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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.

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.

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.

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.

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.

In this study, an inexact two-stage stochastic downside risk-aversion programming is developed for regional industrial water resources allocation under considering system return-risk and various environment control strategies. In the model, interval-parameter programming, two-stage stochastic programming, and downside risk measure are introduced into an integrated framework for reflecting the complexity and uncertainty of industrial system, and avoiding the expected revenue risk. The method could not only reflect industrial water resources allocation characteristic among multiple users and suppliers, but also provide an effective linkage between economic cost and the system stability. The model is applied to a real case of industrial water resources allocation management in Chongqing city, China, where regional industrial system has faced with lots of difficulties and complexities in water resources utilization and water environmental protection. The impact of pollutants emission reduction and risk-aversion attitude on water resources allocation for different industry sectors, system benefits, and pollutants emissions were analyzed. The results indicated that the total pollutants emission amount control and the expected revenue risk can be used as effective measures for regional industry structure adjustment from terminal environmental and macro-economic perspective. The model has a significant value for regional industrial water optimization allocation under uncertainty to achieve the maximum economic benefits and the effective utilization of multiple water resources.

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.

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.

Resource and environmental elements as controlling factors for ecologic and socio-economic are crucial to seek new ideas and paths for development and prosperity. In this study, environmentally-extended input-output analysis and ecological network analysis were combined to develop three ecological networks including energy ecological network, water ecological network, and carbon ecological network for searching the complex relationships among different departments for water utilization, energy consumption, and carbon emissions under considering China as a superorganism with various complex metabolic processes and the most fundamental metabolic materials. The embodied ecological elements intensity, the indirect consumption and emissions, the embodied material flows, the ecological relationships, and the dependence intensities among sectors was obtained through transforming the monetary input-output data to physical data from 2007, 2012, and 2017. The results show that the Energy Ecological Network and Water Ecological Network were in a relatively stable state with a mutualism index greater than 1, and the relationship among different sectors in the CO

In this study, a comprehensive research framework coupled with electric power demand forecasting, a regional electric system planning model, and post-optimization analysis is proposed for electric power system management. For dealing with multiple forms of uncertainties and dynamics concerning energy utilization, capacity expansions, and environmental protection, the inexact two-stage stochastic robust programming optimization model was developed. The novel programming method, which integrates interval parameter programming (IPP), stochastic robust optimization (SRO), and two-stage stochastic programming (TSP), was applied to electric power system planning and management in Harbin, China. Furthermore, the Gray-Markov approach was employed for effective electricity consumption prediction, and the forecasted results can be described as interval values with corresponding occurrence probability, aiming to produce viable input parameters of the optimization model. Ten scenarios were analyzed with different emissions reduction levels and electricity power structure adjustment modes, and the technique for order of preference by similarity to ideal solution (TOPSIS) was selected to identify the most influential factors of planning decisions by selecting the optimal scheme. The results indicate that a diversified power structure that dominates by thermal power and is mainly supplemented by biomass power should be formed to ensure regional sustainable development and electricity power supply security in Harbin. In addition, power structure adjustment is more effective than the pollutants emission control for electricity power system management. The results are insightful for supporting supply-side energy reform, generating an electricity generation scheme, adjusting energy structures, and formulating energy consumption of local policies.

Resources and environmental crises are involved in energy management system, confronting a larger amount of coal consumption and GHG emission during structure adjustment plan making in China. In this study, a GHG-mitigation oriented and coal-consumption constrained inexact robust energy system management model is developed for adjusting regional power structure and analyzing the effects of different policy instruments on the performance of power system. The proposed model is a hybrid methodology of interval two-stage stochastic programming and stochastic robust programming. It can handle uncertainties presented as discrete intervals and probability distributions, and reflect the tradeoff between system costs and the tractability under different policy scenarios. The proposed model was applied to a case study of Jiangsu Province, a developed region within the total coal consumption control pilot area of China. The results revealed that ambitious carbon emission and coal consumption reduction targets would promote the utilization and development of renewable energy. Stricter carbon emission caps were shown to be more efficient than coal consumption cap policies for encouraging investment in renewable energy generation, especially wind power. If over 15% carbon emission reduction target was carried out, the recent coal consumption control policy would have little impact on the electricity system development.

