Game-based analysis of energy-water nexus for identifying environmental impacts during Shale gas operations under stochastic input

The Effects of Green Intellectual Capital on Green Innovation: A Green Supply Chain Integration Perspective

To demonstrate how green innovation (GI) effectively occurs, this study examines the effects of green intellectual capital (GIC) on GI from the perspective of green supply chain integration (GSCI). Based on a natural-resource-based view and knowledge-based view, the authors constructed an intermediary model of GIC-GSCI-GI, and analyzed the effects of green absorptive ability (GAA) and relationship learning ability (RLA) as moderators. An empirical survey of 328 Chinese manufacturing companies was conducted. Our results indicate that three dimensions of GIC positively impact GI. The mediating effects of internal and external GSCI exist in the relationship between GIC and GI. The moderating effects of GAA and RLA in these effects were also verified. Our study provides further empirical evidence for the relationship between GIC and GI, highlights the effects of companies' internal and external abilities on GI, and suggests new ways and implementation contexts for GI.

Analysis of the relationship between water and energy in China based on a multi-regional input-output method

The shortage of water and energy are hindering the rapid development of the regional economy in recent years. Therefore, exploring the synergy of water and energy and managing the two resources comprehensively is conducive to the sustainable development of the economy. Based on the multi-regional input-output (MRIO) model, this study proposed a new assessment framework for investigating the water-energy (WE) relationship. We used this novel framework to identify the relationships in different sectors. The achieved results are as follows. First, water and energy are closely related in many sectors, including agriculture, extractive sector, petroleum, coking, and nuclear fuel processing sector, and other sectors. However, the construction sector, textile and clothing sector, and wood processing and furniture manufacturing sector showed low correlation (p > 0.05). Second, on the whole, the WE relationship has been improving. Among the eight regions, the relationship varies greatly, and the Southern coastal region has the best relationship (r = 0.78). Third, the spatial distribution of water and energy footprints shows high agreement. Although the virtual water and energy flows alleviated the energy pressure in Coastal areas, it has aggravated the water and energy shortages in Central areas. Therefore, identification of key sectors and construction of suitable policies may help alleviate the contradiction between water and energy shortages and drive regional economic development.

Does educated labor force is managing the green economy in BRCS? Fresh evidence from NARDL-PMG approach

It is observed that an educated labor force can increase the absorption capacity of the economy and improve the effectiveness of green technologies that lead to a reduction in potential CO2 emissions. The study investigates whether an educated labor force contributes to the management of the green economy or not in BRCS economies. Panel ARDL-PMG and NARDL-PMG approaches have been employed for empirical analysis for data ranging from 1995 to 2019. According to the ARDL-PMG results, a highly educated labor force contributes to alleviating CO2 emissions in the long run. In contrast, the findings of NARDL-PMG infer that positive component of a highly educated labor force has a significant negative impact on CO2 emissions, while negative component of a highly educated labor force has a positive impact on CO2 emissions in the long run. The study suggests that BRCS countries' policymakers should promote education and training for the labor force to maintain a reduction in CO2 emissions.

Assessing the dynamic linkage between energy efficiency, renewable energy consumption, and CO2 emissions in China

Efficient energy is crucial in reducing CO2 emissions. The researchers are digging for a new efficient source of energy in the modern era. Therefore, this study explores the dynamic impacts of energy efficiency and renewable energy consumption on CO2 emissions from 1991 to 2019 for China. By using the non-linear ARDL approach, we found that a negative shock in energy efficiency has a positive impact on CO2 emissions in long run. Furthermore, renewable energy consumption with positive shock has a negative significant impact on CO2 emissions, but negative shock in renewable energy consumption leads to increase pollution emissions in long run. Besides, positive shocks to energy efficiency and renewable energy consumption have also a favorable negative effect on CO2 emissions in the short run. While a negative shock in energy efficiency has only unobservable negative impacts on CO2 emissions in the short run. Based on findings, some policy measures are suggested to attain environmental sustainability in China.

Re-evaluating the asymmetric economic policy uncertainty, conventional energy, and renewable energy consumption nexus for BRICS

Economic policy uncertainty has increased throughout the world since the previous few decades. Moreover, economic policy uncertainty significantly influences economic activities that may also produce a strong effect on energy consumption. The objective of the study is to investigate the effect of economic policy uncertainty on renewable and non-renewable energy consumption in the case of BRICS countries, for the period 1991-2019. The outcome of the panel NARDL-PMG modeling technique demonstrates that a positive shock in economic policy uncertainty exerts a negative impact on renewable energy consumption and positive impact on non-renewable energy consumption in the short-run and long-run. However, a negative shock in economic policy uncertainty has a positive impact on renewable energy consumption and negative impact on non-renewable energy consumption in the long run, while this effect becomes statistically insignificant in the short run. Numerical elements of long-run results infer that economic policy uncertainty is more influence on renewable energy compared to non-renewable energy consumption in BRICS in long run. On the basis of findings, the study suggests that the authorities should launch such programs that result in shrinking uncertainties linked with economic policy.

