Structural decomposition analysis of global carbon emissions: The contributions of domestic and international input changes.

Finding the essential factors driving carbon emissions is vital for the carbon reduction policy-making. Different from the existing research, this paper studied the separate influence of internal and external input structural changes on global carbon emissions. We applied structural decomposition analysis (SDA) to decompose the global carbon emission change into six factors: namely, the carbon emission intensity, the domestic input structure, the international input structure, consumption pattern, consumption volume and population. The results firstly showed that the contributions of different factors to global carbon emissions changed over time. In recent five years, structural changes of domestic inputs became the principle driver of decrease in global carbon emissions. Secondly, the results showed that there were significant differences for countries in their factors for carbon emissions. In India and Russia, domestic input structural change was the major contributor to the decrease in carbon emissions. In Japan and Germany, the most important factor for the increase in carbon emissions was the international input structure. Finally, the results showed the factors for carbon emission changes were correlated to economic development. The international input structural changes significantly increased carbon emissions in high-income countries. Our findings suggested that some countries such as India and Russia, improving the usage efficiency of domestic carbon-intensive products would help reducing carbon emissions. For most high-income countries such as Japan and Germany, they should reduce the dependence on the imported carbon-intensive products by turning the external input sources to countries with technology advantages. In addition, technology exportation of high-income countries would also be beneficial for the global carbon reduction.

Dynamic heteroscedasticity of time series interpreted as complex networks.

Heteroscedasticity of time series is an important issue addressed in relation to the nonlinearity and complexity of time series. Previous studies have focused on time series heteroscedasticity during a long-term period but have rarely analyzed it from a nonlinear dynamic perspective. This paper proposes a new model for converting a time series into a complex network. Our proposed model can examine not only the heteroscedasticity of a short-term series but also the dynamic evolution process of this heteroscedasticity. Using four typical crude oil time series as sample data, we construct four networks. A network node denotes the types of fluctuation patterns corresponding to the symbolization of the heteroscedastic features of a short-term fluctuation series based on the autoregressive generalized autoregressive conditional heteroscedasticity model, and a weighted edge represents the evolution direction and frequency between two patterns. Our findings show that the choice of the length of a short-term period depends on the diversity of these patterns. The identification of the nodes with greater out-strength or greater betweenness centrality can help us to understand the different roles of fluctuation patterns in the evolution process. We propose a method for predicting the most probable target nodes from a source node. The analysis of clustering effects can help in detecting the fluctuation patterns between different clusters. This paper investigates the evolution dynamic mechanism of the heteroscedastic features of a short-term time series, which can help researchers and investors deeply understand the dynamic process of time series.

Adjusting the intermediate input sources for global carbon emission reduction: An input-output optimization model.

The domestic industrial structure optimization for carbon emission reduction usually causes carbon transfer to other countries, while the global industrial structure adjustment would possibly lead to the unbalance of regional economy development. Based on the previous studies, this paper built an intermediate input source optimization model to reduce the global carbon emissions by adjusting the international input sources of countries (regions). The results showed that the proposed model could effectively realize the goals of global carbon emission reduction and economic development. After optimization, the requirements for the inputs from some sectors would significantly decrease, such as the materials in China, while the requirements for the inputs from some sectors would significantly increase, such as energy in the EU countries. The results also showed that the important sectors in the domestic industrial structures were more sensitive to the global intermediate input source adjustment. Furthermore, the global input source changes would indirectly improve the industrial structures of some countries including China, India and Russia. Our work suggested that countries should further promote the technology advantages of some important sectors to avoid the probable industrial risks bought by the global actions toward carbon neutrality.

Adjusting the global industrial structure for minimizing global carbon emissions: A network-based multi-objective optimization approach.

Although many scholars have focused on industrial structure adjustment to find the optimal balance between carbon emission reduction and economic growth, few studies have considered the comprehensive influence of the supply chain structure on carbon emissions. Based previous studies, we proposed a novel network-based optimization model. The results showed that carbon emissions would decrease by 4.31%, 6.26 and 8.07% with GDP increasing by 5.53%. 4.45% and 2.50% in different scenarios in the proposed network-based model, which performed better than the previous non-network-based model. There were some principal sectors which played special roles in the optimization of global industrial structure. To achieve the goal of global carbon emission reduction, some sectors should significantly reduce their total outputs such as materials and energy in China, while other sectors could continue to increase their total outputs such as machinery and services in China and South Korea. The results also showed that the change rate of carbon emissions was related with the costs of carbon emissions for the GDP growth. Countries with higher costs of carbon emissions, such as China, India and Russia, would burden more responsibilities. Furthermore, we found that the changes of the industrial structures of countries (regions) were different under global carbon emission reduction. Due to the current technology limitation, the production activities of energy and material industries in developing countries, such as China and India, should be reduced. Technology exportation of developed countries in such industries would be beneficial for the global carbon reduction.