With the deregulation of electricity market and the penetration of renewable energy, microgrid system operators may encounter more difficulties in operation management when facing complex economic, technological, and political uncertainties. In this paper, a robust cost-risk tradeoff model is developed for day-ahead schedule optimization in residential microgrid system under uncertainties. This method is an integration of inexact two-stage stochastic programming and worst-case conditional value-at-risk theory, and could handle uncertainties with inexact or partly known probability distribution information. Besides, by introducing the financial risk measurement, it could also hedge against the worst-case scenario caused by multiple independent uncertainties. The proposed model was applied to a hypothetical residential microgrid system with combined heat and power generation for obtaining optimal day-ahead schedule strategies under variable conditions with respect to renewable energy generation, power demand, and electricity market price. The obtained solutions demonstrate that the proposed model could reflect better tradeoff information between economic operation and stable performance according to different risk-aversion attitudes. In general, more conservative risk attitude would be coupled with higher system cost, which implies higher system stability is at the expense of the economic costs. The developed robust cost-risk tradeoff method would be expected to have a potential for wide applications.

A simple laser wavelength calibration technique, based on second harmonic signal, is demonstrated in this paper to improve the performance of quartz enhanced photoacoustic spectroscopy (QEPAS) gas sensing system, e.g. improving the signal to noise ratio (SNR), detection limit and long-term stability. Constant current, corresponding to the gas absorption line, combining f /2 frequency sinusoidal signal are used to drive the laser (constant driving mode), a software based real-time wavelength calibration technique is developed to eliminate the wavelength drift due to ambient fluctuations. Compared to conventional wavelength modulation spectroscopy (WMS), this method allows lower filtering bandwidth and averaging algorithm applied to QEPAS system, improving SNR and detection limit. In addition, the real-time wavelength calibration technique guarantees the laser output is modulated steadily at gas absorption line. Water vapor is chosen as an objective gas to evaluate its performance compared to constant driving mode and conventional WMS system. The water vapor sensor was designed insensitive to the incoherent external acoustic noise by the numerical averaging technique. As a result, the SNR increases 12.87 times in wavelength calibration technique based system compared to conventional WMS system. The new system achieved a better linear response ( R 2 = 0 . 9995 ) in concentration range from 300 to 2000 ppmv, and achieved a minimum detection limit (MDL) of 630 ppbv.

In this paper, a fuzzy risk-explicit interval parameter programming (FREIPP) approach was provided for multiple energy supply and demand management in microgrid system under uncertainties. The FREIPP method integrates risk-explicit interval linear programming and fuzzy theory within a general framework. It can tackle fuzzy and interval uncertainties in terms of various cost coefficients, forecasted load demand, decision maker's risk attitude and other uncertainties in microgrid system management. Compared with traditional interval parameter programming, the proposed method has distinct advantages in minimizing the system cost and risk simultaneously and providing more risk explicit solutions with the regard of obscure risk preference of decision maker. The FREIPP approach was successfully applied in a microgrid system with combined cooling, heating and power (CCHP) generation for three types of decision maker (i.e. defensive, neutral and aggressive). The obtained results indicated that the proposed FREIPP approach could provide optimal operation strategies with explicit cost-risk tradeoff information for decision maker when facing multiple complex uncertainties. Furthermore, it could help decision maker with different risk tolerance select desired optimal risk-aversion strategies, which is more realistic in real-world decision making process.

The growing energy crisis and severe atmospheric pollution have put much pressure on the Chinese government and resulted in a lot of revolutionary changes. Considering the upcoming new environmental and carbon tax, how would the tax reform affect the power sector; which types and how much of the different power generation technologies would be added are important questions that need to be answered by decision makers. In this study, a deterministic optimization model is proposed for determining the optimal power mix through the introduction of environmental and carbon taxes. A case study of Hebei Province in China is provided to illustrate the effects of these two taxes. The capacity additions of different generation technologies, air pollutants and CO2 emission amounts, system costs, and regional power security under different tax levels are profoundly examined. The modeling results indicate that such tax policies could significantly improve to the power mix adjustment as well as the quality of the ambient air quality. Higher tax levels would promote the development of renewable power generation. Meanwhile, different degrees of CO2 and air pollutant emission reduction can be achieved. The modeling results could help the decision makers identify the satisfactory tax levels in the future.

