Revealing the hidden carbon flows in global industrial Sectors-Based on the perspective of linkage network structure.

This paper interprets the implicit carbon flows in global industrial sectors from a network perspective. Using the SNA-IO integrated model, along with cross-border input-output data from Eora26 (2000-2020) and global energy balance data, the implicit carbon emissions of global industrial sectors and their evolution are analyzed. A carbon emission network structure from an industrial chain perspective is proposed. The results indicate that the carbon emissions responsibility of an industry is not only associated with its own energy consumption. It also involves the carbon emissions transfer resulting from the exchange of products and services between upstream and downstream industries. Block model analysis reveals the carbon emission transfer relationships and their interconnections among global industrial sectors, tending towards an industry clustering pattern where "production side" converges with "demand side" coexisting in supply and demand. There are noticeable inequalities in wealth gains and environmental burdens between these blocks. This paper can provide targeted carbon reduction policy recommendations for various industrial sectors to participate in global responsibility allocation and promote the formation of a low-carbon global industrial sector network.

How the new energy industry contributes to carbon reduction? -Evidence from China.

New energy is an inevitable choice to cope with global climate change. China has invested heavily in new energy, but it still faces enormous pressure to reduce emissions. The effectiveness and path of new energy industry development still need to be solved. This paper studies the relationship between the development of new energy industry and carbon emissions. A theoretical model of new energy firms' production behaviour was constructed, reflecting that the internal carbon emissions of the new energy industry mainly depend on its cost structure and R&D intensity. Specifically, part of the carbon emission caused by scale effect comes from direct capacity construction, and the other part comes from the production-cost effect of R&D. Based on the provincial panel data in China from 2005 to 2019, empirical tests are carried out from two aspects of scale effect and technology effect. Results show that the scale expansion has an inverted U-shaped relationship with carbon emissions, which is supported by the regression with GDP as the threshold variable. The effect of new energy technologies in reducing emissions is continuous. The threshold for technology to play a role in reducing emissions is smaller than the threshold for scale. The findings explain the expansion of the new energy industry in the early stages may lead to an increase in carbon emissions. Our study provides important insights that the scale and technology are two dimensions that cannot be ignored in the process of energy transformation. It is necessary to act in the reasonable range and pay attention to the accumulation of technology innovation and the orderly expansion of production capacity.

How to advance China's carbon emission peak?- A comparative analysis of energy transition in China and the USA.

This paper investigates carbon emission peak in China based on a comparative analysis of energy transition in China and the United States (US). The LMDI model is adopted to decompose carbon emissions into several driving factors in 2000-2018 for China and the US. Gray forecasting and NAR neural network are combined to predict peak time and identify optimal transition paths. The factor decomposition indicates that energy intensity is the main inhibitory factor for increased carbon emissions, while economic growth and population size are contributors for increased carbon emissions. There are significant differences in the impact of structure effect on carbon emissions in the two countries. The industry decomposition indicates that industry development is a critical inhibitor for increased carbon emissions after 2014 in China. The growth of transport and agriculture are basically contributing to increase carbon emissions in China and the US. The forecast results illustrate that China could complete carbon emission peak by 2030 under the baseline scenario, with a peak volume of 11354.72Mt CO2. Under the industrial structure adjustment scenario, the carbon peak year may be advanced to 2028. While adjusting industrial structure and energy consumption structure at the same time, China could achieve carbon emission peak at 9918.21Mt CO2 in 2025.

Green development of Yangtze River Delta in China under Population-Resources-Environment-Development-Satisfaction perspective.

This paper explores green development of Yangtze River Delta (YRD) under PREDS (Population-Resources-Environment-Development-Satisfaction) perspective. Based on gray relevance analysis, synergy evaluation model and factor analysis model are constructed with improvement of weight determination and relevance degree calculation. Synergy evaluation results show that for the entire YRD the relevance degree of green development increases strictly. As the synergistic effect of the inner system continues to rise, the green development tend toward equilibrium (2003-2017). The provincial level green development ranking is put forward. The results of factor analysis show that four dimensions' impacts on public satisfaction are different. The prediction of "13th Five-Year Plan" suggests that, improving environmental indicators is the most potential solution to promote green development of YRD. Investment completed in industrial pollution treatment is taken as an example to show how the index will affect satisfaction under different growth rates. It turns out that when the growth rate is below or over the critical value (16%), the influence will go out of the trough and continue to increase, forming a "J" curve.

Investigating carbon tax pilot in YRD urban agglomerations-Analysis of a novel ESER system with carbon tax constraints and its application.

This paper attempts to explore carbon tax pilot in Yangtze River Delta (YRD) urban agglomerations based on a novel energy-saving and emission-reduction (ESER) system with carbon tax constraints, which has not yet been discussed in present literature. A novel carbon tax attractor is achieved through the discussion of the dynamic behavior of the new system. Based on the genetic algorithm-back propagation neural network, the quantitative coefficients of the actual system are identified. The scenario analysis results show that, under the same tax rate and constraint conditions, the ESER system in YRD urban agglomerations is superior to the average case in China, in which the impacts on economic growth are almost the same. The former's energy intensity is lower and the shock resistance is stronger. It is found that economic property of YRD urban agglomerations is the main cause for the ESER system of YRD urban agglomerations being superior. In the current YRD urban agglomerations' ESER system, energy intensity cannot be adjusted to an ideal level by commercialization management and government control; however, it is under effective control of carbon tax incentives. Therefore, strengthening the economic property of YRD urban agglomerations and effective utilization of carbon tax incentives could perfectly control energy intensity, without obvious potential negative impact on economic growth.

Spatiotemporal Dynamics and Fitness Analysis of Global Oil Market: Based on Complex Network.

We study the overall topological structure properties of global oil trade network, such as degree, strength, cumulative distribution, information entropy and weight clustering. The structural evolution of the network is investigated as well. We find the global oil import and export networks do not show typical scale-free distribution, but display disassortative property. Furthermore, based on the monthly data of oil import values during 2005.01-2014.12, by applying random matrix theory, we investigate the complex spatiotemporal dynamic from the country level and fitness evolution of the global oil market from a demand-side analysis. Abundant information about global oil market can be obtained from deviating eigenvalues. The result shows that the oil market has experienced five different periods, which is consistent with the evolution of country clusters. Moreover, we find the changing trend of fitness function agrees with that of gross domestic product (GDP), and suggest that the fitness evolution of oil market can be predicted by forecasting GDP values. To conclude, some suggestions are provided according to the results.