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Integrating renewable electricity and green hydrogen with CO 2 utilization to produce chemicals has drawn much attention due to its low carbon emission characteristics. The goal of this paper is to investigate the environmental sustainability of producing a steady hourly output of methanol by utilizing electricity and hydrogen produced via renewable energy resources, and CO 2 captured from a coal-fired power plant located in Inner Mongolia, China. Six renewable energy-based electricity and hydrogen systems are considered based on photovoltaic, wind turbines and combinations thereof, including battery and grid technologies. The environmental impact indicators of these system are compared comprehensively through life cycle assessment approach, including not only global warming potential but also fossil fuel depletion, water & soil & air pollution indices and human health index. The results indicate that the most environmentally friendly system is Case B2 which uses photovoltaic to produce hydrogen and electricity, with battery for energy storage and excess electricity sold back to grid. It shows apparently the best sustainability performance in global warming potential, which is 0.105 kg CO 2 -eq per kg methanol. In general, the proposed six systems outperform conventional methanol production systems in terms of global warming potential and abiotic depletion potential, with reductions ranging from 74% to 92% and 51.1%–73.8% respectively, but they have high levels in other environmental sustainability indicators such as human toxicity, eutrophication potential, and ozone layer depletion potential due to the photovoltaic electricity generation unit. The findings highlight that it is essential to consider a comprehensive range of environmental sustainability indicators when developing renewable energy-based methanol systems on a large scale. [Display omitted] • Component capacity of the integrated system is optimized. • Comprehensive life cycle environmental impact assessment is conducted. • GWP of proposed route is 74 %–92% lower than fossil-based route. • PV manufacturing is critical in most environmental impact indicators. • HTP, ODP and EP indicators of proposed route are much high. [ABSTRACT FROM AUTHOR]

The paper investigates how climate changes and socioeconomic transformations affected grassland productivity from 2000 to 2017 in counties in Inner Mongolia of China. The grassland productivity and climatic change variables included 162 readings (9 periods in the growing season × 18 years), while ten socioeconomic variables contained 18 yearly readings. The mismatch between the environmental observations and socioeconomic data compounds quantitative analysis of causal relations between them. We applied several time-series processing methods to generate five datasets. We fit them with conventional, dependent-lagged, and dynamic-lagged panel regression models to investigate which combinations augment causal relationships between grassland productivity, climate, and socioeconomic changes. The results confirmed that the conventional model with the original data failed to reveal causal relationships between these factors. The panel regression models with the original data tended to intensify the causal effects of climate factors on grassland productivity. The models with the transformed datasets disclosed more socioeconomic variables showing causal relationships with grassland productivity. The dependent-lagged model with the empirical mode decomposition transformed data produced the highest R-squared value and lowest prediction error. GDP, state fixed asset investment, highway construction, grain plantation, and livestock density were primary factors affecting grassland productivity. In brief, the integration of time-series data mining and time-lagged panel analysis effectively unveiled the causes of grassland deterioration under climate and socioeconomic changes. [Display omitted] • Augment causality hidden in env-socio-economic systems with data assimilation. • Assimilate temporal environmental and socioeconomic data into compatible time-series. • Experiment with four groups of computational data mining methods on data assimilation. • Compare combinations of data assimilation methods and lagged panel regression models. [ABSTRACT FROM AUTHOR]

The construction of fast electric vehicle (EV) charging stations is critical for the development of EV industry. The integration of renewable energy into the EV charging stations comprises both threats and chances. A successful and reasonable capacity configuration and scheduling strategy is beneficial and significant. This paper studies the optimal design for fast EV charging stations with wind, PV power and energy storage system (FEVCS-WPE), which determines the capacity configuration of components and the power scheduling strategy. Firstly, an EV charging load simulation model considering demand response is built, which dynamically modified charging expectation under time-of-use electricity price. Secondly, based on the system design, a multi-objective optimization model is proposed with minimum objectives of cost of electricity and pollution emissions. Then, this model is solved by a hybrid optimization algorithm which combines multi-objective particle swarm optimization (MOPSO) algorithm and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. Finally, the proposed optimization framework is applied to a case in Inner Mongolia, China. A scenario analysis is conducted and concludes that the renewable energy supplies, the connection with utility grid and demand response can help improve the performance on optimization objectives. A sensitivity analysis is also performed to verity the model's effectiveness. In addition, the proposed method is compared with simulated annealing and genetic algorithm to show its faster computation speed and higher solution quality. • A fast EV charging station with renewable energies and ESS is proposed. • An EV charging load simulation model based on demand response is built. • An optimization design model for FEVCS-WPE is proposed with two objectives. • A hybrid optimization algorithm combining MOPSO and TOPSIS method is used. [ABSTRACT FROM AUTHOR]

The creation of an efficient and environmentally friendly irrigation canal schedule strongly depends on the accuracy of the prespecified water demand (PSWD) process in existing scheduling practices and modeling studies. Overestimating water demand would result in spillage losses from over-irrigation, while underestimating water demand would lead to crop yield losses due to deficit water supply. However, prespecifying an accurate water demand often poses various challenges for both farmers and canal administrators, as it involves the temporal variability in agrometeorological conditions, the spatial diversity of soil properties, and the sensitivity of crops to water stress at different growth stages. Therefore, to reduce the uncertainty of prespecifying water demand and avoid the adverse effects of an inaccurate water demand on canal water allocation, this paper proposes an alternative canal scheduling model that maximizes canal scheduling service quality by targeting the ultimate objectives of the irrigation system (UOIS) without considering the prespecified water demand as the model input. The UOIS canal scheduling (UOIS-CS) model aims to optimize the canal water delivery schedule via simulation of soil water and salt migration using the Simultaneous Heat and Water (SHAW) model, to provide the most suitable soil moisture content and soil salinity for crops. The UOIS-CS model is used for the Hetao Irrigation District, Inner Mongolia, a typical agricultural watershed in China, where supplementary irrigation is performed after the autumn harvest (i.e., autumn irrigation) to leach excessive soil salt and preserve a moderate amount of soil moisture for the beginning of the next spring sowing period. To evaluate the irrigation performance of the UOIS-CS model further, a comparative analysis between the UOIS-CS model and the widely used PSWD-based canal scheduling (PSWD-CS) model is conducted. Compared with the PSWD-CS model, the proposed UOIS-CS model exhibits a superior irrigation performance in terms of leaching soil salt and preserving soil water, and a lower risk of canal overload operation during a higher water demand period. The improved performance of the UOIS-CS model is attributed to its greater adaptability to the spatial diversity of soil properties and the temporal variability in agrometeorological conditions, which originates from the full use of the equifinality of model performance metrics and model inputs. The UOIS-CS model provides managers with an effective approach to formulate a canal water delivery schedule with improved irrigation efficiency and lower overload risk. The proposed framework is generic and can be applied to non-crop growing seasons. • The canal scheduling method based on the ultimate objectives of irrigation system is well adapted to the environment. • Scheduling canals with prespecified water demand processes has a higher risk of canal overload operation. • Adopting the SHAW model to simulate soil water and salt migration in canal water allocation optimization. [ABSTRACT FROM AUTHOR]