Carbon dioxide is believed widely to be the major contributor to global warming. Policymakers worldwide are turning to tax policies in an effort to abate carbon emissions. China is the largest emitter of carbon emissions on our planet. The central government, as well as the local official, has introduced a series of environmental regulations, such as environmental protection tax and emissions trading system, to reduce carbon emissions and improve environmental quality. In the near future, the carbon emission tax is also expected to be implemented by the Chinese government. In order to analyze and predict the effect of the carbon emission tax on environmental and economic systems, we developed a four department dynamic stochastic general equilibrium model, which includes households, enterprises, the government, and the environment. The dynamic parameters were obtained using maximum likelihood estimation. In the comparative static-s analysis, we found that after the introduction of carbon emission tax, the level in environmental quality was substantially improved, whereas most economic variables were significantly reduced. Moreover, we used impulse responses functions to evaluate how one shock to the carbon emission tax affects the steady static values for these endogenous variables in our model. We found that the carbon emission tax shock has an instantaneous effect on the majority of economic variables, but it does not affect the environmental quality immediately. In addition, we tested the Porter hypothesis and found no evidence suggesting the statement regarding this hypothesis. Finally, we applied Bayesian estimation to assure our findings in this study, again.

Here, we assessed the impact of low-carbon city pilot projects on carbon emissions across China through application of a series of econometric techniques to data on these three waves of low-carbon city construction. Our baseline results are obtained from a difference-in-differences estimator, comparing cities with and without introducing low-carbon city pilot projects, and show that low-carbon city pilot projects reduce carbon emissions by about 2 percentage points. We found a similar impact of low-carbon city pilot projects on carbon emissions when we controlled for the estimated propensity of a city to launch the low-carbon city pilot project based on a series of urban characteristics. We obtained comparable estimates when we instrumented whether a city would launch the low-carbon city pilot projects using regional waves of low-carbon city pilot projects. Our results also show that low-carbon city pilot projects have a larger impact on carbon emissions in northern, poorer, and less industrialized cities than those with the opposite characteristics. We found little evidence for the persistence of this impact on carbon emissions, implying that it is necessary to dynamically adjust the low-carbon city pilot projects for cities that have launched the project.

We provide evidence that payment for ecological services programs have had a significant and robust positive impact on grassland quality by focusing on China’s grassland ecological compensation policy (GECP)—the planet’s largest. Our baseline results are obtained from a difference-in-differences estimator, comparing counties which have and have not introduced a GECP. It shows that such a policy increases grassland quality by about four percentage points on average. We found a similar impact of the GECP on grassland quality when we controlled for the estimated propensity of a county to launch this policy based on a series of county characteristics, such as weather and economic conditions. We obtained comparable estimates when we used the propensity score to balance county characteristics between counties which have and have not launched the GECP. Our results also show that the policy has a larger impact on grassland quality in warmer, richer, and in less populated counties than those with the opposite characteristics. We found strong suggestions for the persistent impact of the GECP on grassland quality, implying that Chinese officials should persist with the policy and expand the range of the pilot policy. In addition, we carried out a series of robustness tests, including the leave-one-county-out test, bootstrapping test, and the permutation test, to illustrate the robustness of our results.

Innovation, as a driving force to economic growth, has been referred to as an important development strategy by the central government of China. In order to improve the innovative capability of cities, Chinese officials started to construct innovation cities in 2008. Previous studies have investigated the ecological and economic effects of innovation city construction; however, the environmental effect of the project remains unclear. In this study, we constructed an annual panel of 285 cities in China, from 2007 to 2015, to assess the effect of innovation city construction on carbon emissions. Our baseline results are obtained from a difference-in-differences estimator, comparing cities with and without introducing innovation city construction, whose results show that innovation city construction reduces carbon emissions by about 2% on average. We found a similar effect of innovation city construction on carbon emissions when we controlled for the estimated propensity of a city to launch the innovation city construction based on a series of urban characteristics, such as gross regional product and population. We obtained comparable estimates when we used the propensity score as weights to balance urban characteristics between cities with and without launching the innovation city construction. Our results also show that innovation city construction has a larger effect on carbon emissions in western, poorer, and fewer population cities than in those with opposite characteristics. We found suggested the persistence of the effect that innovation city construction had on carbon emissions, implying that the Chinese government should encourage innovation to reduce carbon emissions. Besides, we performed a series of robustness tests, including the leave-one-city-out test, the bootstrapping test, and the permutation test, to illustrate the robustness of our results.

We propose the Enhanced Predictive Recurrent Neural Network (EnPredRNN) based on PredRNN to extend the period of spatio-temporal prediction. To better capture global spatial dependencies, we integrate the Self-Attention (SA) module into PredRNN’s basic unit, forming the Enhanced Long Short-Term Memory (EnLSTM). Aiming at the problem that the standard temporal memory state in PredRNN contains insufficient information about inter-frame motion, we propose the Enhanced Temporal Memory (ETM) by aggregating past multi-step temporal memory states. Aiming at the gradient vanishing problem in Recurrent Neural Networks (RNN), the Alleviating Gradient Vanishing (AGV) structure is used to construct the high-speed path that facilitates gradient propagation. Experimental results show that EnPredRNN effectively extends spatio-temporal prediction from ten to thirty time-steps.

The grey model, which is abbreviated as GM (1, 1), has been widely applied in the fields of decision and prediction, particularly in the prediction of time series with few observations, referred to as the poor information and small sample in the literature related to grey model. Previous studies focus on improving the accuracy of prediction but pay less attention to the robustness of the grey model to outliers, which often occur in practice due to an incorrect record by chance or an accidental failure in equipment. To fill that void, we develop a robust grey model, whose structural parameters are obtained from the least trim squares, to forecast Chinese electricity demand. Also, we use the last value in the first‐order accumulative generating time series as the initial value, according to the new information priority criterion. We name the novel grey model, proposed in this paper, the novel robust grey model integrating the new information priority criterion, which could be abbreviated as NIPC‐GM (1, 1). In addition, we introduce a novel approach, that is, the bootstrapping test, to investigate the robustness against outliers for the novel robust grey model and the classical grey model, respectively. Using the data on Chinese electricity demand from 2011 to 2021, we find that not only does the novel robust grey model integrating the new information priority criterion have a better robustness to outliers than the classical grey model, but it also has a higher accuracy of prediction than the classical grey model. Finally, we apply the novel robust grey model integrating the new information priority criterion to forecasting the future values in Chinese electricity demand during the period 2022 to 2025. We see that Chinese electricity demand would continue to rise in the next four years.