The existing research on the business environment index system has a single viewpoint, which can hardly reflect the requirements of SDGs on the business environment. Few studies have focused on the “front end” factors determining the business environment. Based on panel data from 2011 to 2020, this study measures the business environment in 275 Chinese cities and uses the ArcGIS software and Natural Breaks (Jenks) to examine its spatiotemporal characteristics. The PSM‐DID model is utilized to examine the policy implications of the reform of government functions on the business environment. The findings demonstrate that China's urban business environment's total score has risen, and its geographic characteristics are “strong in the east and weak in the west.” Among the various sub‐dimensions of the business environment, the largest contribution is the infrastructure environment, followed by the rule of law environment. The reform of government functions can improve the business environment from the second year onward. Regional and city‐level heterogeneities exist in the effects of the reform of government functions on the business environment. In addition, informatization level, population density, and consumption level can optimize the business environment. This study provides a guide for accelerating the creation of a marketized, legalized, international, and sustainable business environment.

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At present, the destruction of the marine ecological environment and the imbalance of economic structure have put forward urgent requirements for the green development of the marine economy. Based on the input and output data of China’s coastal provinces from 2006 to 2018, the RDM (range directional model) direction distance function was used to measure the output bias technology progress (OBTC) index of each region, and its influence on China’s marine economy green total factor productivity (GTFP) was judged accordingly. Furthermore, the rationality of the current OBTC index was studied. The results show that there is obvious output-biased technological progress in China’s marine economy, and it has led to the improvement of the GTFP. Although most coastal areas still tend to pursue the improvement of the total output value of the marine economy at the expense of environmental damage, the green bias of China’s marine economy has improved significantly since 2015, driven by relevant marine environmental protection policies. From the perspective of different areas, the imbalance of regional development in the process of China’s marine economic development is significant. The green bias of the marine economy is highest in the East China Sea area and lowest in the Bohai rim area. However, the coordination between the development of the green marine economy and environmental protection in the South China Sea area needs to be improved.

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As a water-scarce country and the world’s largest trader of industrial products, China’s industrial virtual water (VW) flow may exacerbate its water scarcity problem. Thus, industrial VW flows’ spatial and temporal evolution in international trade should be analyzed, and influencing factors must be identified. This study developed the multiregional input–output (MRIO) model, combined with the Leontief inverse matrix, to measure and decompose the industrial VW flows between China and other economies from 2000 to 2014. This extended MRIO model considers intermediate production water consumption and indirect water use, which technically distinguishes the sources of pressure on water use more accurately, thus enabling a new elaboration of the composition of China’s industrial water use. Then, the evolution of China’s industrial VW trade is analyzed spatiotemporally, and the structural decomposition analysis (SDA) method is invoked to identify the endogenous drivers further. The results indicate the following. (1) China was a net exporter of industrial VW trade. The main VW export sectors in China were the manufacture of textiles and wearing apparel, paper products, and chemical products, which had the characteristics of high water consumption, high pollution, and low added value, respectively. (2) The net exports of industrial VW from China mainly went to the US, EU, ROW (rest of the world), and Japan. China’s VW exports to the US and Japan are declining, while exports to the EU and Russia are increasing. (3) The decrease in the water-use coefficient in all industrial sectors in China was the most critical reason for inhibiting the increase in the country’s industrial VW exports. The export structure effect of intermediate products, product structure effect of foreign final demand, and scale effect of foreign final demand were the primary reasons for the rise in VW exports, but all gradually diminished. Moreover, the structural effects of China on the use of domestic intermediate products had a significant positive effect on the increase in VW exports. In contrast, those of foreign products had an extremely weak effect.