Cognitive Behavioral Model of an Operation Crew in the Main Control Room of a Nuclear Power Plant Based on a State-Oriented Procedure

The team’s cognitive behavior plays a crucial role in dealing with accidents at nuclear power plants. Herein, the main behaviors of reactor operators and coordinators in performing accident management were analyzed in executing a state-oriented procedure. According to these cognitive behavioral characteristics, we established cognitive behavioral models of accident management procedures. After that, a cognitive behavioral model was established for the team in the main control room of the nuclear power plant based on the two models, which is expected to provide support to the optimization of a corresponding Human Reliability Analysis model.

A Laboratory Approach to Measure Enhanced Gas Recovery from a Tight Gas Reservoir during Supercritical Carbon Dioxide Injection

Supercritical carbon dioxide injection in tight reservoirs is an efficient and prominent enhanced gas recovery method, as it can be more mobilized in low-permeable reservoirs due to its molecular size. This paper aimed to perform a set of laboratory experiments to evaluate the impacts of permeability and water saturation on enhanced gas recovery, carbon dioxide storage capacity, and carbon dioxide content during supercritical carbon dioxide injection. It is observed that supercritical carbon dioxide provides a higher gas recovery increase after the gas depletion drive mechanism is carried out in low permeable core samples. This corresponds to the feasible mobilization of the supercritical carbon dioxide phase through smaller pores. The maximum gas recovery increase for core samples with 0.1 mD is about 22.5%, while gas recovery increase has lower values with the increase in permeability. It is about 19.8%, 15.3%, 12.1%, and 10.9% for core samples with 0.22, 0.36, 0.54, and 0.78 mD permeability, respectively. Moreover, higher water saturations would be a crucial factor in the gas recovery enhancement, especially in the final pore volume injection, as it can increase the supercritical carbon dioxide dissolving in water, leading to more displacement efficiency. The minimum carbon dioxide storage for 0.1 mD core samples is about 50%, while it is about 38% for tight core samples with the permeability of 0.78 mD. By decreasing water saturation from 0.65 to 0.15, less volume of supercritical carbon dioxide is involved in water, and therefore, carbon dioxide storage capacity increases. This is indicative of a proper gas displacement front in lower water saturation and higher gas recovery factor. The findings of this study can help for a better understanding of the gas production mechanism and crucial parameters that affect gas recovery from tight reservoirs.

Non-oil economic transition for economic and environmental sustainability in Saudi Arabia: a multi-factor analysis under fuzzy environment

In this study, an analysis of Saudi Arabia's economic diversification, particularly non-oil transition, is conducted. Initially, key success factors and sub-factors that provide basis for the analysis are identified through literature survey. Three key factors (economic, labor, and market), twenty-one sub-factors (seven under each factor) are identified. To obtain the relative importance of factors and sub-factors, fuzzy analytical hierarchy process (FAHP) has been used. The economic criterion obtained the highest weight followed respectively by fiscal and labor criteria. The "proportion of Saudis in the workforce" sub-criterion received the highest weight under labor criterion, the "investment-intensive business models" sub-criterion obtained the highest weight under economic criterion, and the "increase non-oil revenue" sub-criterion got the highest weight under fiscal criterion. Overall, increase non-oil revenue sub-criterion (under fiscal criterion) received the highest weight. Later, eight major non-oil sectors are prioritized with respect to criteria and sub-criteria using fuzzy technique for order preference by similarity to the ideal solution. Petrochemicals sector ranked topped in the contribution to achieving non-oil transition. The findings of the study shall enable the government and policymakers to specifically design policies for respective sectors knowing their importance in the transition and subsequently bring a new cycle of prosperity to the Kingdom of Saudi Arabia.

