RNN-GWR: A geographically weighted regression approach for frequently updated data

Generalized Geographically Weighted Regression Model within a Modularized Bayesian Framework

Geographically weighted regression (GWR) models handle geographical dependence through a spatially varying coefficient model and have been widely used in applied science, but its general Bayesian extension is unclear because it involves a weighted log-likelihood which does not imply a probability distribution on data. We present a Bayesian GWR model and show that its essence is dealing with partial misspecification of the model. Current modularized Bayesian inference models accommodate partial misspecification from a single component of the model. We extend these models to handle partial misspecification in more than one component of the model, as required for our Bayesian GWR model. Information from the various spatial locations is manipulated via a geographically weighted kernel and the optimal manipulation is chosen according to a Kullback-Leibler (KL) divergence. We justify the model via an information risk minimization approach and show the consistency of the proposed estimator in terms of a geographically weighted KL divergence.

The impact of heterogeneous environmental regulations on regional spatial differences in net carbon emissions

Effected by spatial difference and types of environmental regulation, enhanced environmental regulation does not necessarily lead to reduce net carbon emissions (NCE). Based on panel data of 30 province-level administrative regions in China from 2003 to 2018, this paper analyzed the impact of heterogeneous environmental regulations on NCE using geographically weighted regression (GWR). The results showed that (1) the total NCE of each province in China presented an increasing trend followed by fluctuation, with obvious spatial aggregation characteristics. (2) The intensity of four environmental regulations, command-and-control (CAC), market-incentive (MI), public-participation (PP), and voluntary environmental program of firm (VEPF) tended to increase slightly over the study period with decreasing spatial heterogeneity. (3) CAC environmental regulations had a "green paradox" effect on NCE, meaning an improving CAC environmental regulation would surprisingly increase its negative effects and contribute to NCE growth. The effect of MI environmental regulation on NCE shifted from overall promotion to partly promotion and even suppression. PP and VEPF environmental regulation would reduce NCE. The suppressive effect of PP environmental regulation grew over time, while VEPF did not. Finally, based on the research conclusion, some policy suggestion are proposed for differentiated management, diverse system of governance, and collaborative governance.

Carbon price forecasting using multiscale nonlinear integration model coupled optimal feature reconstruction with biphasic deep learning

Precise carbon price forecasting matters a lot for both regulators and investors. The improvement of carbon price forecasting can not only provide investors with rational advice but also make for energy conservation and emission reduction. But traditional methods do not perform well in prediction because of the nonlinearity and non-stationarity of carbon price. In this study, an innovative multiscale nonlinear integration model is proposed to improve the accuracy of carbon price forecasting, which combines optimal feature reconstruction and biphasic deep learning. For one thing, the optimal feature reconstruction, including variational mode decomposition (VMD) and sample entropy (SE), is used to extract different features from the original carbon price effectively. For another thing, biphasic deep learning based on deep recurrent neural network (DRNN) and gate recurrent unit (GRU) is applied to predict carbon price. DRNN, a novel framework of deep learning, is applied to predict each component. Meanwhile, GRU is used for nonlinear integration, and the final prediction of carbon price can be acquired through this procedure. For illustration and comparison, this study takes carbon price from Beijing, Hubei, and Shanghai in China as sample data to examine the capability of the proposed model. The empirical result proves that the new hybrid model can improve the carbon price predictive accuracy in consideration of statistical measurement. Hence, the novel hybrid model can be considered as an efficient way of predicting carbon prices.

Conditional Uncorrelation and Efficient Subset Selection in Sparse Regression

Given m d -dimensional responsors and n d -dimensional predictors, sparse regression finds at most k predictors for each responsor for linear approximation, 1 ≤ k ≤ d-1 . The key problem in sparse regression is subset selection, which usually suffers from high computational cost. In recent years, many improved approximate methods of subset selection have been published. However, less attention has been paid to the nonapproximate method of subset selection, which is very necessary for many questions in data analysis. Here, we consider sparse regression from the view of correlation and propose the formula of conditional uncorrelation. Then, an efficient nonapproximate method of subset selection is proposed in which we do not need to calculate any coefficients in the regression equation for candidate predictors. By the proposed method, the computational complexity is reduced from O([1/6]k³+(m+1)k²+mkd) to O([1/6]k³+[1/2](m+1)k²) for each candidate subset in sparse regression. Because the dimension d is generally the number of observations or experiments and large enough, the proposed method can greatly improve the efficiency of nonapproximate subset selection. We also apply the proposed method in real scenarios of dental age assessment and sparse coding to validate the efficiency of the proposed method.

Influence of transportation network on transmission heterogeneity of COVID-19 in China

In this paper, we propose a novel approach to model spatial heterogeneity for epidemic spreading, which combines the relevance of transport proximity in human movement and the excellent estimation accuracy of deep neural network. We apply this model to investigate the effects of various transportation networks on the heterogeneous propagation of COVID-19 in China. We further apply it to predict the development of COVID-19 in China in two scenarios, i.e., i) assuming that different types of traffic restriction policies are conducted and ii) assuming that the epicenter of the COVID-19 outbreak is in Beijing, so as to illustrate the potential usage of the model in generating various policy insights to help the containment of the further spread of COVID-19. We find that the most effective way to prevent the coronavirus from spreading quickly and extensively is to control the routes linked to the epicenter at the beginning of the pandemic. But if the virus has been widely spread, setting restrictions on hub cities would be much more efficient than imposing the same travel ban across the whole country. We also show that a comprehensive consideration of the epicenter location is necessary for disease control.