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Massive diagnostic testing has been performed for appropriate screening and identification of COVID-19 cases in the ongoing global pandemic. However, the environmental impacts of COVID-19 diagnostics have been least considered. In this paper, the environmental impacts of the COVID-19 nucleic acid diagnostics were assessed by following a full cradle-to-grave life-cycle approach. The corresponding life-cycle anthology was established to provide quantitative analysis. Moreover, three alternative scenarios, i.e., material substitution, improved waste treatment, and electric vehicle (EV)-based transportation, were further proposed to discuss the potential environmental mitigation and conservation strategies. It was estimated that the life cycle of a single COVID-19 nucleic acid diagnostic test in China would lead to the emission of 612.9 g CO

Energy-related carbon emissions take a large proportion in China, and the interregional trade caused by provincial disparities has led to significant differences in carbon footprint (CF) and embodied carbon flows among provinces that make great bottlenecks for the balance of economic development and carbon mitigation. In this study, we developed an embodied carbon flow-based ecological network (ECFEN) model with economic trade and emission data through combining the multi-regional input-output model and ecological network analysis approach. The developed ECFEN model was applied to 30 provinces in China to quantify the interprovincial flow of carbon embodied in final goods and services and identify the ecological utility (competitive, exploitative, and exploited) and control/dependence relationships between different regions. The main findings can be summarized as follows: Firstly, Jiangsu had the highest total CF with amount of 906 Mt, which was approximately 24.5 times than that of Hainan (37 Mt). Especially, the local CF in Shandong was the largest among all of the provinces with an amount of 683 Mt. Secondly, 13 pairs of embodied carbon flow paths exceeded 20 Mt, and the remarkable embodied carbon flowed from resource-oriented regions (e.g., Inner Mongolia, Shanxi, Hebei) to economically developed eastern coastal provinces and municipalities (e.g., Jiangsu, Guangdong, Beijing, Chongqing). Metallurgy and electricity, water, and gas contributed 30-80% of the total embodied carbon import and export for each province. Thirdly, the exploitative and exploited relationship dominated the ecological relationship between provinces. Meanwhile, the resource-oriented regions played the role of controllers while economically developed provinces were dependents. On the one hand, the central government could take vertical compensation measures such as financial subsidies for major exporter and controllers. On the other hand, it is necessary to take horizontal technical transfer especially from economically developed southeast coastal provinces to western underdeveloped inland area. The obtained results and policy implications are expected to provide reasonable insights for decision-makers to formulate carbon mitigation strategies under the domestic circulation strategy.

In this study, a new concept concerning comprehensive characteristics of water resources utilization as an index for risk modeling within the water allocation management model is proposed to explore the tolerance of unbalanced allocation problem under the water–energy nexus. The model is integrated with interval two-stage stochastic programming for reflecting system uncertainties. These uncertainties are associated with the industrial production feature and the decision-making process. With respect to the water–energy nexus, energy proposed is mainly focused on the consumption intensity of water purification and transportation from different water sources. The developed model is applied for industrial water resources allocation management in Henan province, China. Multiple scenarios related to disparate energy consumption control and the comprehensive risk levels are simulated to obtain a reasonable trade-off among system profit, comprehensive risk, and energy consumption. The results indicated that the strict comprehensive risk management or energy consumption control measures could cause damage to system benefit owing to decreasing the flexibility of industrial water resources distributions, and the preliminary energy consumption or the comprehensive risk control would be beneficial to moderate the conflict between industrial sectors and water resources, and accelerate industrial structure transformation in the future. HIGHLIGHTS A comprehensive risk-oriented industrial water resources management model is proposed.; Uncertainties are reflected as stochastic and interval information.; Energy–water nexus is a constraint index for water purification and transportation.; The tolerance of unbalanced allocation problem is explored.; Trade-off among system profit, comprehensive risk, and energy consumption is analyzed.

