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To improve the capabilities of conventional methodologies in supporting greenhouse gas (GHG) emission mitigation from food production under uncertain conditions, an integrated approach was developed through incorporating copula-based violation risk analysis into a general life cycle analysis (LCA) framework. This approach strengthened the applicability of LCA in terms of uncertainty and risk reflections for food systems in the background of urbanization under multiple GHG emission targets. In detail, such an approach can (a) reflect uncertainties of food production and consumption processes, (b) tackle joint probability of two correlated variables (i.e., GHG emissions and economic benefits) through the employment of Archimedean copula (i.e., Gumbel, Frank, and Clayton copula) and Gaussian copula, and (c) assess violation risk of GHG emissions in food production considering the trend of future urbanization. A case study was proposed to illustrate application of the approach in Dalian City, China. Considering uncertainties of dietary patterns in 2020, two scenarios (scenarios of baseline and increase) were proposed according to dietary structures of urban and rural residents. In detail, the scenario of baseline represented the recent dietary pattern of Dalian in 2015. Scenario of increase indicated 50% rise in beef, mutton, and milk compared with the dietary pattern under the scenario of baseline. The results showed that based on the current agenda upon GHG emission intensity in China, violation risks under the scenario of baseline would be more prominent than the violation risks under the scenario of increase. [ABSTRACT FROM AUTHOR]

Due to the lack of automatic control for urban drainage systems in China, urban stormwater runoff pollution cannot be addressed effectively. To remedy this disadvantage, a theoretical framework of real time control (RTC) system was established. The key indicator identification and pollution load estimation of stormwater runoff pollution were investigated to provide control parameters (such as amount, duration, and intensity of rainfall). Stormwater storage tank (SST) is the main component of the RTC system. In order to enhance the efficiency of SST, a simulator was developed. A back propagation neural network (BPNN) was adopted to predict the turbidity of SST. The developed BPNN contained input, implicit and output layers. A simulation device of SST was proposed to provide the input data for the developed BPNN. Also, several RTC parameters of water quality (e.g., turbidity, ammonia nitrogen, total phosphorus and chemical oxygen demand) of SST were investigated by an experimental simulation. Then turbidity was chosen as the key RTC parameter. The turbidity prediction of the developed BPNN included two types, one was based on a single variable (i.e., flow quantity) at the inlet of SST to predict the turbidity at the inlet of SST. The other was based on multiple variable (i.e., flow quantity, ammonia nitrogen, total phosphorus and chemical oxygen demand) at the inlet of SST to predict the turbidity at the outlet of SST. Based on the training and verification processes of the experimental data, the results indicated that the developed BPNN performed well (R > 0.94) in predicting turbidity of SST. The developed BPNN was incorporated into a RTC system as a simulator, which can thus help efficient decision making for facilitating water storage and pollution reduction of stormwater storage tanks. [ABSTRACT FROM AUTHOR]

Due to frequent and serious waterlog and environmental pollution in cities in recent years, urban stormwater management (USM) has become an essential issue of urban sustainable development in China. Low Impact Development (LID) technologies are effective and popular measures of USM, and can be used to reduce urban waterlog and the associated environmental pollution. In this paper, in order to identify an optimal strategy of LID technologies based on economic efficiencies and environmental performances, the construction costs and environmental benefits of the four LID technologies (including grass swales, bioretention cells, green roofs, and permeable pavement) were analyzed. Then, due to the uncertain feature of rainfall, concentration of pollutants and many social-economic factors, an inexact fuzzy chance-constrained programming (IFCCP) model was developed. Multiple uncertainties that can be expressed as interval parameters, fuzzy sets, and stochastic distribution can be addressed effectively and incorporated directly into the modeling process. Based on the analysis of the precipitation probability distribution and land use data, the developed model was applied to a university campus with relatively high building density and plot ratio in Beijing. Then, optimal solutions of multiple precipitation probabilities ( p m ) under varying USM goals were obtained. The four LID technologies were chosen to meet multiple USM goals based on multiple p m values. When   p m = 0.1 , 0.05 , 0.02 and   0.015 , the total construction investments would be 1.46 to 4.66, 1.46 to 5.25, 2.31 to 8.54 and 3.95 to 12.7 million yuan, respectively. The relationship between construction costs and p m represented the relationship between economic benefit and system risk. The results indicated that USM was influenced by the rainfall capacity, construction costs would increase with the increase of precipitation, and the risks of constraint violation would decrease as the construction costs increase. When considering the four LID technologies during the planning stage of USM, the preferred selection order would be bioretention cells, permeable pavements, grass swales and green roofs. Therefore, the solutions under each p m level could provide the references for desired USM plans. [ABSTRACT FROM AUTHOR]