Current Challenges and Advancements on the Management of Water Retreatment in Different Production Operations of Shale Reservoirs

Nowadays, water savings on industrial plants have become a significant concern for various plants and sections. It is vitally essential to propose applicable and efficient techniques to retreat produced water from onshore and offshore production units. This paper aimed to implement the PFF (Photo Fenton Flotation) method to optimize the water treatment procedure, as it is a two-stage separation technique. The measurements were recorded for the HF (hydraulic fracturing) and CEOR (chemically enhanced oil recovery) methods separately to compare the results appropriately. To assure the efficiency of this method, we first recorded the measurements for five sequential days. As a result, the total volume of 2372.5 MM m3/year of water can be saved in the HF process during the PFF treatment procedure, and only 20% of this required fresh water should be provided from other resources. On the other hand, the total volume of 7482.5 MM m3/year of water can be saved in CEOR processes during the PFF treatment procedure, and only 38% of this required fresh water should be provided from other resources. Therefore, the total water volume of 9855 MM m3 can be saved each year, indicating the efficiency of this method in supplying and saving the water volume during the production operations from oilfield units.

Renewable energy and non-renewable energy consumption: assessing the asymmetric role of monetary policy uncertainty in energy consumption

Previous infant literature has assessed the symmetric impact of monetary policy uncertainty on a few macro variables. Our study has considered asymmetric monetary policy uncertainty impacts on energy consumption. Our key concern in this study is to regulate whether US monetary policy uncertainty has an asymmetric impact on energy consumption. We employ the symmetric and asymmetric autoregressive distributed lag (ARDL) estimation methods, and we found that monetary policy uncertainty has short- and long-run negative effects on renewable energy consumption in the linear model, while decreased monetary policy uncertainty has a significant negative influence on renewable energy consumption in the USA in the non-linear model. However, in the short and long run, the measure of monetary policy uncertainty has an insignificant impact on non-renewable energy consumption, while increased monetary policy uncertainty in the USA has negative effects and decreased monetary policy uncertainty has positive effects on non-renewable energy consumption in the short and long run in the non-linear model. The effects are asymmetric in direction and magnitude. The study results call for vital changes in renewable and non-renewable energy policies to accommodate monetary policy uncertainties.

Comparison of multi-criteria and artificial intelligence models for land-subsidence susceptibility zonation

Land subsidence (LS) in arid and semi-arid areas, such as Iran, is a significant threat to sustainable land management. The purpose of this study is to predict the LS distribution by generating land subsidence susceptibility models (LSSMs) for the Shahroud plain in Iran using three different multi-criteria decision making (MCDM) and five different artificial intelligence (AI) models. The MCDM models we used are the VlseKriterijumska Optimizacija IKompromisno Resenje (VIKOR), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Complex Proportional Assessment (COPRAS), and the AI models are the extreme gradient boosting (XGBoost), Cubist, Elasticnet, Bayesian multivariate adaptive regression spline (BMARS) and conditional random forest (Cforest) methods. We used the Receiver Operating Characteristic (ROC) curve, Area Under Curve (AUC) and different statistical indices,i.e. accuracy, sensitivity, specificity, F score, Kappa, Mean Absolute Error (MAE) and Nash-Sutcliffe Criteria (NSC)to validate and evaluate the methods. Based on the different validation techniques, the Cforest method yielded the best results with minimum and maximum values of 0.04 and 0.99, respectively. According to the Cforest model, 30.55% of the study area is extremely vulnerable to land subsidence. The results of our research will be of great help to planners and policy makers in the identification of the most vulnerable regions and the implementation of appropriate development strategies in this area.

Suggesting a Stochastic Fractal Search Paradigm in Combination with Artificial Neural Network for Early Prediction of Cooling Load in Residential Buildings

Early prediction of thermal loads plays an essential role in analyzing energy-efficient buildings’ energy performance. On the other hand, stochastic algorithms have recently shown high proficiency in dealing with this issue. These are the reasons that this study is dedicated to evaluating an innovative hybrid method for predicting the cooling load (CL) in buildings with residential usage. The proposed model is a combination of artificial neural networks and stochastic fractal search (SFS–ANNs). Two benchmark algorithms, namely the grasshopper optimization algorithm (GOA) and firefly algorithm (FA) are also considered to be compared with the SFS. The non-linear effect of eight independent factors on the CL is analyzed using each model’s optimal structure. Evaluation of the results outlined that all three metaheuristic algorithms (with more than 90% correlation) can adequately optimize the ANN. In this regard, this tool’s prediction error declined by nearly 23%, 18%, and 36% by applying the GOA, FA, and SFS techniques. Moreover, all used accuracy criteria indicated the superiority of the SFS over the benchmark schemes. Therefore, it is inferred that utilizing the SFS along with ANN provides a reliable hybrid model for the early prediction of CL.