Water, energy, and food resources are indispensable and irreplaceable resources for the survival and development of human society. This study systematically assessed the three resources system in Guangdong, Hong Kong, and Macao based on constructed direct and nexus-oriented, multi-regional input-output, and ecological network analysis models. Various network analysis (e.g., control, utility, hierarchy, and robustness) was adopted to identify the critical factors of inter-regional resources trade from a perspective of supply-demand. The results indicated that Guangdong, Hong Kong, and Macao have complex control linkages in the three resources trade network, and Guangdong is the key to improving the three resources network structure. The three resources network existed highly competition and exploitation in the three regions. Industrial development is unbalanced and competitive for the three resources. The wholeness water-energy-food trade network of the three regions stayed in a positive environment, but the positive effect level was relatively weak. The three resources network robustness in the three regions is at a medium level. Hong Kong and Macao's water-energy-food network systems have a high vulnerability, and the lowest system robustness was food-related energy in Hong Kong. Finally, we provide some measures to help the sustainable development of the water-energy-food resource system in the three regions, such as cross-regional coordinated management, integration industries development, seawater toilets-flushing, sea rice, and renewable energy.

Due to climate change and human disturbances, rainfall spatial and temporal variabilities at the watershed have become increasingly prominent, significantly impacting the patterns of runoff generation and pollutant transportation. Originally, rainfall variabilities in space and time were frequently neglected or idealized in the traditional applications of hydrology and water quality models. This research attempted to investigate the responses of runoff quality and quantity simulation to rainfall spatial and temporal variabilities. We used Rainfall Hazard Modeling System (RainyDay model) combined with TRMM (Tropical Rainfall Measuring Mission) rainfall products to generate rainfall events for 500 return periods. Such events were resampled into eight rainfall scenarios with multiple levels of spatial and temporal variabilities. They were used to drive the Soil and Water Assessment Tool (SWAT) to simulate runoff quality (TP as the index) and quantity under multiple watershed scales and return periods at the Dongjiang River watershed. Analysis of Variance was employed to analyze the relative contributions of rainfall spatial and temporal variabilities to the total variabilities of runoff quality and quantity. The results highlighted that rainfall spatial and temporal variabilities would have notable impacts on runoff quality and quantity under multiple watershed scales and event magnitudes. For headwater at small watershed scales, rainfall spatial variability would have a relatively significant impact on runoff quality and quantity. For relatively larger watershed scales, rainfall temporal variability showed an increasingly important impact than spatial variability for smaller return periods, whereas the opposite results can be obtained for relatively larger return periods. The results can be caused by the following reasons:1) rainfall was concentrated in the middle and lower reaches of the watershed; 2) it was based on the daily scale. Our research suggested that attention should be paid to spatial and temporal variabilities of rainfall inputs in hydrological and water quality models.

Rooftop photovoltaic (PV) system, as part of the renewable energy development strategy to guarantee energy security and reduce greenhouse gas emissions in urban areas, has received a lot of attention during the last decade. To provide an up-to-date and systematic research landscape of the rooftop PV field, this study conducted the bibliometric analysis, collaboration network analysis, co-citation analysis, and hotspots detection based on 595 articles collected from the core collection database of Web of Science. The results showed that the number of publications per year in this field has increased steadily since 2015. The USA was the most important contributor in this research field in terms of quantity (number of publications) and impact (number of citations). The co-authorship communities were obtained by collaboration network analysis, and the international collaboration is expected to be further strengthened according to the research focuses of each community. The key knowledge base and the main hot topics of the rooftop PV research field were identified from co-citation analysis and keywords co-occurrence network. Furthermore, based on the literature review, a detailed analysis of the main topics was provided for a better understanding of the current research trends and opportunities. This study can be served as a strategic review of the rooftop PV field to help relevant researchers carry out in-depth research in the future.

