water quality management in the Kaidu-kongque River Basin, where encounter serve water deficit and water quality degradation simultaneously in Northwest China. Results of water allocation pattern, pollution mitigation scheme, and system benefit under various scenarios are analyzed. The tradeoff between economic activity and water-environment protection with interval necessity levels and Laplace criterions can support policymakers generating an effective and robust manner associated with risk control for WAQM under multiple uncertainties. These discoveries avail local policymakers gain insight into the capacity planning of water-environment to satisfy the basin’s integrity of socio-economic development and eco-environmental sustainability.

Risk analysis for water resources management under dual uncertainties through factorial analysis and fuzzy random value-at-risk

Water resources systems are associated with a variety of complexities and uncertainties due to socio-economic and hydro-environmental impacts. Such complexities and uncertainties lead to challenges in evaluating the water resources management alternatives and the associated risks. In this study, the factorial analysis and fuzzy random value-at-risk are incorporated into a two-stage stochastic programming framework, leading to a factorial-based two-stage programming with fuzzy random value-at-risk (FTSPF). The proposed FTSPF approach aims to reveal the impacts of uncertainty parameters on water resources management strategies and the corresponding risks. In detail, fuzzy random value-at-risk is to reflect the potential risk about financial cost under dual uncertainties, while a multi-level factorial design approach is used to reveal the interaction between feasibility degrees and risk levels, as well as the relationships (including curvilinear relationship) between these factors and the responses. The application of water resources system planning makes it possible to balance the satisfaction of system benefit, the risk levels of penalty and the feasibility degrees of constraints. The results indicate that decision makers would pay more attention to the tradeoffs between the system benefit and feasibility degree, and the water allocation for agricultural section contributes most to control the financial loss of water. Moreover, FTSPF can generate a higher system benefit and more alternatives under various risk levels. Therefore, FTSPF could provide more useful information for enabling water managers to identify desired policies with maximized system benefit under different system-feasibility degrees and risk levels.

A stochastic rough-approximation water management model for supporting sustainable water-environment strategies in an irrigation district of arid region

In this study, a stochastic rough-approximation water management model (SRAWM) associated with optimistic and pessimistic options is proposed for supporting regional sustainability in an irrigation system (IS) of an arid region with uncertain information. SRAWM can not only handle conventional stochastic variations in objective functions or constraints, but also tackle objective and subjective (i.e., risk performance of the decision maker) fuzziness through rough-approximation model based on measure Me. The developed model would be applied to a real case study of an irrigation district (ID) in Kaidu-kongque River Basin, China, which is encountering challenges in economic development and a serious environmental crisis (e.g., drought, water deficit, land deterioration, stalinization, soil erosion and water pollution) synchronously. Simulation technical (i.e., support vector regression) is put into SRAWM framework to reflect dynamic prediction of water demand in the future. Results of optimized irrigation area, water allocation, water deficit, pollution reduction, water and soil erosion and system benefit under various water-environmental policies (corresponding to various ecological effects) are obtained. Tradeoffs between ecological and irrigative water usages can facilitate the local decision makers rectifying the current irrigation patterns and ecological protection polices. Moreover, compromises between systemic benefit and failure risk can help policymakers to generate a robust risk-control plan under uncertainties. These detections are beneficial to achieve conjunctive goals of socio-economic development and eco-environmental sustainability in such an arid IS.

Development of a fuzzy-stochastic programming with Green Z-score criterion method for planning water resources systems with a trading mechanism

This study developed a fuzzy-stochastic programming with Green Z-score criterion (FSGZ) method for water resources allocation and water quality management with a trading-mechanism (WAQT) under uncertainties. FSGZ can handle uncertainties expressed as probability distributions, and it can also quantify objective/subjective fuzziness in the decision-making process. Risk-averse attitudes and robustness coefficient are joined to express the relationship between the expected target and outcome under various risk preferences of decision makers and systemic robustness. The developed method is applied to a real-world case of WAQT in the Kaidu-Kongque River Basin in northwest China, where an effective mechanism (e.g., market trading) to simultaneously confront severely diminished water availability and degraded water quality is required. Results of water transaction amounts, water allocation patterns, pollution mitigation schemes, and system benefits under various scenarios are analyzed, which indicate that a trading-mechanism is a more sustainable method to manage water-environment crisis in the study region. Additionally, consideration of anthropogenic (e.g., a risk-averse attitude) and systemic factors (e.g., the robustness coefficient) can support the generation of a robust plan associated with risk control for WAQT when uncertainty is present. These findings assist local policy and decision makers to gain insights into water-environment capacity planning to balance the basin’s social and economic growth with protecting the region’s ecosystems.

Analyzing the carbon mitigation potential of tradable green certificates based on a TGC-FFSRO model: A case study in the Beijing-Tianjin-Hebei region, China

Contradictions of increasing carbon mitigation pressure and electricity demand have been aggravated significantly. A heavy emphasis is placed on analyzing the carbon mitigation potential of electric energy systems via tradable green certificates (TGC). This study proposes a tradable green certificate (TGC)-fractional fuzzy stochastic robust optimization (FFSRO) model through integrating fuzzy possibilistic, two-stage stochastic and stochastic robust programming techniques into a linear fractional programming framework. The framework can address uncertainties expressed as stochastic and fuzzy sets, and effectively deal with issues of multi-objective tradeoffs between the economy and environment. The proposed model is applied to the major economic center of China, the Beijing-Tianjin-Hebei region. The generated results of proposed model indicate that a TGC mechanism is a cost-effective pathway to cope with carbon reduction and support the sustainable development pathway of electric energy systems. In detail, it can: (i) effectively promote renewable power development and reduce fossil fuel use; (ii) lead to higher CO2 mitigation potential than non-TGC mechanism; and (iii) greatly alleviate financial pressure on the government to provide renewable energy subsidies. The TGC-FFSRO model can provide a scientific basis for making related management decisions of electric energy systems.

A hybrid land-water-environment model for identification of ecological effect and risk under uncertain meteorological precipitation in an agroforestry ecosystem

In this study, a hybrid land-water-environment (LWE) model is developed for identifying ecological effect and risk under uncertain precipitation in an agroforestry ecosystem. A simulation-based fuzzy-stochastic programming with risk analysis (SFSR) method is used into LWE model to reflect the meteorological impacts; meanwhile, it also can quantify artificial fuzziness (e.g., risk attitude of policymaker) and natural vagueness (e.g., ecological function) in decision-making. The developed LWE model with SFSR method is applied to a practical agroforestry ecosystem in China. Results of optimized planting scale, irrigative water schedule, pollution mitigation scheme, and system benefit under changed rainfall, precise risk-adoption and vague ecological function are obtained; meanwhile their corresponding ecological effects and risks are analyzed. It found that current LWE plans could generate massive water deficits (e.g., 23.22×106m3 in crop irrigation and 26.32×106m3 in forest protection at highest) due to over-cultivation and excessive pollution discharges (e.g., the highest excessive TP and TN discharges would reach 460.64 and 15.30×103 ton) due to irrational fertilization, which would increase regional ecological risks. In addition, fifteen scenarios associated with withdrawing cultivation and recovering forest based on regional environment heterogeneity (such as soil types) have been discussed to adjust current agriculture-environment policies. It found that, the excessive pollution discharges (TN and TP) could be reduced 12.95% and 18.32% at highest through ecological expansions, which would generate higher system benefits than that without withdrawing farmland and recovering forest. All above can facilitate local policymakers to modulate a comprehensive LWE with more sustainable and robust manners, achieving regional harmony between socio-economy and eco-environment.