The pursuit of the sustainable agriculture often ignores to consider the ecological benefits. To achieve this goal, it is necessary to evaluate the ecological impacts related to the sustainable development. Therefore, to assess the overall ecological impact of crop production in 31 provinces in China, this study proposes to set up a non-monetary framework and indicators based on Emergy theory. Further analysis focuses on sustainable ecosystem under three different planting scenarios to identify the driving factors. The results demonstrated that the ecosystem disservice of crop production processes is greater than the services. The total emergy inputs, fertilizers used and R%, all of these can affect the ecological benefits. Furthermore, simply changing the cultivation pattern cannot achieve a balance between ecology and economy, there is no doubt that other method needed are needed. These findings provide valuable insights for developing appropriate policies for decision makers. • Establishing a framework to calculate the agri-ecosystem services and dis-services. • Analyzing the ecological benefits of crop system in the three different scenarios. • Building three emergy-based ecosystem service indicators to assess the crop system. • Proposing some feasible advices from ecological benefits perspective. [ABSTRACT FROM AUTHOR]

Intensive coastal reclamation activities can lead to ecological degeneration in coastal areas. Therefore, the engineering design concept of an ecosystem-based coastal reclamation has aroused great interest. However, the inconsistency of standards and the unclear understanding of ecological processes are the difficulties in this field. In this paper, we selected eco-seawall as object to determine and optimize the 4 engineering parameters (including L 1 , L 2 , a, and h 1). We set up 20 groups of working conditions under analyzing, and applying a computational framework to optimize eco-seawall designing processes. The computational efficiency would be enhanced by this developed method from the following aspects: (a) establishing surrogate model for eco-seawall to replace the high cost of simulation process; (b) searching engineering control parameters to optimize eco-seawall design; (c) obtaining the Petro curve set of optimal parameters for coastal reclamation management and ecological restoration. The design domain of the related parameters was determined by analyzing simulated and experimental data. As the results, the design domain were obviously reduced through optimization process: L1∈[0.1, 0.3]; L2∈[0.2, 0.8]; a∈(30,60]; h1∈[0.2,0.4]. Additionally, according to pilot-scale experiment, it can be concluded that the overtopping frequency for the low slope angle dam is 66.7%; the overtopping frequency for the high slope angle dam is 44.4%. The optimal design range for the practical engineering design can be provided by the optimal solution set of eco-seawall obtained from the optimization results. The calculation approach used in this research will provide reference for other engineering optimization work. • Using a optimization method to optimize engineering parameters of eco-seawall. • Establishing an surrogate model for eco-seawall. • Searching engineering control parameters to optimize eco-seawall design. [ABSTRACT FROM AUTHOR]

