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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]