Synthesizing Multi-Layer Perceptron Network with Ant Lion Biogeography-Based Dragonfly Algorithm Evolutionary Strategy Invasive Weed and League Champion Optimization Hybrid Algorithms in Predicting Heating Load in Residential Buildings

The significance of accurate heating load (HL) approximation is the primary motivation of this research to distinguish the most efficient predictive model among several neural-metaheuristic models. The proposed models are formulated through synthesizing a multi-layer perceptron network (MLP) with ant lion optimization (ALO), biogeography-based optimization (BBO), the dragonfly algorithm (DA), evolutionary strategy (ES), invasive weed optimization (IWO), and league champion optimization (LCA) hybrid algorithms. Each ensemble is optimized in terms of the operating population. Accordingly, the ALO-MLP, BBO-MLP, DA-MLP, ES-MLP, IWO-MLP, and LCA-MLP presented their best performance for population sizes of 350, 400, 200, 500, 50, and 300, respectively. The comparison was carried out by implementing a ranking system. Based on the obtained overall scores (OSs), the BBO (OS = 36) featured as the most capable optimization technique, followed by ALO (OS = 27) and ES (OS = 20). Due to the efficient performance of these algorithms, the corresponding MLPs can be promising substitutes for traditional methods used for HL analysis.

Double-Target Based Neural Networks in Predicting Energy Consumption in Residential Buildings

A reliable prediction of sustainable energy consumption is key for designing environmentally friendly buildings. In this study, three novel hybrid intelligent methods, namely the grasshopper optimization algorithm (GOA), wind-driven optimization (WDO), and biogeography-based optimization (BBO), are employed to optimize the multitarget prediction of heating loads (HLs) and cooling loads (CLs) in the heating, ventilation and air conditioning (HVAC) systems. Concerning the optimization of the applied algorithms, a series of swarm-based iterations are performed, and the best structure is proposed for each model. The GOA, WDO, and BBO algorithms are mixed with a class of feedforward artificial neural networks (ANNs), which is called a multi-layer perceptron (MLP) to predict the HL and CL. According to the sensitivity analysis, the WDO with swarm size = 500 proposes the most-fitted ANN. The proposed WDO-ANN provided an accurate prediction in terms of heating load (training (R2 correlation = 0.977 and RMSE error = 0.183) and testing (R2 correlation = 0.973 and RMSE error = 0.190)) and yielded the best-fitted prediction in terms of cooling load (training (R2 correlation = 0.99 and RMSE error = 0.147) and testing (R2 correlation = 0.99 and RMSE error = 0.148)).

An Innovative Metaheuristic Strategy for Solar Energy Management through a Neural Networks Framework

Proper management of solar energy as an effective renewable source is of high importance toward sustainable energy harvesting. This paper offers a novel sophisticated method for predicting solar irradiance (SIr) from environmental conditions. To this end, an efficient metaheuristic technique, namely electromagnetic field optimization (EFO), is employed for optimizing a neural network. This algorithm quickly mines a publicly available dataset for nonlinearly tuning the network parameters. To suggest an optimal configuration, five influential parameters of the EFO are optimized by an extensive trial and error practice. Analyzing the results showed that the proposed model can learn the SIr pattern and predict it for unseen conditions with high accuracy. Furthermore, it provided about 10% and 16% higher accuracy compared to two benchmark optimizers, namely shuffled complex evolution and shuffled frog leaping algorithm. Hence, the EFO-supervised neural network can be a promising tool for the early prediction of SIr in practice. The findings of this research may shed light on the use of advanced intelligent models for efficient energy development.

Electrical Power Prediction through a Combination of Multilayer Perceptron with Water Cycle Ant Lion and Satin Bowerbird Searching Optimizers

Predicting the electrical power (PE) output is a significant step toward the sustainable development of combined cycle power plants. Due to the effect of several parameters on the simulation of PE, utilizing a robust method is of high importance. Hence, in this study, a potent metaheuristic strategy, namely, the water cycle algorithm (WCA), is employed to solve this issue. First, a nonlinear neural network framework is formed to link the PE with influential parameters. Then, the network is optimized by the WCA algorithm. A publicly available dataset is used to feed the hybrid model. Since the WCA is a population-based technique, its sensitivity to the population size is assessed by a trial-and-error effort to attain the most suitable configuration. The results in the training phase showed that the proposed WCA can find an optimal solution for capturing the relationship between the PE and influential factors with less than 1% error. Likewise, examining the test results revealed that this model can forecast the PE with high accuracy. Moreover, a comparison with two powerful benchmark techniques, namely, ant lion optimization and a satin bowerbird optimizer, pointed to the WCA as a more accurate technique for the sustainable design of the intended system. Lastly, two potential predictive formulas, based on the most efficient WCAs, are extracted and presented.