In this research, an integrated prediction-optimization approach (IPOA) was developed to manage coupled risks of water and energy nexus (WEN) systems. This approach combined Markov-chain based unbiased grey model (MC-UGM), Copula analysis and inexact optimization into a general modeling framework. The proposed approach was employed for dealing with the joint probability distributions of water and energy resources, and further supporting coordinated decision-making under uncertainties. Then, Beijing Tianjin Hebei Integrated Region (BTHIR), as the leading economic center of north China, was employed as the demonstrative site for the developed approach. Results indicated that IPOA could help predict water and energy demands, excavate the interactions between water and energy, identify and deal with the uncertain and complex features of the WEN system in the region. Optimal allocation schemes of water and energy under multiple coupled risks for multiple users in BTHIR were obtained. It can be found that under certain coupled risks, the lower for water risk, and the greater for energy risks. Also, the net benefits of WEN systems would continue to increase over time. Under most of the risk levels, water resource in BTHIR would be mainly allocated for domestic and industrial sectors. Energy resource would be mainly allocated for secondary and tertiary industrial sectors. The developed IPOA could not only realize the identification and quantification of inherent interactions between energy and water, but also help decision makers formulate reasonable policies balancing system benefits and coupled risks. Besides, IPOA can improve the robustness of utilization decisions regarding water and energy, which could help promote the coordinated development for BTHIR.

Energy-related carbon emissions are increasing the rate of climate change, and controlling carbon emissions is a common challenge for the international public. Despite attempts to restrict the utilization of fossil energy and advancing technology for cleaner production, there has been little discussion on the determinants of change in carbon emissions for future scenarios and planning energy systems according to the analysis of low carbon development. In this study, a comprehensive energy optimization planning framework under a low-carbon mode is established. A framework based on the gray model (GM) and logarithmic mean Divisia index (LMDI) method are constructed to predict the emission mitigation potential and decompose the carbon emission driving factors. The decomposition results are key input prerequisites for the following energy optimization model: An interval parameter multiple-objective programming (IPMOP) optimization model, which is developed to support regional energy system administration by seeking the trade-offs among economic development, energy utilization, and environmental protection under multiple uncertainties. Furthermore, the proposed approach is applied to a case study in Guangdong, China. The results reveal that (a) the clean production effect (GDP per unit of atmospheric pollutants emission) would become the primary positive force for carbon emission increase, and the pollutant reduction effect (total atmospheric pollutants emission) would play the primary negative role; (b) the coal-dominated energy structure in Guangdong is expected to be transformed to a petroleum-dominated energy structure; (c) the GDP in Guangdong would steadily increase over time, but the pace of economic growth will decelerate, and the annual average growth rate of GDP for the coming fifteen years will be [3.67%, 4.26%]. This study provides a new pathway for policymakers to identify the determinants of carbon emission increase and to generate optimal solutions on a regional scale.

Reliable access to electricity is still a serious problem for remote villages, however, abundant solar radiation and biomass have the potentials to provide energy supply in a sustainable way. This paper focuses on the optimal design and techno-economic analysis of the stand-alone hybrid energy system for a typical rural village in the northwest of China. Based on the estimation of local renewable energy potential and the investigation of heat and power consumption patterns, a mixed-integer linear programming optimization model is presented with the aim of minimum total annualized cost. The techno-economic analysis of different system configurations is conducted and compared. Particularly, the levelized cost of energy production and levelized cost of energy consumption is proposed to evaluate the economic-cost of energy from both supply and demand-side respectively. Furthermore, sensitivity analysis and scenario analysis is conducted to demonstrate the influences of the variations and uncertain environmental policy on total system cost and performance. The results demonstrate that the PV/biomass-CHP/diesel generator/gas boiler/battery/thermal energy storage hybrid energy system is the most economical option with a total annualized cost of 652 × 103 $. The levelized cost of energy consumption (0.355 $/kWh) is greater than that of energy production (0.275 $/kWh), implying a great potential for local electrification and more flexible heat/power output equipment.

Rapid industrialization leads to potential energy scarcity. The formulation of energy intervention can alleviate the economic losses caused by insufficient energy inputs in advance. In this study, the energy dependency and sensitivity quantification model (EDSQ) is firstly developed to evaluate the impacts of insufficient energy support on economic development under network-based perspective and sector-based perspective. A special case study for the Province of Shanxi, China, is conducted to illustrate the potential benefits of its use in the formulation of energy intervention for energy-dependent regions. Seven energy groups are explored to gain more insights into the impacts of specific energy groups on energy metabolism and the complicated interactions among sectors. It is found that the energy metabolism of Shanxi is under an unhealthy state. The internal flows are dominated by exploitation and competitive relationships, which is not conducive to effective energy metabolism. The hierarchy analysis indicates that the pulling force is hardly affected by the classified energy groups but the driving force is sensitive, which further reveals that the producers can choose different energy sources according to their production structure. Faced with potential energy scarcity, the shortage of coal may bring considerable economic losses. The energy intervention should be formulated for the sectors that are less dependent on energy. However, it is not recommended to curb energy use in the petroleum, coking, nuclear fuel processing and coal mining sectors, because their sectoral losses can impose significant losses to the entire network. Thus, the scientific results of this study can provide academic support for quantifying the impact of potential energy scarcity, and guide the formulation of energy intervention to achieve sustainable development for energy-dependent regions.