With the cascade hydropower stations (CHSs) development and climate change in a large dam-reservoir-river (DRR) system, distinguishing the impact of climatic and anthropogenic factors on vegetation dynamics is vital for a sustainable watershed ecosystem management. In this study, a Breaks for Additive Seasonal and Trend (BFAST) algorithm was adopted to detect the breakpoints (BP) and trend change of climatic factors (CFs) and vegetation dynamics at the pixel scale in the DRR system. The partial correlation and time lag analysis methods were chosen to examine the relationship between vegetation dynamics and CFs (i.e., temperature and precipitation). The multiple linear regression analysis method was adopted to separate the impacts of climatic and anthropogenic factors on vegetation dynamics in multiple periods (i.e., from 1999 to 2013, before BP (BBP), and after BP (ABP)). The most significant results indicated that the area fraction of the BP of vegetation dynamics was 19% of the basin, and the trend BP mainly occurred in 2008. The trend of BP mainly was positive BBP and negative ABP in the basin, and its area fraction was 53.01%. For the eight CHSs, the vegetation dynamics around Ludila and Guanyinyan were more affected by their constructions than that around the other hydropower stations. For the CFs, the vegetation dynamics was mainly affected by precipitation. The lag periods of temperature and precipitation on vegetation dynamics were four months and one month, respectively. Most of the vegetation dynamics appeared to have been impacted by anthropogenic factors in the DRR system. For the eight CHSs, the impact degree of anthropogenic factors on vegetation dynamics BBP was more severe than that ABP, especially around Ludila and Guanyinyan hydropower stations. These results are beneficial for sustainable watershed ecosystem management in the DRR system and ecological development planning around the CHSs. [ABSTRACT FROM AUTHOR]

High flow events (including both the high flow pulses and floods) exert many critical ecological functions in riverine ecosystems and their elimination will lead to severe ecological degradation. However, common hydropower operations are only required to legally maintain the minimum environmental flows (e-flows). The extra release of high flow events will result in revenue loss of hydropower producers. In this research, we propose an approach to assessing the eco-compensation standard which is necessary to compensate the loss of hydropower producers and promote the release of high flows to downstream rivers. The scenario tree approach is adopted for the generation of possible scenarios of flows and electricity prices to deal with the uncertainties of the two factors in deregulated electricity markets. A stochastic linear programming method is proposed to determine the maximum mean annual revenues under two e-flow provision scenarios, i.e., only sustaining the minimum e-flows (scenario 1) and sustaining both the minimum e-flows and a specified number of high flow events (scenario 2). The revenue difference under the two e-flow scenarios is set as the eco-compensation standard. A case study in the Wangkuai reservoir, China, demonstrates that the eco-compensation standard is obviously influenced by the installed hydropower generation capacity (IHGC) and the number of high flow events planned to be released. Moreover, the greatest standards may be not corresponding to a very large IHGC. [ABSTRACT FROM AUTHOR]

The problem of fresh water scarcity is one of the major constraints for regional development. Rapid urbanization and rising economic prosperity has further exacerbated problems with not only water quantity, but also water quality. Hence, sustainable utilization of water resources should be a priority, especially in water stressed areas. However, most current studies on optimal water allocation models are based on physical water, which cannot fully reveal the water flows embedded in imported and exported raw materials and products throughout all production processes. Thus, they fail to achieve sustainability goals. Therefore, the objective of this study was to set up an optimal water allocation model for industrial sectors based on water footprint accounting. In this paper, first the internal and external blue and gray water footprints were evaluated. The results indicated that the external water footprint was the major contributor to Dalian's water footprint, which shared around 72.58% of the total. Among all the industrial sectors, the chemical industry and chemical production sector had the highest total WF. Second, analyses of water productivity intensity of the WF and physical water for the industrial sectors were conducted. The comparison of the results revealed that physical water productivity intensity failed to explain the embedded water inflows in the production processes among the regions and sectors. Third, an optimization model based on the water footprint accounting results was thus set up. The optimal consequences indicated that a water allocation plan could meet both the water requirements for the blue and gray water footprints of the industrial sectors, and their target output goals as well. Therefore, this optimal allocation model is both rational and applicable for sustainable water utilization in future water management strategy formulations. [ABSTRACT FROM AUTHOR]