Multi-criteria decision making and fairness evaluation of water ecological carrying capacity for inter-regional green development

Determining water ecological carrying capacity (WECC) is of great significance to ensure inter-regional green development. This study presents a comprehensive evaluation framework for WECC assessment in the Yangtze River Economic Zone (YREZ), China. Effects of water resources, socio-economic, and ecological elements on WECC can be evaluated based on multi-criteria decision analysis. Gini and unbalance coefficients are used for measuring the regional fairness between WECC and socio-economic development. Surface water production pressure (SWPP) and groundwater pollution risk (GPR) are further regarded as indicators for expressing water resources constraint on shale gas extraction in the YREZ. Results disclose that the average WECC level decreases from 0.439 in 2000 to 0.4007 in 2016, which is the opposite of the changing trend in the Beijing-Tianjin-Hebei Region. A high WECC level appears in Zhejiang (0.5126) with a good state, but that of Guizhou (0.3983), Anhui (0.3968), Hunan (0.3914), and Chongqing (0.3651) are at the alert state. The obstacle factors of WECC in the eastern YREZ mostly originate from socio-economic and water resource subsystems, while that in the middle and western YREZ mainly arise from water resources and ecological subsystems. Fairness analysis shows a well-matching characteristic between the overall WECC and socio-economic performances due to a majority of their Gini coefficients lower than 0.4, while a poor matching characteristic exists in terms of provincial differences owing to their varied unbalance coefficients, especially in Guizhou, Jiangsu, and Shanghai. Moreover, Chongqing with most of shale gas reserves is characterized by slight SWPP (1.0202) and GPR (0.0188), but the prospect of shale gas development in Sichuan is not optimistic due to its high SWPP (1.0846) and GPR (0.0647). Recycling of flowback and product waters can significantly lighten regional water resources pressure. This presented framework can be applied into many other Chinese cities (e.g., Beijing-Tianjin-Hebei Region) with slight modifications according to their actual situations for supporting water resource managers and government with decision making.

Systematically Incorporating Environmental Objectives into Shale Gas Pipeline Development: A Binary Integer, Multiobjective Spatial Optimization Model

Shale gas pipeline development can have negative environmental impacts, including adverse effects on species and ecosystems through habitat degradation and loss. From a societal perspective, pipeline development planning processes should account for such externalities. We develop a multiobjective binary integer-programming model, called the Multi Objective Pipeline Siting (MOPS) model, to incorporate habitat externalities into pipeline development and to estimate the trade-offs between pipeline development costs and habitat impacts. We demonstrate the utility of the model using an application from Bradford and Susquehanna counties in northeastern Pennsylvania. We find that significant habitat impacts can be avoided for relatively low cost, but the avoidance of the additional habitat impacts becomes gradually and increasingly costly. For example, 10% of the habitat impacts can be avoided at less than a two percent pipeline cost increase relative to a configuration that ignores habitat impacts. MOPS or a similar model could be integrated into the pipeline siting and permitting process so oil and gas companies, communities, and states can identify cost-effective options for habitat conservation near shale gas development.

Planning for agricultural return flow allocation: application of info-gap decision theory and a nonlinear CVaR-based optimization model

A new methodology is proposed for sizing the required infrastructures for water and waste load allocation in river systems receiving return flow from agricultural networks. A nonlinear optimization model with a constraint based on conditional value at risk (CVaR) is developed to provide water and waste load allocation policies. The CVaR-based constraint limits the probabilistic losses due to existing uncertainties in available surface water. The deep uncertainties of return flow simulation model parameters, which have significant impacts on the simulated quantity and quality of agricultural return flows, are handled by using the info-gap theory. Total dissolved solid (TDS) is selected as water quality indicator and diverting a fraction of return flows to evaporation ponds is considered to control the TDS load of agricultural waste load dischargers. Quantity and TDS load of agricultural return flows over a 1-year cultivation period are simulated by using a calibrated SWAP agro-hydrological model. The results of many runs of SWAP model for different combinations of important uncertain parameters in their ranges of variations provide some response (impact) matrixes which are used in optimization model. The applicability of the proposed methodology is illustrated by applying it to the PayePol region in the Karkheh River catchment, southwest Iran. The selected strategy for water and waste load allocation in the study area is expected to provide total annual benefit of 48.64 million US dollars, while 7.84 million m³ of total return flow should be diverted to evaporation ponds. The results support the effectiveness of the methodology in incorporating existing deep uncertainties associated with agricultural water and waste load allocation problems.