By combining geographic information systems (GIS) and multi-criteria decision making (MCDM) method, this paper presents a framework to evaluate the suitable location and technical potentials for large-scale solar photovoltaic (PV) and concentrated solar power (CSP) plants. With the assist of spatial tools, the potential locations are identified by extracting restrictive criteria (e.g. protected area, land cover, transport infrastructure, and orography). Using MCDM, the land suitability for PV and CSP plants is evaluated with the comprehensive consideration of different factors, such as climate, orography, water availability, and location, where factor weights are calculated by the analytic hierarchy process (AHP) method. The study framework is illustrated by Ningxia, China, a key energy project area along the Belt and Road with abundant solar radiation. The obtained results suggested that the middle area of Ningxia is recommended for the solar power plant deployment. Besides, due to the limitation of water resource availability, PV technology is more appropriate for the study area than CSP technology. The estimated total technical potentials of PV and CSP systems could be 443 TWh/year and 308 TWh/year, indicating great potential for future solar energy development to enhance the renewable energy supply and participate in energy cooperation with the other regions. Moreover, sensitivity analysis is conducted to investigate the impacts of system efficiency variation on energy output. The results can be used for the identification of the potential areas for solar power plant installation, as well as support for the synergic expansion of generation plants and the power grid.

Government subsidy for waste incineration is an effective measure to promote the economic growth of the waste management system. It is of vital importance to formulate optimal management strategies for decision-makers by considering conflicting objectives among system cost and environmental risk under complex uncertainties. In this study, an inexact two-stage multi-objective planning (ITMOP) model is proposed for supporting municipal solid waste (MSW) management. This model advanced existing optimization methods through integrating interval linear programming and two-stage stochastic programming within the multi-objective programming framework, where various uncertainties expressed as interval value and probability distributions were effectively reflected. The environmental risks associated with exhaust gas and leachate emission from incineration and landfill are quantitively assessed. The economic performance of these two waste disposal facilities is evaluated through the introduction of different energy recovery and subsidy policy scenarios. The ITMOP-based municipal solid waste management model is applied to a case study of MSW management planning in the Baotou city of China. Ten scenarios integrated with different economic incentives are designed for analyzing the optimized solutions. The generated waste flow allocation pattern and facility expansion capacity results provide scientific waste management strategies for local authorities. Moreover, the obtained total system cost and environmental risks under different scenarios can help policymakers resolve eco-economic conflicts according to their preferences. Waste incineration will gradually replace landfilling as the mainstream of waste treatment in the future. Revenues from energy recovery and financial subsidies for incineration can compensate for its high economic cost and promote waste resource utilization. The results indicate that the local government can use the subsidy policy to encourage waste disposal to proper facilities and simultaneously reduce environmental emissions.

In this paper, a risk explicit interval two-stage programming (REITSP) model was proposed for supporting the regional electricity generation expansion with renewable portfolio standard (RPS) constraint. It could effectively tackle multiple uncertainties expressed as interval numbers. But unlike the traditional interval two-stage programming model, the proposed REITSP model could provide an explicit trade-off information between system cost and risk for decision makers with different risk preferences. It could minimize the total system cost, as well as the decision risk according to the aspiration risk level of decision maker. The developed REITSP model was applied to the case study in Guangdong Province, China for its long-term electricity system planning. Crisp solutions under different aspiration risk levels for varying RPS targets were obtained and analyzed. The results showed that according to the current available renewable energy and affordable construction speed, the maximum RPS target for Guangzhou Province during 2016–2025 should be 17%. Higher RPS level would promote the renewable energy generation, especially solar power; meanwhile, it would reduce the CO2 emission and the imported electricity, but with greater investment cost. The obtained results and trade-off information would be valuable for the optimal long-term electricity system expansion planning when facing future uncertain situation.

