A large number of studies have been conducted to examine China’s CO2 emissions problem disaggregated to the city level. However, few studies have delved further into the black box of economic production to examine the characteristics of CO2 emissions at the city supply chain level. In the context of the reality that Beijing takes the lead in achieving CO2 emissions reduction, this study decomposes CO2 emissions change in Beijing at three levels: overall, supply stage, and supply chain, using structural decomposition analysis (SDA) and structural path decomposition (SPD), filling the gap in urban CO2 emissions studies. The results show that: (i) energy consumption intensity is the most significant driver of emissions reduction, while per capita final demand is the largest factor in increasing emissions; (ii) Beijing’s emissions reduction contribution is mainly reflected in the first supply stage (76.50%) and the second supply stage (18.85%); (iii) the expansion of domestic exports and thus greater demand for transportation is significant in emissions increase supply chains; (iv) the improvement of the demand structure for electricity from domestic exports contributes a large part in emissions reduction supply chains; (v) the existence of many offsetting effects, such as the ebb and flow of domestic exports on the demand for different products, has led to the loss of emissions reduction. Finally, corresponding policy recommendations are presented from the energy, industry, and demand perspectives. Our study will provide assistance in developing more microscopic policies to reduce emissions and replicating the Beijing experience.

China, the world's largest developing country, faces a severe water shortage. As the government has set a goal of limiting water use to 7000 × 108 m3 by 2035, how to control the increase in water use will be a thorny issue for China. Unbalanced and uncoordinated regional socio-economic development is an important feature of China. Research on the interaction between provincial water use will help to optimize the rational allocation of water resources and control of water use. In this paper, SNA (social network analysis) method is first used to explore the characteristics of social network relationship between inter-provincial water use, construct a two-stage model of SNA–LMDI, and decompose the driving factors of inter-provincial water use evolution. We found the following points. (1) From 2000 to 2018, the spatial correlation network structure of water use is tending to be stable, and the stability and risk resistance of the whole network are enhanced. (2) From different angles to quantify the centricity analysis, can be found that eastern provinces located right in the heart of water network, obviously larger impact on water resources utilization in other provinces, Shanghai and Beijing is located in the former two, and central and western provinces in the edge position. (3) The national water use spatial correlation network can be divided into four blocks, net beneficial block, bidirectional spillover block, brokers block, and net spillover block. (4) Technological progress and industrial structure adjustment were the primary and secondary factors inhibiting the increase of total water use, while income increase was the main factor promoting the increase of total water use, population scale expansion had a weak role in promoting the increase of total water use. Some policy implications are put forward related to our research conclusions.