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Highlights • Techno-economic modelling of PV/battery systems. • Novel approach developed to assess solar PV/battery technologies. • Grid-connected PV systems identified to have great potential. • Feasibility of PV/battery systems possible with >90% reduction in battery costs. • Further research and innovation in battery technologies necessary. Abstract Oman is a country characterised by high solar availability, yet very little electricity is produced using solar energy. As the residential sector is the largest consumer of electricity in Oman, we develop a novel approach, using houses in Muscat as a case study, to assess the potential of implementing roof-top solar PV/battery technologies, that operate without recourse to the electricity grid. Such systems target the complete decarbonisation of electricity demand per household and are defined in this study as grid-independent systems. The approach adopted starts with a technical assessment of grid-independent systems that evaluates the characteristics of the solar panel and the battery facility required to provide grid-independence. This is then compared to a similar grid-connected system and any techno-economic targets necessary to enhance the feasibility of residential roof-top PV systems in Muscat are identified. Such an analysis was achieved through developing a detailed techno-economic mathematical model describing four sub-systems; the solar panel DC source, the grid-independent sub-system, the grid-connected sub-system and the economic sub-system. The model was implemented in gPROMS and uses real hourly weather and climate conditions matched with real demand data, over a simulated period of 20 years. The results indicate that, in the context of the system studied, grid-independent PV systems are not feasible. However, combined with a sufficiently high electricity price, grid-independent systems can become economically feasible only with significant reductions in battery costs (>90% reductions). [ABSTRACT FROM AUTHOR]

Human livelihood requires reliable water supply and protection from water-related hazards. Significant growth in population and anthropogenic activities along with unfolding effects of global environmental change have enormously increased the vulnerability of human systems to changes in water quantity and/or quality. Addressing such vulnerabilities has high management priority in urban regions due to the concertation of population and socio-economic activities. This has resulted into emerging various discussions around the concept of “urban water security”. Here we provide a brief overview on the water-related threats to human society and the context of water security, with a greater goal of recognizing operational and social challenges along with science needs in urban environments. [ABSTRACT FROM AUTHOR]

In recent years, evolutionary techniques have been widely used to search for the global optimum of combinatorial non-linear non-convex problems. In this paper, we present a new algorithm, named fuzzy Multi-Objective Particle Swarm Optimization (f-MOPSO) to improve conjunctive surface water and groundwater management. The f-MOPSO algorithm is simple in concept, easy to implement, and computationally efficient. It is based on the role of weighting method to define partial performance of each point (solution) in the objective space. The proposed algorithm employs a fuzzy inference system to consider all the partial performances for each point when optimizing the objective function values. The f-MOPSO algorithm was compared with two other well-known MOPSOs through a case study of conjunctive use of surface and groundwater in Najafabad Plain in Iran considering two management models, including a typical 12-month operation period and a 10-year planning horizon. Overall, the f-MOPSO outperformed the other MOPSO algorithms with reference to performance criteria and Pareto-front analysis while nearly fully satisfying water demands with least monthly and cumulative groundwater level (GWL) variation. The proposed algorithm is capable of finding the unique optimal solution on the Pareto-front to facilitate decisions to address large-scale optimization problems. [ABSTRACT FROM AUTHOR]

The feasibility of groundwater monitoring was investigated by a case study on adopting smart water meters to measure groundwater extraction at individual farms and a centralized online information management system to measure collective aquifer water extraction. Benefits of optimal groundwater management was estimated using hydro-economic models that simulate, for a 70-year period, private and social optimality, taking into account the effects of seawater intrusion on groundwater salinity. A Bayesian inference system was used as an interface between a dynamic programming model and MODFLOW groundwater simulation model. The case study’s cost data were scaled-up to the aquifer level and compared to the incremental benefits between private and socially optimal water extraction. The results showed that the Net Present Value of measuring and monitoring groundwater extraction using smart water meters as $790 million ($1332/ha/year) with an Internal Rate of Return of 93%. The sustainable use of the aquifer results to a reduction of the cropped area by 10%, a reduction of the groundwater extraction by 20%, a change in the crop mix, and 42% of the least-efficient farms exiting farming. The exiting farmers could convert farm lands to other land uses such as residential, urban, industrial land use, with adequate facilitation provided by the government. The impact of change of groundwater management strategy on the arid ecology with reduction in tree cover is noted. [ABSTRACT FROM AUTHOR]

