Revisiting the Dynamic Response of Chinese Price Level to Crude Oil Price Shocks Based on a Network Analysis Method.

Crude oil price shocks have led to a fluctuation in commodity prices through the industrial chain and supply-demand relationships, which can substantially influence a country's economy. In this paper, we propose a transmission model of oil price shocks to Chinese price levels and explore the direct and indirect impacts of crude oil price shocks on various Chinese price indices, combining the Granger causality test, impulse response function, and network analysis method. The empirical data are the Brent, WTI, Dubai, and Daqing spot crude oil prices and eight categories of Chinese price indices from January 2011 to March 2020. We found the following results: (1) Consumer price index (CPI) and the price index for means of agricultural production (MAPPI) cannot be directly impacted by crude oil price fluctuations, while they could be indirectly affected. (2) The duration and degree of the impacts of oil prices on each price index vary, and the export price index (EPI) is the most significantly affected. (3) The proportion of the indirect impact in the total impact of crude oil price shocks ranges from 0.03% to 100.00%. Thus, indirect influence cannot be ignored when analyzing the influence of crude oil price fluctuation on Chinese price level.

Global food-miles account for nearly 20% of total food-systems emissions.

Food trade plays a key role in achieving global food security. With a growing consumer demand for diverse food products, transportation has emerged as a key link in food supply chains. We estimate the carbon footprint of food-miles by using a global multi-region accounting framework. We calculate food-miles based on the countries and sectors of origin and the destination countries, and distinguish the relevant international and domestic transport distances and commodity masses. When the entire upstream food supply chain is considered, global food-miles correspond to about 3.0 GtCO2e (3.5-7.5 times higher than previously estimated), indicating that transport accounts for about 19% of total food-system emissions (stemming from transport, production and land-use change). Global freight transport associated with vegetable and fruit consumption contributes 36% of food-miles emissions-almost twice the amount of greenhouse gases released during their production. To mitigate the environmental impact of food, a shift towards plant-based foods must be coupled with more locally produced items, mainly in affluent countries.

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.

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.