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Household sector is the end consumer in the socio-economic water cycle and consumes a large amount of water in both physical and virtual ways. The life cycle water consumption by households, also denoted as Blue Water Footprint (BWF), is mostly affected by consumption expenditure and varies among different household groups. This study assessed the household BWF in China by integrating the input-output model with a national survey of household consumption expenditure. The logarithmic mean divisia index method was employed to examine the influencing factors in BWF disparity among the different household groups. We found that 1) per capita BWF, on national average, increased from 157 m3 in 2012 to 176 m3 in 2018; 2) in 2012, urban households, on average, consumed 185 m3 of water per capita annually, while rural households consumed 126 m3 per capita; there is a significant difference in dietary between rural and urban households, e.g., starchy foods predominate in rural dietary while animal-based foods are more preferred in urban regions; 3) BWF shows distinctive regional characteristics, e.g., more water was consumed on clothes in cold regions and on aquatic products in coastal regions; 4) income disparity is a key factor in causing the BWF difference. Although BWF has a positive correlation with income, its marginal growth diminishes, because of the fading consumption propensity and the switch of consumption pattern from water-consuming to water-saving, e.g., rich households are inclined to spend most of the money on education and recreation rather than food. This study is of significance in understanding how the BWF varies among different households, and how the BWF is affected by the influencing factors, including income, propensity to consume, consumption pattern, and virtual water intensity. Image 1 [ABSTRACT FROM AUTHOR]

To determine whether China can achieve the commitment of reducing carbon emission intensity in 2030, a general regression neural network (GRNN) forecasting model based on improved fireworks algorithm (IFWA) optimization is constructed to forecast total carbon emissions (TCE) and carbon emissions intensity (CEI) in 2016–2040. Random forests (RF) method is used to select the important carbon emissions influencing factors to reduce data redundancy. The superiority of IFWA-GRNN forecasting model is verified by historical data from 1990 to 2015. The basic as usual (BAU), policy tightening (PT) and market allocation (ML) scenarios are set to forecast the TCE and CEI. The results show that under the BAU scenario, China's CEI reduction commitments in 2020 (40%–45%) can be achieved, but the commitment in 2030 (60%–65%) cannot be achieved. Under the PT and ML scenarios, China can achieve its CEI commitments in 2030, and the TCE will decrease gradually after reaching its peak in 2030. Under the existing macro development planning and policy intensity in China, there are still certain pressures to achieve CEI reduction targets. It is necessary to implement policy adjustment and market mechanism incentives for both energy supply and consumption, optimize power supply structure, promote electric energy substitution, and accelerate the construction of a unified national electricity market, carbon market, etc. Image 10939 • A hybrid carbon emissions forecasting model based on improved general regression neural network is proposed. • The influencing factors of carbon emissions is selected by random forests. • Basic as usual, policy tightening and market allocation scenarios are set, the carbon emission intensity will decrease by 54.7%, 63.4% and 60.8% in 2030 compared to 2005. [ABSTRACT FROM AUTHOR]