Serious environmental management games can improve understanding of practical environmental sustainability challenges by offering opportunities to obtain first-hand experiences that may be otherwise too costly, difficult or dangerous to reproduce in reality. Game-based learning (GBL) has been found to increase soft skills, such as critical thinking, creative problem solving and teamwork, as well as to improve cognitive development, learning retention and social learning, which are important for future environmental researchers and professionals. Environmental management games can be applied in educational settings to promote awareness about sustainable resource planning and management among citizens who are increasingly exposed to products of the information age. This paper provides an overview of game-based learning and the state of serious games (SG) for environmental management, offering insight into their potential as effective tools in facilitating environmental education. SGs have been shown to possess numerous qualities that have been connected with improved learning experiences and cognitive development, but research must continue to study the SGs’ efficacy. Shortcomings found with games reviewed are that few evaluate or explain pedagogical foundation, and many are hard to implement or not accessible. Methods employed in determining the effectiveness of SGs vary greatly among environmental studies, necessitating a standardized methodology to reduce disparities in testing procedures. Furthermore, a centralized source, effectively an online database for SGs, is needed for locating and obtaining information pertaining to the available environmental games and their most appropriate applications. [ABSTRACT FROM AUTHOR]

In practice, selecting an energy project for development requires balancing criteria and competing stakeholder priorities to identify the best alternative. Energy source selection can be modeled as multi-criteria decision-maker problems to provide quantitative support to reconcile technical, economic, environmental, social, and political factors with respect to the stakeholders' interests. Decision making among these complex interactions should also account for the uncertainty present in the input data. In response, this work develops a stochastic decision analysis framework to evaluate alternatives by involving stakeholders to identify both quantitative and qualitative selection criteria and performance metrics which carry uncertainties. The developed framework is illustrated using a case study from Fairbanks, Alaska, where decision makers and residents must decide on a new source of energy for heating and electricity. We approach this problem in a five step methodology: (1) engaging experts (role players) to develop criteria of project performance; (2) collecting a range of quantitative and qualitative input information to determine the performance of each proposed solution according to the selected criteria; (3) performing a Monte-Carlo analysis to capture uncertainties given in the inputs; (4) applying multi-criteria decision-making, social choice (voting), and fallback bargaining methods to account for three different levels of cooperation among the stakeholders; and (5) computing an aggregate performance index (API) score for each alternative based on its performance across criteria and cooperation levels. API scores communicate relative performance between alternatives. In this way, our methodology maps uncertainty from the input data to reflect risk in the decision and incorporates varying degrees of cooperation into the analysis to identify an optimal and practical alternative. [ABSTRACT FROM AUTHOR]

Historically, the U.S. waste collection fleet was dominated by diesel-fueled waste collection vehicles (WCVs); the growing need for sustainable waste collection has urged decision makers to incorporate economically efficient alternative fuels, while mitigating environmental impacts. The pros and cons of alternative fuels complicate the decisions making process, calling for a comprehensive study that assesses the multiple factors involved. Multi-criteria decision analysis (MCDA) methods allow decision makers to select the best alternatives with respect to selection criteria. In this study, two MCDA methods, Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Simple Additive Weighting (SAW), were used to rank fuel alternatives for the U.S. waste collection industry with respect to a multi-level environmental and financial decision matrix. The environmental criteria consisted of life-cycle emissions, tail-pipe emissions, water footprint (WFP), and power density, while the financial criteria comprised of vehicle cost, fuel price, fuel price stability, and fueling station availability. The overall analysis showed that conventional diesel is still the best option, followed by hydraulic-hybrid WCVs, landfill gas (LFG) sourced natural gas, fossil natural gas, and biodiesel. The elimination of the WFP and power density criteria from the environmental criteria ranked biodiesel 100 (BD100) as an environmentally better alternative compared to other fossil fuels (diesel and natural gas). This result showed that considering the WFP and power density as environmental criteria can make a difference in the decision process. The elimination of the fueling station and fuel price stability criteria from the decision matrix ranked fossil natural gas second after LFG-sourced natural gas. This scenario was found to represent the status quo of the waste collection industry. A sensitivity analysis for the status quo scenario showed the overall ranking of diesel and fossil natural gas to be more sensitive to changing fuel prices as compared to other alternatives. [ABSTRACT FROM AUTHOR]

