Accurate multi-objective prediction of CO2 emission performance indexes and industrial structure optimization using multihead attention-based convolutional neural network.

The establishment of specific targets for the global carbon peaking and neutrality raises urgent requirements for prediction of CO2 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.

Evaluating the water-saving and wastewater-reducing effects of water rights trading pilots: Evidence from a quasi-natural experiment.

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.

Evaluating the effectiveness of the water-saving society construction in China: A quasi-natural experiment.

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.

Spatiotemporal characteristics and determinants of agricultural carbon offset rate in China based on the geographic detector.

This paper attempts to explore the spatiotemporal variation characteristics of the agricultural carbon offset rate (ACOR) and the reasons that shape its differentiation characteristics in China. To achieve this objective, the Dagum Gini coefficient, kernel density estimation, and geographic detector model are employed in this study. The results show that there are some differences in ACOR among regions in China. Interregional differences are the main source of their overall variation. Excluding the spatial conditions, the ACOR of each province in the sample period shows low mobility characteristics. Considering the spatial conditions, there is convergence in the lower-middle neighborhoods. The three-year lag period did not significantly affect the interaction of ACOR between regions under the accession time horizon. At the aggregate level, the spatial and temporal divergence in China's ACOR is driven by urbanization rate, agricultural fiscal expenditure, and rural education level. As for the regional level, the scale of household farmland operation plays a greater role in determining the spatiotemporal variation of the eastern and central regions' ACOR. While urbanization rate is more determinant for the western region, the interaction between any two factors has significantly higher explanatory power for the spatial and temporal variation of ACOR than the single factor.

Does the Digital Economy Promote the Reduction of Urban Carbon Emission Intensity?

The impact of the digital economy is increasing, and its environmental effect has attracted more and more attention. The digital economy promotes the improvement of production efficiency and the government's environmental governance capacity, and contributes to the reduction of urban carbon emission intensity. In order to study the impact of digital economy development on urban carbon emission intensity, this paper analyzes the theoretical basis of the digital economy on the reduction of carbon emission intensity, and then, based on the panel data of cities from 2011 to 2019, uses the two-way fixed effect model for empirical testing. The regression results show that the development of the digital economy has promoted the reduction of carbon emission intensity of cities, promoted the green transformation and upgrading of cities, and lays a foundation for China to achieve carbon peaking and carbon neutralization through the improvement of human capital investment and green innovation level. The basic conclusion is robust by changing core explanatory variables, changing samples, replacing regression methods, and shrinking and truncating tests. The impact of the digital economy on urban carbon emission intensity varies with the location, grade and size of the city. Specifically, the development of the digital economy in cities in the eastern and central regions, cities at or above the sub provincial level, large cities and non-resource-based cities has promoted the reduction of urban carbon emission intensity. In terms of resource-based cities, the development of the digital economy in renewable resource-based cities and resource-based cities dominated by iron ore and oil mining has promoted the decline in urban carbon emission reduction intensity.

Associations between Greenspaces and Individual Health: A Longitudinal Study in China.

Using a longitudinal survey in China, this study identifies the effect of greenspaces on individual health in the aspects of self-rated health, mental health, feeling physical discomfort, and being hospitalized. The normalized difference vegetation index (NDVI) is used to measure the greenery cover of each city, and findings show that higher NDVI leads to the improvement of personal self-rated health status, and it also decreases the probability of being hospitalized, having symptoms of physical discomfort, and being lost in bad mood. The positive health effect of NDVI in the city is much more significant and larger for the middle-aged, the elderly, and the low-educated. The evidence of the three possible channels through which the NDVI of each city shows a positive correlation with individual physical and mental health is found. The increased NDVI in the city encourages residents to foster healthier habits (e.g., decreasing smoking or drinking, increasing sleeping hours), improves air quality and reduces air pollution in each city, and promotes the social cohesion of people, and so the health status of residents is enhanced. This study implies that increasing greenspaces in the city is an effective strategy to improve social welfare and residents' health.

Blue sky defense in low-carbon pilot cities: A spatial spillover perspective of carbon emission efficiency.

The improvement of carbon emission efficiency is crucial to the realization of the global carbon peaking and carbon neutrality goals. Based on the panel data of 282 cities from 2004 to 2018 in China, this paper employs the stochastic frontier analysis approach combined with the Sheppard distance function to calculate the total factor carbon emission efficiency of each city. Regarding the low-carbon pilot city policy as a quasi-natural experiment, we evaluate the impact of the pilot policy on carbon emission efficiency and its spatial spillover effect using the spatial difference-in-differences model. The results show that the pilot policy can significantly improve the carbon emission efficiency and has long-term dynamic effects. Also, the effect of the policy has spatial spillover, and has a positive impact on the neighboring cities. Mechanism analysis implies that optimal allocation of resources, energy conservation and green technology innovation make the low-carbon policy play an important intermediary role in promoting carbon emission efficiency. Besides, the effects of the pilot policy have obvious heterogeneity, especially cities in large, higher population densities and the north. These findings reveal that low-carbon pilot city policies are indispensable for both implementing the dual-carbon strategy and winning the defense for the blue sky.

Does the SO2 emissions trading scheme encourage green total factor productivity? An empirical assessment on China's cities.

As a market-based environmental regulation tool, emissions trading scheme for SO2 (SO2 ETS) has been carried out in China for decades, so impacts of SO2 ETS have become a vital issue to the society. Based on the SO2 ETS of China in 2007, this paper attempts to test and verify impacts of the scheme on environment and economy, especially on green total factor productivity (TFP). We firstly combine biennial weight-modified non-radial direction distance function and Luenberger productivity indicator to measure and decompose the green TFP of 280 cities in China over the period 2003 to 2016, and apply a difference-in-differences method (DID) with fixed effect models to investigate whether SO2 ETS achieves a win-win scenario of "emission reducing" and "efficiency increasing." The results show that the scheme significantly may decrease SO2 emissions and SO2 intensity by 12.3% and 11.0%, respectively, in ETS regions while no obvious impact on GDP. In terms of green TFP, we find SO2 ETS inhibits the growth of green TFP, and the negative impact mainly caused by the deterioration in efficiency change. Therefore, we hold that SO2 ETS is effective for improving environment, but it is still difficult to achieve the promotion of green TFP simultaneously.