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.
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.
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.
In recent years, the Chinese Central Government has put great emphasis on the marine economy, since it has been a new driving force of the national economic development. Yet, the relationship between marine economic development and energy efficiency remains unknown. In this paper, we investigate whether marine economic development affected energy efficiency. We focused on the Zhoushan Archipelago, the first National New Area for marine economic development in China. We applied panel data approach to construct the counterfactual of Zhoushan Archipelago. We compared Zhoushan and its counterfactual, the synthetic Zhoushan, and viewed the difference between them, after the Zhoushan Archipelago New Area, as the impact of marine economic development on energy efficiency. We found that compared with its counterfactual, the real Zhoushan had a more substantial improvement in energy efficiency after the National New Area construction. We estimated that the construction of the National New Area for Marine Economy increased energy efficiency by about 10 percentage points. We also applied the bootstrapping technique to illustrate the significance of the estimated results. The results suggest that the Zhoushan Archipelago New Area construction had a statistically significant impact on energy efficiency. In addition, we conducted two tests, including leave-one-out tests and permutation tests, to check the robustness of estimated results, which also serve as an illustration of significance of the estimated results from an alternative empirical perspective. The results from these two tests are similar to each other, both indicating that the Zhoushan Archipelago New Area construction had a statistically significant positive impact on energy efficiency. Overall, our results suggest that marine economic development had a sizable improvement in energy efficiency.
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