Abstract: Post-Soviet legal governance regime of Caspian Sea – the largest inland body of water on earth – remains a source of conflict among the five coastal states of Azerbaijan, Iran, Kazakhstan, Russia, and Turkmenistan. Although different division methods have been suggested for sharing the sea and its valuable resources, the actual gain of the countries is unclear as the proposed methods focus either on the oil and gas or the areal share of the parties. The Caspian Sea Negotiation Support System (Caspian Sea NSS) is developed in this study to delineate optimal boundaries for sharing the sea through simultaneous consideration of the countries' areal and resource shares under different sharing methods. This NSS is a complex optimization model, with a solver engine that provides reliable results with a reasonable computational effort using a heuristic method. The model is run under different division scenarios to evaluate the sensitivity of each party's gain and locations of nautical boundaries to the division rules and the economic values of the resources. Results show a high sensitivity of the optimal nautical boundaries to the division rules and an indirect relationship between the allocated area and resource shares. The findings highlight the necessity for considering utility shares in negotiations as opposed to adopting areal division rules which ignore the utilities and might result in unfair resource allocation. The main policy implication of the study is that clarification of the countries' resource and areal gain under any suggested legal regime for governing the Caspian Sea is essential to the success of the negotiations. [Copyright &y& Elsevier]

Abstract: Game theory provides a useful framework for studying Multi-Criteria Decision Making problems. This paper suggests modeling Multi-Criteria Decision Making problems as strategic games and solving them using non-cooperative game theory concepts. The suggested method can be used to prescribe non-dominated solutions and also can be used as a method to predict the outcome of a decision making problem. Non-cooperative stability definitions for solving the games allow consideration of non-cooperative behaviors, often neglected by other methods which assume perfect cooperation among decision makers. To deal with the uncertainty in input variables a Monte-Carlo Game Theory (MCGT) approach is suggested which maps the stochastic problem into many deterministic strategic games. The games are solved using non-cooperative stability definitions and the results include possible effects of uncertainty in input variables on outcomes. The method can handle multi-criteria multi-decision-maker problems with uncertainty. The suggested method does not require criteria weighting, developing a compound decision objective, and accurate quantitative (cardinal) information as it simplifies the decision analysis by solving problems based on qualitative (ordinal) information, reducing the computational burden substantially. The MCGT method is applied to analyze California’s Sacramento-San Joaquin Delta problem. The suggested method provides insights, identifies non-dominated alternatives, and predicts likely decision outcomes. [Copyright &y& Elsevier]

Abstract: Cooperative game theory solutions can provide useful insights into how parties may use water and environmental resources and share any benefits of cooperation. Here, a method based on Nash and Nash–Harsanyi bargaining solutions is developed to explore the Federal Energy Regulatory Commission (FERC) relicensing process, in which owners of non-federal hydropower projects in the United States have to negotiate their allowable operations, with other interest groups (mainly environmental). Linkage of games to expand the feasible solution range and the “strategic loss” concept are discussed and a FERC relicensing bargaining model is developed for studying the bargaining stage (third stage) of the relicensing process. Based on the suggested solution method, how the lack of incentive for cooperation results in long delay in FERC relicensing in practice is explained. Further, potential effects of climate change on the FERC relicensing are presented and how climate change may provide an incentive for cooperation among the parties to hasten the relicensing is discussed. An “adaptive FERC license” framework is proposed, based on cooperative game theory, to improve the performance and adaptability of the system to future changes with no cost to the FERC, in face of uncertainty about future hydrological and ecological conditions. [ABSTRACT FROM AUTHOR]