We investigated the regulatory effects of hypoxia-inducible factor-1a (HIF-1α) on glycolysis metabolism in esophageal carcinoma (ESCA) cells. A series of bioinformatics databases and tools were used to investigate the expression and role of HIF-1α in ESCA. The expression of HIF-1a in ESCA tissues and adjacent tissues was validated by real-time PCR. Small interfering RNA (siRNA) was used to inhibit HIF-1α-related genes in human ESCA cells (Eca109 and KYSE150). Cell proliferation was detected by the CCK-8 assay. The expression of HIF-1α and glycolytic enzymes were investigated by real-time PCR and Western blot. HIF-1α is highly expressed in ESCA and is involved in many biological processes such as cell hypoxia reaction, glucose metabolic process. Further in vitro experiments showed that expression of HIF-1α in Eca109 and KYSE150 significantly increased under hypoxia compared with normoxia conditions. Also, the glucose uptake and lactate production under hypoxia were higher. The expression levels of hexokinase 2 (HK2) and pyruvate dehydrogenase kinase 1 (PDK1), glycolysis-related genes, were significantly increased under hypoxia. After siRNA knockdown of HIF-1a in Eca109 and KYSE150, the glucose uptake and lactate production, as well as cell proliferation were significantly decreased under hypoxia, and HK2 and PDK1 were significantly downregulated. HIF-1α promotes glycolysis of ESCA cells by upregulating the expression of HK2 and PDK1 under hypoxia.

Despite the considerable contribution of hydropower for meeting electricity demand and meanwhile achieving free carbon emission, the rationality of hydropower exploitation needs to be reassessed in the context of hydropower curtailment being severe in some hydro-dominant areas. In this paper, a two-stage fuzzy-stochastic chance-constrained interval (TFCI) regional energy system optimization model under multiple complexities is developed for systematically assessing the rationality of existing hydropower exploitation plan, and reflecting the tradeoff between system cost and reliability for Sichuan territory where the exploitable hydropower resources rank first in China. Results showed that the degree of hydropower overcapacity would become far more severe under the real existing energy structure planning. Instead of continuously boosting hydropower exploitation, proper limitation on hydropower installed capacity expansion to make other renewable energy obtain deserved promotion can effectively reduce system cost and improve the utilization efficiency of all the renewable technologies. The highest utilization efficiency of hydropower, wind power and solar power could achieve [90%, 100%], [100%, 100%], and [100%, 100%], respectively. The obtained results also indicated that greater uncertainty about system cost may have to be faced if policymakers aim to lower risk and enhance system reliability. Suggestions are provided for eliminating the irrationality of hydropower in Sichuan and other similar hydro-dominant regions based on above mentioned conclusions.

BackgroundPurified platelet-rich plasma (P-PRP) is gradually being used in the treatment of osteoarthritis (OA), and its sources are mainly divided into autologous and allogeneic blood. However, it is unclear whether autologous PRP is more effective or allogeneic PRP is superior.ObjectiveIn this study, autologous and allogeneic P-PRP was injected at early stage of KOA in rabbits, and then the differences in the efficacy of the two P-PRPs against KOA were compared from several perspectives, including pathological histology and immunohistochemistry.MethodExperimental rabbits were divided into normal group (n = 8), model group (n = 8), autologous P-PRP group (n = 8), and allogeneic P-PRP group (n = 8) using a random number table method. The normal and model groups did not receive any treatment, and the autologous P-PRP and allogeneic P-PRP groups received intra-articular injections of autologous and allogeneic P-PRP, respectively, to observe the changes in the gross specimens of the knee joints of the experimental rabbits in each group. The histopathological changes of chondrocytes were also observed by HE-stained sections of articular cartilage, and the expression of chondrocytes Bone morphogenetic protein-2 (BMP-2) and Sox9 were detected by immunohistochemistry.ResultsCompared with the allogeneic P-PRP group, the differences were statistically significant (P P P > 0.05).ConclusionIntra-articular injection of autologous P-PRP activated the expression of BMP-2 and Sox9 in chondrocytes and effectively improved KOA cartilage repair and reduced bone redundancy and joint fluid formation, and its efficacy was superior to that of intra-articular injection of allogeneic P-PRP.