The intensification of heat stress in a changing climate poses great threats to both human health and labor productivity. It is of great practical importance to assess the impacts of climate-induced heat stress on labor productivity and to develop effective adaptation strategies. In this paper, an integrated optimization-based productivity restoration modeling framework is proposed for the first time to develop the optimal policies for adaptation to climate change. To address underlying uncertainties associated with climate and labor management systems, we take into account ensemble projections from five global climate models (GCMs) under two Representative Concentration Pathways (RCP2.6 and RCP8.5) and inexact system costs. The system costs, including direct and indirect costs such as management costs, energy costs, and labor costs, are presented as interval numbers due to inherent uncertainty caused by population growth, technology development, and other social-economic factors. Uncertain information can be effectively communicated into the optimization processes in this study to generate optimal and reliable decision alternatives. We find that the increased Wet-Bulb Globe Temperature (WBGT) will lead to a large reduction in labor capacities over China except for the Tibetan Plateau under both RCPs by the end of the 21st century. The less developed regions tend to achieve the minimum system cost by having labor productivity recovered through working overtime due to the relatively low cost of overtime. This could result in more heat-related work injuries in the less developed regions. Since the less developed regions are not heat-prone areas in China, the changing climate would be a more dangerous threat and cause more damages to these regions where the residents are less acclimatized to heat stress. Moreover, we obtain a range of minimum system costs from 1.86 to 8.97 billion dollars under RCP2.6 and from 9.42 to 32.31 billion dollars under RCP8.5 (about 0.2% of China's GDP in 2019, 0.01% of China's GDP projected in 2100 under a sustainable socio-economic development scenario) for the restoration of labor productivity in a warming climate. We argue that urgent actions are needed to mitigate global warming impacts on labor productivity. • An integrated optimization modeling framework is developed to achieve optimal labor productivity restoration in a warming climate. • Various uncertainties are addressed through ensemble climate projections and interval programming models. • Intensification of heat stress leads to a large decrease in labor capacity over China except for the Tibetan Plateau. • More losses in working hours are projected for moderate labor activities compared to light and heavy labor activities. • Less developed regions tend to have more working hours recovered by working overtime than air conditioning. [ABSTRACT FROM AUTHOR]

Online monitoring data of atmospheric environmental quality often deviate or are missing, causing a great impact on regional atmospheric quality analysis. In this study, a deep learning method to repair atmospheric environmental quality data based on Gaussian diffusion and gate recurrent unit (GD-GRU) was developed to improve repair accuracy. A multi-source Gaussian diffusion model was developed to estimate PM 2.5 based on the pollutant diffusion law and the data of 61 stations in Guilin. The root mean square error (RMSE) of the estimated and observed value was extracted as the error sequence. The error value was regarded as output of gate recurrent unit (GRU) with the inputs of weather and pollutant parameters. Missing data were calculated by Gaussian diffusion estimated value and the error predicted by GRU. The established GD-GRU model was applied to repair the long-sequence missing data. The analytical results indicated that the GD-GRU model had higher prediction accuracy of extreme values than Gaussian diffusion model and GRU model, because GD-GRU based on Gaussian diffusion can calculate the extreme value by simulating the diffusion and transmission mechanism. The established model predicted PM 2.5 concentration in the next hours with an RMSE of 12.561, which was approximately 21.02% better, on average, than methods like autoregressive integrated moving average model (ARIMA), support vector regression (SVR), recurrent neural network (RNN), long short-term memory model (LSTM), and GRU. The established GD-GRU model demonstrated good performance on extreme values prediction and air quality data repair, thus providing a new method for air quality long-sequence missing data repair. [ABSTRACT FROM AUTHOR]