Inefficient collection and scheduling procedures negatively affect residential curbside collection (RCC) efficiency, greenhouse gas (GHG) emissions, and cost. As Florida aims to achieve a 75% recycling goal by 2020, municipalities have switched to single-stream recycling to improve recycling efficiency. Waste diversion and increased collection cost have forced some municipalities to reduce garbage collection frequency. The goal of this study was to explore the trade-offs between environmental and economic factors of RCC systems in Florida by evaluating the RCC system design of 25 different Central Florida communities. These communities were grouped into four sets based on their RCC garbage, yard waste, and recyclables collection design, i.e., frequency of collection and use of dual-stream (DS) or single-stream (SS) recyclables collection system. For the 25 communities studied, it was observed that RCC programs that used SS recyclables collection system recycled approximately 15–35%, by weight of the waste steam, compared to 5–20% for programs that used DS. The GHG emissions associated with collection programs were estimated to be between 36 and 51 kg CO 2eq per metric ton of total household waste (garbage and recyclables), depending on the garbage collection frequency, recyclables collection system (DS or SS), and recyclables compaction. When recyclables offsets were considered, the GHG emissions associated with programs using SS were estimated between −760 and −560, compared to between −270 and −210 kg CO 2eq per metric ton of total waste for DS programs. These data suggest that RCC system design can significantly impact recyclables generation rate and efficiency, and consequently determine environmental and economic impacts of collection systems. Recycling participation rate was found to have a significant impact on the environmental and financial performance of RCC programs. Collection emissions were insignificant compared to the benefits of recycling. SS collection of recyclables provided cost benefits compared to DS, mainly due to faster collection time. [ABSTRACT FROM AUTHOR]

Early identification and detection of abnormal patterns is vital for a number of applications. In manufacturing for example, slide shifts and alterations of patterns might be indicative of some production process anomaly, such as machinery malfunction. Usually due to the continuous flow of data, monitoring of manufacturing processes and other types of applications requires automated control chart pattern recognition (CCPR) algorithms. Most of the CCPR literature consists of supervised classification algorithms. Fewer studies consider unsupervised versions of the problem. Despite the profound advantage of unsupervised methodology for less manual data labeling their use is limited due to the fact that their performance is not robust enough and might vary significantly from one algorithm to another. In this paper, we propose the use of a consensus clustering framework that takes care of this shortcoming and produces results that are robust with respect to the chosen pool of algorithms. Computational results show that the proposed method achieves not less than 79.10% G -mean with most of test instances achieving higher than 90%. This happens even when in the algorithmic pool are included algorithms with performance less than 15%. To our knowledge, this is the first paper proposing an unsupervised consensus learning approach in CCPR. The proposed approach is promising and provides a new research direction in unsupervised CCPR literature. [ABSTRACT FROM AUTHOR]

Reforms currently under way in China's electricity markets bear important implications for its decarbonization objectives. The southwestern province of Yunnan is among the provinces piloting the current iteration of power market reforms. As such, lessons from Yunnan will inform future market reform and renewable energy policies in China and potentially elsewhere. The dominance of hydropower in Yunnan's energy portfolio and the particular transmission constraints it faces, offer an interesting case study of the challenges of decarbonization. We report on market architecture reforms and aggregate market data collected from the Yunnan Power Exchange. We review four elements in the reformed market architecture. Market pricing rules, transitional quantity controls, the generation rights market, and inter-provincial trade. The specifics of market reform reflect a compromise between decarbonization, inter-provincial competition, grid security and development objectives and contribute to understanding of how the dual transitions of hydropower decarbonization and market liberalization interact. We conclude on six insights regarding the role of the grid operator, security checks on trade, integration of cascade hydropower, the inclusion of renewables in the generation rights market, price controls, and market participant price uncertainty. [ABSTRACT FROM AUTHOR]