Understanding the changes in hydrological process is a key subject for water resource management of a high-diversity watershed. In this paper, through an establishment of a SWAT-based model, the effects of climate change and its induced vegetation change on hydrological process were analyzed in the East River Basin. The model could well simulate the hydrological processes of the basin including surface runoff (SURQ), groundwater (GWQ), lateral flow (LATQ), total water yield (WYLD), actual evapotranspiration (ET), and groundwater recharge (PERC). Under the vegetation change induced by temperature increase, the effects of the vegetation change on hydrological process were larger than that of the temperature change. Under the vegetation change caused by the increase of temperature and precipitation, the vegetation change enhanced the effects of climate change on annual SURQ, LATQ, GWQ, WYLD, and PERC of the basin. Under spatial scale, when the temperature and precipitation changed simultaneously, the increase of precipitation could promote the increase of annual ET in sub-watersheds. Also, the annual SURQ, WYLD, GWQ and ET in western sub-watersheds were more sensitive to the cumulative changes of vegetation and climate. This work can provide useful information to decision makers in water resource management of watersheds.

The water-energy nexus (WEN) system is a large-scale complex system that comes with diverse forms of risks owing to many challenges in the process of maintaining economic-resource-environmental sustainability. First, the rapidly increasing demand for water and energy subjects many regions to the high risk of water and energy shortages. Second, decision makers face difficulties in weighing system benefits and loss risks under a series of stricter water-energy policies. To handle the aforementioned dual risks of WEN, in this study we propose copula-based stochastic downside risk-aversion programming (CSDP) for regional water-energy management. CSDP integrates the superiority of the copula analysis method and downside risk-aversion programming into a framework, which can not only reveal the risk interactions between water resources and energy demand by using copula functions under different probability distributions, even previously unknown correlations, but also control economic risk, tackle systemic uncertainties, and provide an effective linkage between system stability and conflicting economic benefits. The proposed model was applied to a water-energy system case study in Tianjin City, China. Optimal solutions for various water resources and energy demand copulas associated with different scenarios and hierarchical risk levels were examined in the CSDP model. The results showed that water resources have a greater influence than energy on industrial structure adjustment in Tianjin, with consequent effects on system benefits, optimal output value schemes, and environmental protection strategies. In addition, the tertiary industry provides a new opportunity for economic growth based on a large amount of water-energy consumption, and its potential resources and water-air pollution risks also deserve extensive attention.

As the total water resources consumption control and carbon mitigation continuous improvement, the weak water-carbon incorporate management is increasingly exposed. In this study, a water-carbon nexus assessment framework is proposed to analyze the nexus relationship between water consumption and carbon emission, and distinguishes the coupled water-carbon transmission intensity and the transfer paths under regional and industrial scales. According to the practical input-output table, water consumption, and carbon emission information, the framework is applied to Beijing-Tianjin-Hebei urban agglomeration (BTHUA), a population, resource, and trade intensive area of China. Inter-regional/intra-regional water consumption and carbon emission transfer fluxes between sectors, the pairwise ecological relationship, and the water-carbon nexus were analyzed. Results indicated that the water-carbon transfer indexes from Hebei to Beijing and Tianjin were 161.85 kg/m

Energy and water are rapidly consumed as the most basic strategic resources of various nations. It is of vital importance to systematically explore the environmental and economic impacts of energy-water co-management policies. This study is to develop a multiperspective-driven factorial metabolic network analysis framework (MPDF) to (a) investigate the direct/indirect/total resource consumption response mechanisms induced by changes in production and consumption; (b) explore the factor interactions of different policies in diverse energy and water metabolic networks by initiating factorial analysis; (c) quantify the economic effects of co-management policies by proposing multiple vulnerability indicators. A typical energy-dependent region, Shanxi Province, China was selected as a case study. The results indicated that the production- and consumption-oriented policies have various guidelines for reducing direct and indirect energy-water consumption. Significant interactions in simulation results suggest synergistic effects across sectors. Considering that Shanxi's energy-water nexus economic vulnerability is as high as 2.22%, it is recommended to prioritize the allocation of resources to sectors with significant factor effects to avoid economic losses. Implementing corresponding resource conservation policies for light industry, machinery manufacturing, construction can reduce water consumption by 18.8%. The findings are expected to provide a solid scientific basis for formulating co-management strategies to alleviate resource scarcities.

