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Global warming has endangered the natural ecosystem's balance, as well as human existence and development, and it is mostly caused by carbon dioxide. Identifying carbon emission characteristics and predicting carbon emission reasonably is helpful to provide indication for the effective design of emission reduction path. The most literature use a single prediction model; this paper predicts carbon emission using a number of strategies based on previous research. Considering the prediction accuracy, advantages, and disadvantages of each method, a new method combining autoregressive integrated moving average (ARIMA) model and NAR neural network (NAR-NN) is proposed; in addition, this paper attempts to explain the carbon emission characteristics and emission reduction paths of each region from the new perspective of clustering. First, the results show that China's carbon emission features can be divided into four categories: low-carbon demonstrative type, low-carbon potential type, high-carbon developed type, and high-carbon traditional type. Moreover, low-carbon demonstrative type includes merely Beijing and Shanghai, low-carbon potential type is distributed in the southeast coastal areas of China, the high-carbon developed type is mainly distributed in Northeast China, and the western region basically belongs to high-carbon traditional type. Second, ARIMA model and NAR-NN are the two best methods in terms of prediction effect, and the combined model has better prediction effect than the single model. Third, carbon emissions in most regions of China will increase in the next few years; the time of carbon peak in the east is earlier than that in the west regions of China. Beijing will probably be the first region in China to complete the carbon peak. Besides, there is a certain correlation between the carbon peak time and the type of carbon emission in each region. [ABSTRACT FROM AUTHOR]

Under the background of rapid global economic transformation, the influence of economic policy uncertainty (EPU) has penetrated all fields of production and life. It is significant to study how it affects the regional carbon emission intensity for regional sustainable development. Based on the data from 30 provinces from 2004 to 2017, this paper takes a complete account of spatial heterogeneity and the dynamic impact of economic policy uncertainty on carbon emission intensity using a spatial econometric model. The main conclusions are as follows: (1) For more than ten years, there have been significant differences and instabilities in economic policy uncertainty and carbon emission intensity in different regions of China. (2) China's local carbon emission intensity shows an objective spatial aggregation effect, which is significant, spatially auto-correlated and clustered. (3) Based on the national level, economic policy uncertainty will significantly increase the regional carbon emission intensity. Additionally, economic policy uncertainty has a significant positive spatial spillover effect, which may increase carbon emission intensity in neighboring provinces. (4) Based on the provincial level, the impact of economic policy uncertainty on carbon emission intensity in various regions is significantly positive, with the most significant impact on the western region. Based on the above conclusions, the paper proposes policy suggestions to stabilize the regional carbon emission intensity in all directions. [ABSTRACT FROM AUTHOR]

Carbon peaking and carbon neutrality will be important policies to constrain China's economic and social development in the future. Based on the theory of computable general equilibrium, CGE model of carbon emission was constructed in this paper to simulate the influence of different scenarios of carbon emission reduction on economic and social development. The major contribution is to subdivide the energy sector according to primary energy and secondary energy, and construct the macro SAM table and micro SAM table of 21 energy sectors in 2017 to quantitatively simulate the impact of carbon emission reduction policies on China's economy and society, and put forward some policy suggestions based on this. [ABSTRACT FROM AUTHOR]

To achieve carbon peak targets, realize carbon neutrality vision, and tackle global climate change, China must improve the carbon emission performance at the city level. Based on the carbon emission performance of 191 prefecture-level cities in China from 1997 to 2017, this paper analyses the evolution characteristics of urban carbon emission performance from three aspects: the overall spatial and temporal evolution, the differences according to both region and city size, and the differences among clusters categorized by carbon emission performance at the city level. This paper also reveals the impact of the social and economic transition on China's carbon emission performance. The results show that: (1) The overall level of carbon emission performance of Chinese cities is low, and there is a downward trend during the study period. The differences in carbon emission performance among cities are convergent, but there is a wide gap between high and low values. (2) The carbon emission performance of cities in eastern coastal areas is higher than that in non-coastal areas cities. Large urban agglomerations and economically developed regions, such as provincial capitals, are the agglomeration areas of high urban carbon emission performance values. (3) The carbon emission performance level of cities with similar sizes will converge. At the same time, such changes will enhance the differences among carbon emission performances at the city level within the same region. (4) Cities that belong to high urban carbon emission performance clusters are mainly distributed in the eastern region. Such cities are classified into large cities, supercities, and megacities. Compared with low urban carbon emission performance clusters, cities in high urban carbon emission performance clusters show a higher proportion in the medium-high level and high level of carbon emission performance. Moreover, cities in high urban carbon emission performance clusters are more likely to improve theirurban carbon emission performance. (5) The economic agglomeration effect, industrial structure adjustment and carbon intensity reduction have a significant impact on improving urban carbon emission performance. Population agglomeration has an incremental effect, and the anticipated benefits of environmental regulation have yet to be fully realized. The impacts of different clusters and different regions are variable. Finally, this paper advances policy enlightenment according to its research findings. [ABSTRACT FROM AUTHOR]

