Coping with the relation between the increase in carbon emissions and energy consumption in the transportation sector is a pressing issue today. Machine learning and deep neural networks were used in this study to explore the influential factors and trends in future transportation carbon emissions. First, the least absolute shrinkage and selection operator (LASSO) regression was adopted to screen out the key influencing factors in transportation carbon emissions. Second, the prediction performance of the long short-term memory (LSTM) network, generalized regress neural network (GRNN), and back propagation (BP) network were compared, and an improved LSTM optimized by the sparrow search algorithm (SSA) was proposed. Third, LASSO-SSA-LSTM was used to predict the transportation sector’s future carbon emissions trends under different scenarios. The results suggested that transportation carbon emissions in China presented a trend of “rapid increase - fluctuating decrease - continuous increase” from 2010 to 2019. Although the main determinant in curbing the rising rate of carbon emissions effectively is the continuous development of new clean energy technology, the variation in transportation carbon emissions in China under eight scenarios showed significant differences. Generally, systemic changes and innovations are crucial to accommodate China’s future low-carbon and sustainable transportation development.
Emotion plays an important role in heterogeneous investments and has some direct effects on the cooperation behaviour of a player in a public goods game (PGG). How this irrational factor affects the heterogeneous investments and what level of cooperators in players with emotions are still unknown to us. Here, the heterogeneous investments induced by emotions into a PGG were introduced. The emotional index was firstly quantified by considering a memory-cumulative effect, and then an investment formula was proposed based on this emotional index. At last, the effect of emotions on the cooperation behaviour in a PGG was investigated. Results show that the heterogeneous investments induced by emotions can improve cooperation significantly in a PGG, and that an increase of the memory length, the emotional increment, or the memory discounting factor can improve the cooperation level.
The sequential pricing game model is an approach that can be effectively used to solve the problem with multi-oligopoly pricing mechanisms in raw material supply chains. However, the existing sequential pricing mechanism does not fully consider constraints such as the purchase volume of downstream firms and the change information of each parameter, which leads to the pricing mechanism being detached from the real market. According to the concept of the sequential pricing game model being used among multi-oligopolies under constraints, we constructed the constrained sequential pricing game model by incorporating the parameters related to the product demand function, marginal production cost, dominant coefficient, following coefficient, and agreed minimum purchase volume as constraints, and the model was converted into a nonlinear bilevel programming model to facilitate model solving. Furthermore, we provided the analytical solution formulas for six special cases, thus making the model more similar to the real market. In addition, the effects of the agreed minimum purchase volume and the dominant and following coefficients on the equilibrium quoted prices and profits of the firms were analyzed. The results of the numerical simulation show that the constrained sequential pricing game model is more effective than the unconstrained sequential pricing game model in solving the problem with the multi-oligopoly pricing mechanism, which means that it can be used to establish a better pricing mechanism and provide a more reasonable and scientific basis for market operation and policymakers in solving practical problems.
Focusing on the water conservation of China’s urban agglomerations (UAs), panel data covering 92 cities in the top five agglomerations from 2006 to 2020 are used to study the relationship between the spatial structure of UAs and the water ecological footprint (WEF) of their cities. WEFs and spatial structures are measured by the ecological footprint models and the rank-size law, respectively. Furthermore, the effects of spatial structure on WEF are estimated through the fixed-effects (FE) model with instrumental variables (IVs). Results suggest that the concentricity of the spatial structure has a nonlinear impact on the WEF, in that as the spatial structure moves away from polycentricity, the WEF first declines and then rises. By reducing the WEF through concentrated development, cities with a large proportion of production WEF or a large population can enjoy more benefits. Therefore, promoting the balanced development of JJJ and PRD and enhancing the role of the growth pole in CY and YRMR can help the water conservation of most cities. Moreover, considering household water use and small-population cities in other water-saving policies can serve as a policy reference in the future.
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