Economic restructuring, energy planning and environmental protection are subject to inherent uncertainties in a compound system with competing decision objectives. Therefore, an inexact multi-objective programming model for regional economy-energy-environment system management has been developed to obtain absolutely “optimal” solutions. Under two comparative scenarios, three subsystems, six industries, four types of energy, and three kinds of air pollution were considered in an optimization model, and a net system benefit and trade-off analysis between subsystems was conducted. The methods of interval-parameter programming and multi-objective programming were incorporated into the model to tackle the uncertainties and complexities reflected in the case study. The model results indicated that the developed model could provide effective linkages among the economy-energy-environment systems and offer decision makers great insight into the reliability tradeoffs for the adjustment of the existing management policy.

Energy structure adjustment is a significant way to resolve constrained resources and environment, improve the security of energy supply, and achieve sustainable development, which is a complicated itself but further challenged by existence of uncertainties. In this study, an inexact multistage stochastic cost-risk balanced programming model is developed for energy system management under considering the policies of coal-consumption control and pollutants emission reduction. The method is an integration of inexact multistage stochastic programming and a risk management tools from modern financial theory, and could tackle uncertainties described as interval values and probability distributions. The method could reflect energy system dynamic characteristic in a multi-period problem, and provide an effective linkage between conflicting economic cost and the system stability. The proposed method is applied to a real case of planning electric power structure adjustment in Shandong province, China, where the energy system is faced with lots of difficulties complexities in electric power structure adjustment. The impact of coal-consumption control level and pollutants emission reduction scenarios on energy system structure adjustment, and resources and environmental effect were compared and analyzed. The results are valuable for supporting the adjustment or justification of the existing power generation schemes of the complicated regional energy system.

In the present study, a three dimensional thermal lattice Boltzmann model was developed to investigate the flow dynamics and mixed convection heat transfer of Al2O3/water nanofluid in a cubic cavity in the presence of magnetic field. The model was first validated with previous numerical and experimental results. Satisfactory agreement was obtained. Then the effects of Rayleigh number, nanoparticle volume fraction, Hartmann number and Richardson number on nanofluid flow dynamics and heat transfer were examined. Numerical results indicate that adding nanoparticles to pure water leads to heat transfer enhancement for low Rayleigh numbers. However, this enhancement might be weakened and even reversed for high Rayleigh numbers. In addition, the results show the external applied magnetic field has an effect of suppressing the convective heat transfer in the cavity. Moreover, the results demonstrate that the Richardson number in mixed convection has significant influences on both streamlines and temperature field.

In this study, an inexact fuzzy bi-level programming model was developed for regional energy and traffic system management under uncertainty in Urumqi city, China. The energy system and traffic system are important subsystems of regional areas such as cities. The coordinated management of regional subsystems is a difficult problem for regional management. A bi-level programming model is an appropriate and simple method to describe the coordinated management of regional subsystems. The energy and traffic structure adjustment, clean power generation and pollutant emission–reduction targets are designed to support the construction of an environmentally sustainable city in China. Methods of interval parameter programming and bi-level programming were incorporated into the developed model to tackle uncertainties and reflect the features in the system. The environmental impacts of energy and traffic activities and policies were analysed. The results are valuable for supporting the management or justific...

The recent research progress of the Shandong University research group on the performance improvement of tunable diode laser absorption spectroscopy (TDLAS) fiber gas sensor is presented in this paper. The artificial absorption peak technology and the balanced ratiometric detector (BRD) technology are developed to improve the resolution of the TDLAS fiber optical gas sensor. The method for eliminating background absorption interference, the reliability study and the elimination technology of the residual amplitude modulation (RAM) are studied to improve the accuracy of the TDLAS fiber optical gas sensor. We will continue to conduct the next investigation and research in this field on the basis of existing theories and technologies.