The environmental impacts of the fashion industry have been aroused wide concerns. The globalization and fragmentation of the textile and fashion system have led to the uneven distribution of environmental consequences. As denim is the fabric of jeans that is representative of fashion, this study assessed virtual carbon and water flows embodied in the global denim-product trade, and footprints of denim production were quantified by life-cycle assessment and water footprint assessment. Results indicated that virtual carbon embodied in the global denim trade increased obviously from 14.8 Mt CO 2 e in 2001 to 16.0 Mt CO 2 e in 2018, and the virtual water consumption dropped from 5.6 billion m3 to 4.7 billion m3 from 2001 to 2018. The denim fabric production and cotton fibre production respectively contribute the most of the carbon emissions and water consumption. Polyester blended denim has 5% larger carbon footprint and 72% lower water footprint than cotton denim, and contributes to increasing embodied carbon emissions (from 4% in 2001 to 43% in 2018). Increasing the utilization of polyester blended denim would save water but face more pressures on carbon emission reduction. In the past two decades, virtual carbon and water flows embodied in the global denim trade are relocating, main jean consumers (i.e., the USA, EU-15, and Japan) withdraw the denim manufacturing supply chain and developing countries (i.e., China, India, and Pakistan) with higher carbon and water footprint undertake main global denim production, facing increasing climate-related risks and water crisis. The South-South cooperation helps share successful experiences, save production cost, and lessen resource consumption and environmental emissions. The production and consumption of denim should be shifted to circular and sustainable ways and new business models are required. The analysis framework can provide the basis for exploring environmental flows of product-level trade, and results can offer a basis for environmental policies and control strategies of the fashion industry, and as well as the sustainable production and consumption of garment. [ABSTRACT FROM AUTHOR]

Inflow forecast is an important input for reservoir operation. Due to limited forecast techniques, the inflow forecast always has uncertainties (usually in the form of forecast errors). Handling inflow forecast uncertainties effectively is important for optimizing reservoir operation. The magnitude of this uncertainty depends on both the forecast lead times and the hydrological periods (i.e., wet season vs. dry season). Two-stage Bayesian stochastic dynamic programming (TBSDP) is an effective way to address the forecast uncertainties. However, current TBSDP methods pay more attention to the uncertainty differences caused by forecast lead times, neglecting the uncertainty differences in wet and dry seasons. Starting with a TBSDP model, we proposed a new two-stage and two-period version of the model (TTBSDP), which can not only consider the forecast uncertainty differences caused by forecast lead times, but also separately accounts for forecast uncertainty in wet and dry seasons. Because dealing separately with the uncertainties in the two periods increases the computational complexity, we further explored the conditions under which degree of forecast uncertainties this separation is necessary. We compared our new model's results (in terms of cumulative annual power generation) with the previous Bayesian stochastic dynamic programming models. With increasing forecast uncertainty in the first stage of the overall forecast horizon (in the wet season), the TTBSDP model produced superior results at higher uncertainty, with the most stable performance (the smallest variation of cumulative annual power generation when the forecast uncertainty coefficient increases). The proposed new model is benefit for hydropower operations when the magnitude of forecast uncertainty in the wet seasons is large. • A two-stage and two-period Bayesian stochastic dynamic programming model was developed. • The new model dealt separately with forecast uncertainties not only in two stages but also in two hydrological periods. • The TTBSDP model produced superior results at higher uncertainty, with the smallest variation of power generation. [ABSTRACT FROM AUTHOR]

This paper develops an interval multi-stage robust programming model for regional low-carbon electric power system planning with energy-water nexus. The model integrates interval programming and multi-stage stochastic programming to handle uncertainties with different probability distribution information. In addition, under the framework of robust programming, the optimal solutions could provide tradeoff information between system cost and decision maker's risk preference. The hybrid programming method is tailored for a case study of Shandong Province, China to validate its applicability and provide useful managerial insights, where energy-water interactions are gain great concerns. The model helps to make robust strategies associated with risk control for capacity expansion, carbon capture and storage (CCS) technology retrofitting, power generation, imported electricity, and water allocation for electricity generation, with the goal of minimizing total cost. Besides, considering the variation of water resource conditions and environmental policies under climate change, the impacts of water resource availability and carbon emission caps on regional electric power system planning are explored and discussed. It is found that stricter carbon emission cap policy would facilitate the investment on CCS devices but have little impact on the installed capacity structure, meanwhile it would increase water consumption per power generation. However, scarcer water resource situation would promote the development of renewable energy generation, and limit the investment on CCS technology. • Low-carbon power system management optimization with energy-water nexus under uncertainties. • Impact analysis of various water resource availability and carbon emission cap on optimal strategies. • Useful managerial insights on energy system planning from energy-water nexus perspective. • Robust solutions with risk-cost tradeoff information to reflect different risk preferences. [ABSTRACT FROM AUTHOR]