The related dynamic change in meteorological and hydrological parameters is critical for available water resources, development management options, and making informed decisions. In this study, to enhance the resolution of the predicted meteorological and hydrological parameters under climate change, the statistical downscaling method (SDSM), the generalized regression neural network (GRNN) model, the Soil and Water Assessment Tool (SWAT) model, and the improved Tennant method were integrated into a framework. The available water resources were assessed in the Huangshui watershed of the Qinghai–Tibet Plateau, which has the highest average elevation in the world. The meteorological parameters were obtained by the SDSM model and the GRNN model. The SWAT model used the meteorological parameters to simulate the hydrological data under climate change scenarios. Considering the meteorological conditions and the high sediment content in the basin, the available water resources are evaluated by the improved Tennant method. The meteorological data of the Xining station from 1958 to 2011 were used to analyze the dynamic changes and mutation trends in the data. The results indicated that the precipitation would have a great increase during the wet season from May to September, and the flows and available water resources would decrease with increasing carbon emissions under different representative concentration pathways (RCPs).

Commonalities and asynchronous experiences of realizing zero-waste of water, energy, and food resources between the world and China have critical global importance to support sustainable development, especially for Guangdong-Hong Kong-Macao Greater Bay Area (GBA) urban agglomeration. In this study, a comprehensive assessment framework for the zero-waste city was proposed to evaluate zero-waste construction in the world (e.g., San Francisco, New York, and Tokyo) and China (e.g., Beijing, Shanghai, and GBA) from the perspective of water, energy, and food. The results showed that the zero-waste construction level of the GBA cities was weaker than that of the benchmark city and other world-class cities. The average score of the GBA cities was the lowest, 2.5% lower than the benchmark city and 11.8% lower than other world-class cities. Macao had apparent advantages in the social-economic and ecological-environment system, while the Pearl River Delta cities were considerably better than Macao and Hong Kong in the water and food systems. Future work could improve the level of zero-waste construction by learning from foreign zero-waste cities’ advanced experience, increasing efforts to promote the implementation of a circular economy, and building an all-around government sharing mechanism with public participation.

This paper is aimed at proposing a short-term hybrid energy system robust optimization model for regional energy system planning and air pollution mitigation based on the inexact multi-stage stochastic integer programming and conditional value-at-risk method through a case study in Shandong Province, China. Six power conversion technologies (i.e., coal-fired power, hydropower, photovoltaic power, wind power, biomass power, and nuclear power) and power demand sectors (agriculture, industry, building industry, transportation, business, and residential department) were considered in the proposed model. The optimized electricity generation, capacity expansion schemes, and economic risks were selected to analyze nine defined scenarios. Results revealed that electricity generations of clean and new power had obvious increasing risks and were key considerations of establishing additional capacities to meet the rising social demands. Moreover, the levels of pollutants mitigation and risk-aversion had a significant influence on different power generation schemes and on the total system cost. In addition, the optimization method developed in this paper could effectively address uncertainties expressed as probability distributions and interval values, and could avoid the system risk in energy system planning problems. The proposed optimization model could be valuable for supporting the adjustment or justification of air pollution mitigation management and electric power planning schemes in Shandong, as well as in other regions of China.

Water and land are crucial natural resources for agricultural development. It is necessary to allocate water and land resources effectively in order to achieve the maximum economic profits and the minimum environmental costs. In this research, an inexact two-stage fractional programming model was developed for the allocation of water and land resources, which is integrated interval-parameters (IPP), two-stage stochastic programming (TSP), fractional programming (FP). This model could optimally allocate water and land resources at the same time under the scenario of the maximum economic profit and the minimum environmental cost; it was proved to be beneficial in (1) dealing with the conflicts between economic development and environmental protection and give insights in trade-off among the agricultural system; (2) allocating water and land resources for five crops under multiple flow level simultaneously; and (3) describing the uncertain inputs as interval-parameters to reduce model uncertainties. The developed model was applied to the northeast region of China. The optimal allocation schemes of water and land resources, the maximum economic profits, and the minimum environmental costs were obtained. The results showed that economic profits in the agricultural system in the northeast region of China would not definitely be connected with the allocation of water and land resources, and solid waste pollution would bear the largest environmental cost. The developed model could help decision-makers to get a deeper understanding of the agricultural system and manage water and land resources in an efficient and environment-friendly way.

