Applying an entropy-weighted TOPSIS method to evaluate energy green consumption revolution progressing of China.

The energy green consumption revolution (EGCR) is the highest priority in the Chinese government's energy revolution agenda. The purpose of this study is to provide a comprehensive and objective evaluation of the China's EGCR progressing from 2011 to 2019. In this study, an integrated economic-social-energy-environmental EGCR evaluation framework is built, and the entropy-weighted TOPSIS method with four customized equations is used to calculate and analyze the EGCR index. The study finds that the EGCR index at the national level fluctuates between 0.290 and 0.302, showing a stagnant and regressive trend. At the regional and provincial levels, the EGCR index for eastern China remains at high level, floating above 0.4 and no further growing trend has been indicated. As for the eastern China, Beijing is the only city has high-level EGCR index and is able to maintain positive growth trend. The EGCR index in central, western, and northeastern China is at a low level, fluctuating below 0.4. This result is mainly caused by the fact that the majority of these regions are still constrained by the fossil fuel-dominated social, economy, energy, and environment structures. Therefore, the research findings not only provide supportive evidence for the Chinese government to recognize the progressing of EGCR, but also offer statistical basis over formulating and updating EGCR policies at a timely manner.

Technological roadmap towards optimal decarbonization development of China's iron and steel industry.

China's iron and steel (IS) industry contributes approximately 16 % of the nation's total CO2 emissions. This study evaluates the environmental impact of each step in the production process based on the life cycle assessment method. It then explores potential deep decarbonisation pathways, developing an integrated dynamic model to meet the carbon neutrality target. The results reveal three primary findings. (1) In 2020, the blast furnace-basic oxygen furnace contributed significantly to the global warming potential -1.77 E-8 kg CO2 equivalents per year (eq/yr) higher than the electric arc furnace-and the blast furnace process makes the largest contribution in ironmaking (8.9E-9 kg CO2 eq/yr). (2) Converter negative energy steelmaking technology has the highest energy savings at 39.07 million tons of coal equivalent (Mtce) and an emissions-reduction potential of 72.01 Mt. Its mitigation cost is 69 CNY/t CO2, followed by thick-layer sintering (30.21 Mtce, 61.21 Mt. and 70 CNY/t CO2) and the application of dry vacuum system for molten steel degassing circulation (26.17 Mtce, 56.03 Mt. and 102 CNY/t CO2). (3) Technological improvement could significantly impact the IS industry, reducing CO2 emissions through production structure improvement, technological development and ultra-low emissions technology, from 789 Mt. in a business-as-usual scenario to 516 Mt., 261 Mt. and 157 Mt. in 2060, respectively.

Virtual Water Flow Pattern in the Yellow River Basin, China: An Analysis Based on a Multiregional Input-Output Model.

Research on the Yellow River Basin's virtual water is not only beneficial for rational water resource regulation and allocation, but it is also a crucial means of relieving the pressures of a shortage of water resources. The water stress index and pull coefficient have been introduced to calculate the implied virtual water from intraregional and interregional trade in the Yellow River Basin on the basis of a multi-regional input-output model; a systematic study of virtual water flow has been conducted. The analysis illustrated that: (1) Agriculture is the leading sector in terms of virtual water input and output among all provinces in the Yellow River Basin, which explains the high usage. Therefore, it is important to note that the agricultural sector needs to improve its water efficiency. In addition to agriculture, virtual water is mainly exported through supply companies in the upper reaches; the middle reaches mainly output services and the transportation industry, and the lower reaches mainly output to the manufacturing industry. Significant differences exist in the pull coefficients of the same sectors in different provinces (regions). The average pull coefficients of the manufacturing, mining, and construction industries are large, so it is necessary to formulate stricter water use policies. (2) The whole basin is in a state of virtual net water input, that is, throughout the region. The Henan, Shandong, Shanxi, Shaanxi, and Qinghai Provinces, which are relatively short of water, import virtual water to relieve local water pressures. However, in the Gansu Province and the Ningxia Autonomous Region, where water resources are not abundant, continuous virtual water output will exacerbate the local resource shortage. (3) The Yellow River Basin's virtual water resources have obvious geographical distribution characteristics. The cross-provincial trade volume in the downstream area is high; the virtual water trade volume in the upstream area is low, as it is in the midstream and downstream areas; the trade relationship is insufficient. The Henan and Shandong Provinces are located in the dominant flow direction of Yellow River Basin's virtual water, while Gansu and Inner Mongolia are at the major water sources. Trade exchanges between the midstream and downstream and the upstream should be strengthened. Therefore, the utilization of water resources should be planned nationwide to reduce water pressures, and policymakers should improve the performance of agricultural water use within the Yellow River Basin and change the main trade industries according to the resource advantages and water resources situation of each of them.

Driving forces of temporal-spatial differences in CO2 emissions at the city level for China's transport sector.

The paper aims to investigate the influencing factors that drive the temporal and spatial differences of CO2 emissions for the transportation sector in China. For this purpose, this study adopts a Logistic Mean Division Index (LMDI) model to explore the driving forces of the changes for the transport sector's CO2 emissions from a temporal perspective during 2000-2017 and identifies the key factors of differences in the transport sector's CO2 emissions of China's 15 cities in four key years (i.e., 2000, 2005, 2010, and 2017) using a multi-regional spatial decomposition model (M-R). Based on the empirical results, it was found that the main forces for affecting CO2 emissions of the transport sector are not the same as those from temporal and spatial perspectives. Temporal decomposition results show that the income effect is the dominant factor inducing the increase of CO2 emissions in the transport sector, while the transportation intensity effect is the main factor for curbing the CO2 emissions. Spatial decomposition results demonstrate that income effect, energy intensity effect, transportation intensity effect, and transportation structure effect are important factors which result in enlarging the differences in city-level CO2 emissions. In addition, the less-developed cities and lower energy efficiency cities have greater potential to reduce CO2 emissions of the transport sector. Understanding the feature of CO2 emissions and the influencing factors of cities is critical for formulating city-level mitigation strategies of the transport sector in China. Overall, it is expected that the level of economic development is the main factor leading to the differences in CO2 emissions from a spatial-temporal perspective.