With the carbon reduction targets being set in the Paris Agreement on Climate Change, China is facing great pressure to meet its emission reduction commitment. The electric power industry as the major source of carbon emissions needs to be a focus. However, the uncertainty of power systems, the risk of reducing emissions and the fuzziness of carbon capture technology popularization rate and carbon reduction targets makes previous planning methods unsatisfactory for current planning. This paper establishes an interval fuzzy programming with a risk measure model which takes carbon capture technology and carbon reduction targets into account, to ensure that the complex electric management system achieves the best developmental state. It was concluded that in order to reduce carbon emissions, wind power and hydropower would be the best choices, and coal-fired power would be the suboptimal choice, and solar power would play a complementary role. Besides, decision makers should put much more effort into promoting and improving carbon capture technology instead of simply setting emission reduction targets. The non-synchronism of the downward trend in carbon emissions per unit of electricity generation and electric power industry total carbon emissions need to be taken seriously.

Resource and environmental elements as controlling factors for ecologic and socio-economic are crucial to seek new ideas and paths for development and prosperity. In this study, environmentally-extended input-output analysis and ecological network analysis were combined to develop three ecological networks including energy ecological network, water ecological network, and carbon ecological network for searching the complex relationships among different departments for water utilization, energy consumption, and carbon emissions under considering China as a superorganism with various complex metabolic processes and the most fundamental metabolic materials. The embodied ecological elements intensity, the indirect consumption and emissions, the embodied material flows, the ecological relationships, and the dependence intensities among sectors was obtained through transforming the monetary input-output data to physical data from 2007, 2012, and 2017. The results show that the Energy Ecological Network and Water Ecological Network were in a relatively stable state with a mutualism index greater than 1, and the relationship among different sectors in the CO2 Ecological Network needs to be further adjusted. AM (Advanced Manufacture) and Agr (Agriculture) played the top exporter and importer roles with AM as the largest embodied energy consumer and CO2 emitter, and Agr as the largest embodied water user. More measures about resource conservation and emission reduction for AM and Agr are desired. Con (construction) had a strong dependence intensity on other sectors with amounts of over 90%. Resources and environmental effects on Con can be improved by increasing the utilization efficiency of intermediate products. The results could provide scientific policy implications and guidelines to promote the stable and healthy operations by revealing the dynamic change of sectors within the Energy-Water-CO2 metabolic system in China.

In this study,a structural adjustment optimization model for electric-power system management under fuzzy-random environment was developed for regional electric power system management in Urumqi, China. The emission control policy andstructural adjustment are considered and quantified in the developed model to tackle the electric power problems of a regional electric-power system. The objective of this study was to develop a power structural adjustment optimization model with fuzzy-random parameters for discussing the effects, merits and defects of structural adjustment and policy adjustment to manage the electric power system of the city Urumqi. Finally, the results and comparisons analysis of the case study demonstrated the practicality and efficiency of the optimization method. The results indicated that the model can provide an effective linkage between conflicting economic cost and the system stability, and different power demand levels correspond to different electricity generation schemes with varied energy policy and power structural adjustment. The power generation schemes, pollution emission and CO2 emission were analyzed. The modeling results are valuable for supporting the adjustment or justification of the energy policies and structures within a complicated power system under uncertainty.

Inter-regional electricity transmission inevitably brings about unbalanced carbon emission and air pollution among different regions. It is vital to compensate for the economic and environmental loss of power output region by taking the carbon subsidy strategy into account. In this study, a risk explicit interval multistage stochastic programming model considering inter-regional carbon subsidy is developed to support an optimal energy system planning in Inner Mongolia, China. This model can not only deal with uncertainties embedded in the complex energy system described as interval values and random variables but also reflect dynamic linkage between multiple stages over a time series. It advanced the existing optimization methods by introducing the cost-risk tradeoff information based on the risk preferences of decision-makers. The obtained crisp solutions are more feasible, effective and optimum for the practical policymaking process. Different scenarios associated with carbon subsidy policy implementation, renewable energy share adjustment and carbon emission reduction strategy are employed to project the optimal power generation scheme, net system cost, installed electricity capacity, carbon intensity and pollution emission in the future. The results indicate that the amount of exported electricity will keep increasing, and the carbon subsidy strategy will prominently promote the economic growth of the electric power sector by 3.05%. The further power supply pattern is going to transform from fossil fuel to renewable energy, by which wind and solar power will occupy around 44% of total power generation capacity in West Inner Mongolia. Raising the share of renewable energy will be the most effective approach to adjust the power generation scheme; meanwhile, reducing carbon intensity will significantly contribute to enlarge green energy capacity, enhance low-carbon emission and mitigate air pollution. These findings will help decision-makers gain insights into a more scientific energy system planning under various uncertainties.