Based on the analysis framework of the general equilibrium model of trade environment effect constructed by Copeland and Taylor, this paper explained the mechanism of the effects of coordinated development of two-way FDI on carbon emissions, and then based on the data of two-way FDI and carbon emissions in 30 provinces in China from 2003 to 2020, the paper measured the carbon emission reduction effect of the coordinated development of two-way FDI in China by using the spatial panel model and moment estimation method generated by Han and Phillips and introduced the intermediary effect model to explore its impact mechanism. Moreover, the spatial spillover effect of the spatial model was investigated by applying partial differentiation. The study showed that: ① The coordinated development of China' s two-way FDI could significantly inhibit the improvement of the country' s carbon emission level. The spatial distribution and time path dependence of high and low carbon emission clusters were significant, and the results were still valid after robustness and endogenous tests. ② Heterogeneity analysis showed that coordinated development of two-way FDI in the eastern region had the most obvious carbon emission reduction effect, followed by the central region, while the regression results of the impact of the western region on carbon emissions were not significant. That was mainly related to the highest level of coordinated development of two-way FDI in the eastern region, followed by the central region, while the western region had been in a state of extreme imbalance. ③ The test result of the impact mechanism showed that coordinated development of two-way FDI, on the one hand, reduced carbon emissions by improving the capital-labor ratio, and on the other hand, had a positive impact on carbon emissions by promoting the expansion of industrial scale and the improvement of productive technology. However, under the combined effect of structural effect, scale effect, and technological effect, it ultimately had a significant inhibitory effect on carbon emissions. ④ The spatial spillover effect analysis found that the improvement in coordinated development of two-way FDI in China' s provinces inhibited not only the carbon emissions of their own regions, but also those of neighboring regions and even the whole country. In view of the above findings, we believe that we should do a good job of coordinating the development of two-way FDI, establish a long-term mechanism of coordinated regional economic development and joint pollution prevention and control, further tap the emission reduction effect of structural effects, and encourage and guide technological progress to focus on pollution control. [ABSTRACT FROM AUTHOR]

The continuous increase of carbon emissions is a serious challenge all over the world, and many countries are striving to solve this problem. Since 2020, a widespread lockdown in the country to prevent the spread of COVID-19 escalated, severely restricting the movement of people and unnecessary economic activities, which unexpectedly reduced carbon emissions. This paper aims to analyze the carbon emissions data of 30 provinces in the 2020 and provide references for reducing emissions with epidemic lockdown measures. Based on the method of time series visualization, we transform the time series data into complex networks to find out the hidden information in these data. We found that the lockdown would bring about a short-term decrease in carbon emissions, and most provinces have a short time point of impact, which is closely related to the level of economic development and industrial structure. The current results provide some insights into the evolution of carbon emissions under COVID-19 blockade measures and valuable insights into energy conservation and response to the energy crisis in the post-epidemic era. [ABSTRACT FROM AUTHOR]

As urbanization has developed, a variety of urban clusters have gradually emerged with important effects on regional economic growth and carbon emissions. This paper constructs a multi-period difference-in-difference (DID) model for testing how urban cluster synergistic development policy (UCSDP) affects urban carbon productivity in 148 cities of China with 13 urban clusters as the experimental group from 2003 to 2019. According to the results, the UCSDP improves carbon productivity, mainly via reducing energy intensity. In addition, UCSDP improves carbon productivity by adjusting energy structure, increasing green innovation, altering city greening levels, and accelerating the process of urbanization. Moreover, it can produce synergistic effects in terms of energy structure, green innovation, greening level, and urbanization level. Furthermore, the UCSDP has heterogeneous effects on the carbon productivity in various cities. Specifically, the carbon productivity of high-status, high-rank, and resource-based cities responds more significantly to UCSDP. Therefore, policymakers should strengthen the integration of urban clusters, offer targeted development suggestions for different cities, modify the energy and greening structure, accelerate the process of urbanization, and focus on the formulation of reasonable environmental regulation policies. • Exploring the impact of UCSDP on carbon productivity with multi-period DID model. • The improvement of carbon productivity by UCSDP mainly comes from the decrease of energy intensity. • UCSDP increases carbon productivity by affecting energy structure, green innovation, greening level and urbanization level. • UCSDP produces synergistic effects in terms of energy structure, green innovation, greening level and urbanization level. • The impact of UCSDP on carbon productivity varies by city status, city rank and city type. [ABSTRACT FROM AUTHOR]