A new defined parameter, tuning efficiency (TE), is proposed to evaluate the tuning performance of distributed feedback laser diode (DFB-LD). The TE is defined as the ratio of output wavelength response to power response of DFB-LDs when working in a current driving mode. Simulations and experiments are performed to study its influence on harmonic signal, signal-to-noise ratio (SNR), and residual amplitude modulation (RAM) effect in wavelength modulation spectroscopy (WMS). It is the first time to be found that the parameter TE would change as driving frequency of DFB-LD getting changed; in addition, different DFB-LDs result in changes in TE. A WMS-based water vapor sensing system is constructed to discuss the TE’s influence on gas detection applications, and the experimental results show highly consistent with the simulations. In conclusion, the TE is a robust parameter guiding us to choose favorable DFB-LDs for gas sensing applications.

Severe PM2.5 (particulate matter with aerodynamic diameter smaller than 2.5 μm) pollution in China, with adverse impact on human health and global climate change, has raised great concern in recent years. The volatile organic compound (VOC) is suggested to be a significant precursor of PM2.5, but which VOC structure contributes the most to the aerosol particulate formation is still an open question. With large space occupation and complex structures (cyclic, linear and branched), VOCs together with inorganics may play a critical role in atmospheric new particle formation (NPF). To explore the effect of molecular structure on nucleation in NPF, a multi-component (H2SO4-HNO3-NH3-VOC) kinetic model is established based on the gas kinetic theory and graph theory. As the most common VOCs in the atmosphere, alkanes with 2–15 carbons are selected for model analysis. The results show that alkanes especially those with a cyclic structure or carbon number ≥4 results in the highest rate of NPF within the species considered in this study. The dominant size of nuclei, as well as the concentration of particles (larger than 1 nm), is enhanced by the increasing carbon number in alkanes. Taking the alkanes with same carbon number for comparison, the contribution to the nucleation by molecular structure is found to follow the order cyclic>linear>branched, and inversely proportion to branching number.

The temperature fluctuations inside commercially distributed feedback laser diode were experimentally measured and analyzed in detail in this study. In tunable diode laser absorption spectroscopy based gas sensing systems, the temperature of the laser source is usually fixed and a current signal is used to drive the laser for wavelength scanning or modulation. Even if the laser is controlled by a robust temperature controller, the internal temperature still fluctuates due to the delayed response of the temperature controller when rapidly changing the drive current. These temperature fluctuations result in distortions in the measured absorption signals which can be effectively suppressed by limiting the drive current modulation to the resonance frequency of the temperature control proportional-integral-derivative loop. This study demonstrates a reduction in the temperature dependent absorption distortions and improves the detection uncertainty using this technique.

Aerosol particulate matter with dynamic diameter smaller than 2.5 μm (PM2.5) is the main cause for haze pollution in China. As a dominant precursor of PM2.5, SO2 emitted from industrial process is now strictly controlled by using limestone/gypsum Wet Flue Gas Desulfurization (WFGD) system in China. However, a phenomenon that fine particle derived from WFGD is recently addressed, and is suggested to be a potential source of primary PM2.5. Herein, a first investigation into the particle generation mechanism in WFGD system is conducted with a novel droplet (containing particles) drying and breakage model. The proposed model considers a random and porous crust instead of the previous regular crust assumption, and is verified by comparing the modeling results with measurements. An orthogonal test with four factors and three levels is carried out through modeling calculation, and flue gas temperature (Tg) in the inlet is found to be a governing parameter for PM2.5 yields in WFGD. With Tg in range of 120–160 °C, PM2.5 yields in desulfurizing tower can reach a maximum value at ∼2 × 108 cm−3 under typical WFGD condition. To avoid this situation and reduce the PM2.5 generation, Tg is suggested to be lower than 120 °C. Additionally, a new insight of the elimination effect of gas-gas heater (GGH) on “gypsum rain” in WFGD system is provided.