The uncertainty of a changing climate raises challenges for water infrastructure planning and design. Not accounting for nonstationarity may result in under-designed structures that fail too frequently, or over-designed structures that are economically inefficient. This concern is magnified by uncertainty in the long-term frequency and magnitude of future extreme events. Planning strategies that allow adaptations over a structure's life could improve both reliability and economic efficiency. This study develops a method to inform adaptive water infrastructure planning with uncertain hydrologic and other forms of nonstationarity, applied to levee system planning. A stochastic dynamic programming model including a Markov process is developed for infrastructure planning with uncertain nonstationarity in flood frequency. Bayes' theorem is used to update peak flow probabilities conditioned on observed past peak flows and to update expected residual flood damages over time. A levee system planning problem with a numerical example from California illustrates the approach to derive optimal levee heights over time, and economic values of adapting to uncertain nonstationary flood risk. The projected range of probabilistic hydrology scenarios affects the optimal results, particularly in later planning stages as hydrology scenarios diverge with time. Adaptive planning strategies allowing more levee upgrades over time slightly lowers the overall cost and provides better flood protection than one-time construction under nonstationary hydrology for any climate in the example. Compared to a known future nonstationary hydrology, incorporating uncertain nonstationary climate results in higher levees being planned for observed severe hydrology scenarios in later stages. The overall present value cost with uncertain nonstationary climate depends on rates of change in peak flow distribution parameters in future hydrology scenarios. [ABSTRACT FROM AUTHOR]

Abstract: Increasing private sector involvement in transportation services has significant implications for the management of road networks. This paper examines a concession model’s effects on a road network in the mid-sized city of Fresno, California. Using the existing transportation planning models of Fresno, we examine the effects of privatization on a number of typical system performance measures including total travel time and vehicle miles traveled (VMT), the possibility of including arterials, and the differences between social cost prices and profit maximizing prices. Some interesting insights emerge from our analysis: (1) roads cannot be considered as isolated elements in a concession model for a road network; (2) roads can function as complements at some levels of demand and become substitutes at other levels; (3) policy makers/officials should consider privatizing/pricing arterials along with privatizing highways; (4) temporally flexible but limited price schedule regulations should be part of leasing agreements; and (5) non-restricted pricing may actually worsen system performance, while limited pricing can raise enormous profits as well as improve system performance. [Copyright &y& Elsevier]

Abstract: This study evaluates climate change impacts on crop production and water productivity of four major crops (wheat, barley, rice, and corn) in Iran''s Zayandeh-Rud River Basin. Multi-model ensemble scenarios are used to deal with uncertainties in climate change projections for the study period (2015–2044). On average, monthly temperature will increase by 1.1 to 1.5°C under climate change. Monthly precipitation changes may be positive or negative in different months of the year. Nevertheless, on the annual basis, precipitation will decrease by 11 to 31% with climate change. While warming can potentially shorten the crop growth period, crop production and water productivity of all crops are expected to decrease due to lower precipitation and higher water requirements under higher temperature. Out of the four studied crops, rice and corn are more vulnerable to climate change due to their high irrigation water demand. So, their continued production can be compromised under climate change. This finding is of particular importance, given the locally high economic and food value of these crops in central Iran. [Copyright &y& Elsevier]

• Regional resource availability affects the desirability of an energy technology. • Some low-carbon technologies can lead to unsustainable outcomes. • Unintended impacts on other valuable resources reduces desirability. • Nuclear, geothermal and onshore wind are amongst the most desirable technologies across MENA. • Hydropower and biomass are not desirable across the region. Global warming urges governments to decarbonise their energy portfolios. Many governments have indistinctly transited away from fossil fuels towards renewable energies. However, a sustainable turnaround calls for limiting the overall impact of this transition on environmental resources and the economy. This study identifies the most desirable energy alternatives for the Middle Eastern and North African (MENA) countries based on their environmental and economic footprints using the Relative Aggregate Footprint (RAF) indicator. The RAFs of eleven widespread electricity generation technologies are computed for each country by considering the availability of four resources – energy, water, land, and the economy – which were weighted according to the national resource availability conditions. The results indicate that the MENA region must adapt their electricity mix to mitigate the impacts of climate change with respect to resource availability conditions, especially their scarce water resources. Due to the specificities of this region, deploying biomass and large-scale hydropower may lower greenhouse gases but significantly alter the impact on other valuable natural resources. Therefore, wind, geothermal, and nuclear power plants seem more desirable for a transition away from carbon-intensive technologies while their secondary effects on other resources (e.g. nuclear energy's possible water and environmental impacts) must be carefully considered. [ABSTRACT FROM AUTHOR]