Identifying key sectors based on cascading effect along paths in the embodied CO2 emission flow network in Beijing-Tianjin-Hebei region, China.

The emission of carbon dioxide (CO2) is a serious environmental issue, especially in Beijing-Tianjin-Hebei region. Unlike previous studies that mainly consider the bilateral and direct connection between two sectors, this study identifies path-based key sectors by considering the cascading effect of a sector on other sectors on paths of the entire economic system. We first construct an embodied CO2 emission flow network of Beijing-Tianjin-Hebei region, combining environmental input-output analysis and complex network theory. Then, the path-based key sectors are identified by traversing the path of each sector in the network based on cascading failure theory and hypothesis extraction method. On the one hand, the results show that a small number of sectors shoulder a large proportion of the embodied CO2 emission flows from both path and sector perspectives. On the other hand, we identify some path-based key sectors that did not receive enough attention from the sector perspective. Additionally, the sum of the embodied CO2 emission flows in about 30 steps accounts for 90% of the total embodied CO2 emission flows on its supply chain path. To more effectively reduce carbon emission, sectors that connect these 30 steps should be concerned in some policy recommendations. The method proposed in this paper can complement existing methods and contribute to further reducing CO2 emissions in the Beijing-Tianjin-Hebei region.

Analysis of the transmission characteristics of China's carbon market transaction price volatility from the perspective of a complex network.

Research on the price fluctuation transmission of the carbon trading pilot market is of great significance for the establishment of China's unified carbon market and its development in the future. In this paper, the carbon market transaction prices of Beijing, Shanghai, Tianjin, Shenzhen, and Guangdong were selected from December 29, 2013 to March 26, 2016, as sample data. Based on the view of the complex network theory, we construct a price fluctuation transmission network model of five pilot carbon markets in China, with the purposes of analyzing the topological features of this network, including point intensity, weighted clustering coefficient, betweenness centrality, and community structure, and elucidating the characteristics and transmission mechanism of price fluctuation in China's five pilot cities. The results of point intensity and weighted clustering coefficient show that the carbon prices in the five markets remained unchanged and transmitted smoothly in general, and price fragmentation is serious; however, at some point, the price fluctuates with mass phenomena. The result of betweenness centrality reflects that a small number of price fluctuations can control the whole market carbon price transmission and price fluctuation evolves in an alternate manner. The study provides direction for the scientific management of the carbon price. Policy makers should take a positive role in promoting market activity, preventing the risks that may arise from mass trade and scientifically forecasting the volatility of trading prices, which will provide experience for the establishment of a unified carbon market in China.

Reconstructing complex network for characterizing the time-varying causality evolution behavior of multivariate time series.

In order to explore the characteristics of the evolution behavior of the time-varying relationships between multivariate time series, this paper proposes an algorithm to transfer this evolution process to a complex network. We take the causality patterns as nodes and the succeeding sequence relations between patterns as edges. We used four time series as sample data. The results of the analysis reveal some statistical evidences that the causalities between time series is in a dynamic process. It implicates that stationary long-term causalities are not suitable for some special situations. Some short-term causalities that our model recognized can be referenced to the dynamic adjustment of the decisions. The results also show that weighted degree of the nodes obeys power law distribution. This implies that a few types of causality patterns play a major role in the process of the transition and that international crude oil market is statistically significantly not random. The clustering effect appears in the transition process and different clusters have different transition characteristics which provide probability information for predicting the evolution of the causality. The approach presents a potential to analyze multivariate time series and provides important information for investors and decision makers.

Information Connections among Multiple Investors: Evolutionary Local Patterns Revealed by Motifs.

The concept of motifs provides a fresh perspective for studying local patterns, which is useful for understanding the essence of a network structure. However, few previous studies have focused on the evolutionary characteristics of weighted motifs while further considering participants' differences. We study how information connections differ among multiple investors. The evolutionary 10-year trend of weighted 3-motifs in China's energy stock markets is explored for the networks of co-holding behaviors among shareholders, who are classified as companies, funds and individuals. Our works allow us to detect the preferential local patterns distributed among different agents as their fluctuate involvement in networks. We find that the diversity of shareholders contributes to the statistical significance of local patterns, while homophily always exist among individuals. Modules of information connections are stable among reserved investors, which is especially apparent among companies. Individuals prefer to keep their connections with companies and funds. Unsteady modules happen owing to strengthen links among funds during the time that they are main participants in stock markets. More details about multiple investors informationally connected in evolutionary local patterns can be detected by our work.