For a country like China with unbalanced development pattern among provinces, domestic circulation (i.e., cross-province trade) is important for the long-term stability and prosperous development of economic market. However, with the rapid advance of integration of domestic regional economy, while expanding the internal market scale and deepening the provincial division of labor network for promoting the economic growth, the carbon emissions embedded within the cross-province traded products and services cannot be underestimated. Under the background of climate-trade dilemma, it is necessary to exploring the spatiotemporal variations and socioeconomic determinants of provincial "invisible" carbon emissions for a better understanding of trade-induced eco-environmental effects. To that end, this study developed an environmental-economic system model through integrating the environmentally extended multiregional input-output method and weighted average structural decomposition analysis technique to explore the trade-related emissions at the provincial level and generate the mitigation-management strategies for decisionmakers. Overall, more than half the emissions were embedded within cross-province goods and services trade over the whole study period. Furthermore, the distribution of traded emissions showed obvious spatial heterogeneity and great unbalance was existed between provincial imports and exports. Among all provinces, carbon surplus provinces were always more than deficit ones and the trading patterns of approximately 65% regions remained unchanged during 2007-2017. Remarkably, the emissions trading pattern undergone transition from carbon deficit to carbon surplus in provinces like Henan, Hubei, Guizhou, and so on. Conversely, provinces like Jilin, Shanghai, and Xinjiang showed opposite change. With the prevalence of online payment and electronic commerce in the future, the central and sub-national government could consider launching a pilot project for the design and creation of personal carbon consumption account in the carbon surplus provinces such as Guangdong, Henan, and Jiangsu. Meanwhile, for the provinces with larger carbon exports, it is necessary to establish the horizontal high technical transfer channels and vertical compensation mechanisms such as financial subsidies for improving the low-carbon production level. Our findings provided a holistic depict of national traded emissions at the provincial level, highlighting the importance of cross-province emission effect in exploring ways to promote the low-carbon transition of domestic circulation and fulfill the high-quality development of 'dual circulation' new pattern and successful achievement of 'double carbon' solemn commitment.

Background Studies have shown that platelet-rich plasma (PRP) can enhance the effect of meniscus repair, but some studies have suggested different views on the role of PRP. Purpose To determine whether PRP can enhance the effect of meniscus repair with respect to pain reduction and improved functionality and cure rate in patients with meniscus injury. Methods By searching PubMed, EMBASE, Cochrane Library databases, clinicaltrials.gov , and the CNKI database from their inception till December 1, 2020, we performed a meta-analysis of RCTs reporting the results of the Pain Visual Analog Scale (VAS), the pain of Knee injury and Osteoarthritis Outcome Score (KOOS), Lysholm score, the International Knee Documentation Committee (IKDC), healing rate, and adverse events. The risk of bias is assessed using Cochrane’s collaborative tools. The summary results are expressed with effect size and 95% confidence interval, and sensitivity were performed. Results The meta-analysis included 9 RCTs and 345 patients. In general, compared with the control group, used of PRP during meniscus surgery significantly improved the pain (SMD: -0.95, p < 0.00001,95% CI: -1.22 to -0.69, I2 = 42%) and knee joint function (SMD: 1.00, p = 0.01.95% CI: 0.22 to 1.79, I2 = 89%) of patients with meniscus injury at 6 months after treatment. However, both PRP and non-PRP showed improvements in the pain and knee joint function, with no significant difference between the groups at 1 months and beyond 12 months. The PRP enhancement technique showed benefit in improving the cure rate of meniscus repair (RR:1.44; p < 0.0001, 95% CI: 1.20–1.73). No serious adverse events were reported in any study. Conclusion As an enhancement program for meniscus repair, PRP is worthy of further consideration in improving the function and pain of patients during the mid-term follow-up after surgery, and PRP can further improve the healing rate of meniscus repair. However, the evidence still needs to be interpreted carefully because of the quantity and quality of the included studies.

