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Water rights trading is a market mechanism that promotes the conservation, protection and optimal allocation of water resources. This paper takes the water rights trading pilots' policy as a quasi-natural experiment, and then adopts the spatial difference-in-differences model to investigate the water-saving and wastewater-reducing effects of water rights trading pilots by using the panel data of 30 provinces in China from 2006 to 2019. The results indicate that water rights trading has good environmental governance effects on water-saving and wastewater-reducing in the pilot areas. However, water rights transactions are mainly concentrated within provinces, and there is no spatial spillover effect. Besides, the pilot policy has more long-term dynamic effects in reducing sewage discharge than water-saving due to rebound effect possibly. The optimization of the industrial structure and environmental regulation play important roles in the realization of the policy effects of the water rights trading pilots. An important implication is that water rights trading pilots are worthy of promotion throughout China in view of the policy effects, and China should focus on inter-regional market transactions and industrial structure transformation to promote the optimization of the water rights trading mechanism. • Take the water rights trading pilots' policy as a quasi-natural experiment in China. • Adopt the spatial difference-in-difference model with the panel data of 30 provinces. • Evaluate the water-saving and wastewater-reducing effects and exploit its drive mechanism. • Has good environmental governance effects on water-saving and wastewater-reducing in the pilot area. • The optimization of the industrial structure and environmental regulation are important mechanisms. [ABSTRACT FROM AUTHOR]

The conservation and efficient use of water resources are directly related to the sustainable and high-quality development in China. This paper regards the pilot policy of water-saving society construction as a quasi-natural experiment, and employs a differences-in-differences approach to evaluate the effectiveness of urban water-saving policies and exploit its drive mechanism by using the panel data of 263 cities in China from 2001 to 2016. Findings show that the pilot policy of water-saving society construction can achieve better water-saving effects, which can not only promote the reduction of urban water consumption in intensity and total amount, but also improve the efficiency of water resources utilization. And this policy has brought long-term dynamic effects on the urban water resources conservation. Heterogeneous effects exist in different types of cities, that is, the water-saving performance is more significant in cities with sufficient water resources and in central China. Besides, this paper finds that the impact of this water-saving policy on urban water resource conservation is mainly through two channels such as water resources infrastructure investment and water resources pricing. These findings provide useful inspirations for local governments to take effective economic measures to manage water resources. • Regard the pilot policy of water-saving society construction as a quasi-natural experiment. • Employ a differences-in-differences approach with the panel data of 263 cities in China from 2001 to 2016. • Evaluate the effectiveness of urban water-saving policies and exploit its drive mechanism. • Water-saving society construction can achieve better water-saving effects. • Water infrastructure investment and water pricing are important mechanisms. [ABSTRACT FROM AUTHOR]

The establishment of specific targets for the global carbon peaking and neutrality raises urgent requirements for prediction of CO 2 emission performance indexes (CEPIs) and industrial structure optimization. However, accurate multi-objective prediction of CEPIs is still a knotty problem. In the present study, multihead attention-based convolutional neural network (MHA–CNN) model was proposed for accurate prediction of 4 CEPIs and further provided the rational suggestions for further industrial structure optimization. The proposed MHA–CNN model introduces deep learning mechanism with efficient resolution strategies for training model overfitting, feature extraction, and self-supervised learning to acquire the adaptability for CEPIs. Multihead attention (MHA) mechanism plays important roles in influence weight interpretation of variables to facilitate the prediction performance of CNN on CEPIs. The MHA–CNN model presented its overwhelmingly superior performance to CNN model and long short-term memory (LSTM) model, two frequently-used models, in multi-objective prediction of CEPIs using 8 influence variables, which highlighted advantages of MHA module in multi-dimensional feature extraction. Additionally, contributions of influence variables to CEPIs based on MHA analyses presented relatively high consistency with the geographical distribution analyses, indicating the excellent capacity of the MHA module in variable weights identification and contribution dissection. Based on the more accurate prediction results by MHA–CNN than those by CNN and LSTM model, the increase in the tertiary industry and the decreases in the first and secondary industries are conducive to improvement of total-factor carbon emission efficiency and further enhancement of effective energy utilization in regions with inefficient carbon emissions. This study provides insights towards the critical roles of the proposed MHA–CNN model in accurate multi-objective prediction of CEPIs and further industrial structure optimization for improvement of total-factor carbon emission efficiency. • The MHA–CNN model was proposed for multi-objective prediction of carbon emission. • The MHA module was employed to dissect the contributions of influence variables. • The industrial structure was optimized based on TFCE prediction using MHA–CNN. • MHA–CNN showed its superior performance to CNN and long short-term memory model. • The increase in tertiary industry proportion is conducive to the TFCE improvement. [ABSTRACT FROM AUTHOR]