In this study, an integrated framework is proposed for agricultural water and land resources management under uncertain circumstances and energy-water nexus. The framework has two components: the optimized interval solutions generation of unknown market quotation and environment situation through the novel interval fuzzy-expectation programming, and the post-optimization decision-making of the tradeoff between system risk and return through risk-explicit interval programming. An optimization model based on the framework was developed from a real description for agricultural water resources system of Guangdong province under considering various crop, multiple water sources, and the related energy consumption in irrigation and drainage . The impact of energy consumption and risk level control on water resources allocation among different crops and system benefits were analyzed. The results indicated that the preference for surface water resources as the main water sources can effectively reduce regional irrigation energy consumption, and the planting area of each crop would have different variations duo to resources endowment constraints. Risk tradeoff-based results can provide valuable information and additional concern for developing low carbon-oriented agricultural water resources management schemes.

Quantifying the decoupling states of carbon emissions from a multi-sectoral and dual-perspective can guide more detailed emission reduction strategies. Based on the single-regional input-output (SRIO), Tapio decoupling analysis (TDA), and structural decomposition analysis (SDA), this study investigated the dynamic variation feature and decoupling state of multi-sectoral carbon emissions, and revealed their driving factors of consumption-based emissions in Guangdong province from 2002 to 2017. The main discovery can be summarized as follows from results analysis. Firstly, electricity production sector and construction sector were the largest direct and embodied carbon emission sources, and capital formation was the most important factor with the contribution of approximately 100 % that led to embodied carbon emissions of construction. For most of the manufacturing and service sectors, the embodied carbon emissions caused by international export exceed 50 %. Secondly, the consumption structure, consumption per capita, and population effect promoted the embodied emissions during 2002–2012, while the emission intensity effect was the greatest offsetting factor for all sectors. Consumption structure effect was becoming a major driver to the increase of embodied carbon emissions for construction. Thirdly, agriculture, mining, energy transformation, and service sector showed the unsatisfactory decoupling relationship between direct carbon emissions and economic output. According to the decoupling states, the decoupling relationships in some secondary industries were overestimated under the situation of only considering direct carbon emissions. The obtained results and policy implications are expected to provide holistic reference for policymakers to promote the short-term carbon peak and long-term carbon neutrality of Guangdong province from the sectoral perspective.

In this research, a dual-randomness bi-level interval multi-objective programming (DR-BIMP) model was developed for supporting water resources management among multiple water sectors under complexities and uncertainties. Techniques of bi-level multi-objective programming (BMOP), double-sided stochastic chance-constrained programming (DSCCP), and interval parameter programming (IPP) were incorporated into an integrated modeling framework to achieve comprehensive consideration of the complexities and uncertainties of water resources management systems. The DR-BIMP model can not only effectively deal with the interactive effects between multiple decision-makers in complex water management systems through the bi-level hierarchical strategies, but also can characterize the multiple uncertainties information expressed as interval format and probability density functions . It could thus improve upon the existing bi-level multi-objective programming through addressing discrete interval parameters and dual-randomness problems in optimization processes simultaneously. Then, the developed model was applied to a real-world case to optimally allocate water resources among three different water sectors in five sub-regions in the Dongjiang River basin, south China. The results of the model include determining values, interval values, and stochastic distribution information, which can assist bi-level decision-makers to plan future resources effectively to some extent. After comparing the variations of results, it is found that an increasing probability level can lead to higher system benefits, which is increased from [20,786.00, 26,425.92] × 10 8 CNY to [22,290.84, 27,492.57] × 108 CNY, while the Gini value is reduced from [0.365, 0.446] to [0.345, 0.405]. A set of increased probability levels gives rise to the lower-level objectives. Furthermore, the advantages of the DR-BIMP model were highlighted by comparing with the other models originated from the developed model. The comparison results indicated that the DR-BIMP model was a valuable tool for generating a range of decision alternatives and thus assists the bi-level decision-makers to identify the desired water resources allocation schemes under multiple scenarios.

The water–energy nexus has become a key factor in the implementation of low-carbon green development, which has led to the need for exploring effective management within the coupled integrated system with multi-energy flow supplies. In this study, the coupled relationship between water resources and energy in the integrated energy system was systematically analyzed, and a system operation optimization model was proposed through comprehensively considering cold, heat and electricity load, and nine kinds of energy conversion and supply equipment/technology from the perspective of a water resources and energy nexus in a typical industry park. The system operation scheme, energy supply mode, net benefit and water resource consumption under different water resource control scenarios were obtained. The results show that water resource control would directly bring about a directly positive influence on renewable energy utilization and energy storage reduction, and that a system’s external dependence and benefits, renewable energy utilization potential and other factors in an integrated energy system should be comprehensively considered. The development of more effective control indicators could be better to promote the effectiveness of bidirectional regulation in a water–energy nexus.

To facilitate regional water resources allocation, an integrated bi-level multi-objective programming (IBMP) model with dual random fuzzy variables was developed in this research The proposed model was derived through incorporating dual random fuzzy variables, multi-objective programming, and interval parameter programming within a bi-level optimization framework. This approach improved upon the previous bi-level programming methods and had two advantages. Firstly, it was capable of reflecting tradeoffs among multiple conflict preferences for water related bi-level hierarchical decision-making processes. Secondly, random fuzzy variables were used to tackle the dual uncertainties in both sides of the constraints, which were characterize as probability density functions and discrete intervals. Then, a real-world water resources planning problem was employed for illustrating feasibility of the application of IBMP model in Dongjiang river watershed of south China. Results reflected the alternative decisions for water allocation schemes under a set of probability levels and fuzzy α - cut levels, which can support in-depth analysis of tradeoffs among multiple levels and objective values. Moreover, modeling comparison analysis was undertaken to illustrate the performances of the proposed model.

In this research, an interval two-stage stochastic fuzzy-interval credibility constraint programming (ISFICP) method was developed for water resources allocation among multiple water users under complexities and uncertainties. The method could reflect the multiple complexities of water resources management, also trade-offs between the system benefits and violation risks. Dongjiang River (DJR) Basin, which supplies water to several core cities in south China such as Guangzhou, Shenzhen, and Hong Kong, was applied as the real demonstrative case. The water resources system of DJR Basin is particularly complex due to it is the primary source water for Guangdong-Hong Kong-Macao Greater Bay Area (GBA). Through considering multiple complexities and uncertainties of the water resources system, such as natural, economic, and social conditions, ISFICP was developed to obtain potential water-allocation schemes. Probabilistic distribution, fuzzy-interval sets (FIS), and discrete intervals were introduced to represent the multiple uncertainties associated with the multiple complexities. The results indicated that the model could provide practical schemes for local decision-makers under multiple scenarios such as flow levels, credibility levels, and recycling rates.

Faced with gradually serious energy and water crisis, the exploration of energy-water nexus metabolism relationships could provide a new direction for achieving the resources conservation and consumption reduction. In this paper, a comprehensive assessment for energy-water metabolic network is proposed to search the dynamic evolution and quantify the sectoral metabolic processes in Liaoning Province, China. Input-output analysis (IOA) and ecological network analysis (ENA) are integrated to clarify the embodied linkages and the complicated system interactions in the energy-water nexus network. The coefficients of “water-related energy” and “energy-related water” are explored through the system dynamics model (SDM) based on the consecutive input-output tables from 2012 to 2018. In addition, energy-water nexus efficiency index (EWEI) is constructed to evaluate the metabolism circulation efficiency of energy-water nexus network. The results indicate that (a) the energy and water outflow sectors mainly concentrate in primary industries and tertiary industries, while inflow sectors mainly concentrate in secondary industries; (b) the sectoral relationship provides effective pathways to implement collaborative resource saving and efficiency improvement measures, and the key sectors with the highest correlation with other sectors in energy-water nexus are AGR (agriculture), MIN (mining) and WRC (wholesale, retail and catering services). (c) the EWEI shows a gradually downward trend and the state of the energy system would determine the health of the energy-water nexus metabolism more strongly than water system.