Under the dual pressure of energy transformation and environmental protection, how to use the innovative network and enhance technical innovation (TI) are significant problems for new energy vehicle (NEV) enterprises in China. Based on the patent data of China’s NEV industry from 2001 to 2022, combined with the logical framework of “Patent Analysis—Network Evolution—Empirical research”, this study deeply discusses the influence of enterprises’ innovative network on TI. It is shown that there is an inverse U-relationship between the cooperation breadth of the enterprises, and that the appropriate cooperation breadth favors TI. At the same time, structural holes, knowledge diversity and technical value have a significant positive impact on TI. In addition, the study performs a series of robustness tests and heterogeneity checks. The conclusions are conducive to further improving the study of the relationship between innovative networks and TI, which is important both theoretically and practically for promoting the energy transformation of automotive enterprises.

Energy transition plays a crucial role in supporting sustainable economic growth and the reduction in carbon emissions. In fact, China implemented the national sustainable development experimental zone policy to achieve sustainable development goals, including an energy transition. This paper divided the energy transition dimension into energy consumption and carbon dioxide emissions based on the perspectives of energy input and output. Furthermore, using panel data for 214 cities at the prefecture level in China from 2006 to 2019, the study measured the impact of the national sustainable development experimental zone policy on energy transitions by employing a difference-in-difference (DID) model and an intermediary effect model. The results showed that the national sustainable development experimental zone policy reduced energy consumption and carbon dioxide emissions and accelerated energy transition. The conclusions still held after a series of robustness tests. Additionally, the results of the heterogeneity analysis of different experimental zone types indicated that, compared with prefecture-level experimental zones, county experimental zones play a more obvious role in reducing energy consumption and carbon dioxide emissions. In addition, the results of the heterogeneity analysis of the urban geographical location showed that the national sustainable development experimental zone policy had different negative effects on urban energy consumption and carbon dioxide emissions in different regions, and the impact of policy on energy transition was experienced, in decreasing order, by the western, central, and eastern regions. The results of the mechanism verification indicated that the national sustainable development experimental zone policy can affect energy consumption and carbon dioxide emissions via technological progress and upgrading industrial structure, which had a relatively high aggregation order in the variables deployed.

Gobeil’s model is one of the most widely used models to identify lead (Pb) pollution sources in the environment. It is based on a set of equations involving Pb isotope fractions. Although a well-established numerical method, Gobeil’s model is often unable to provide an accurate estimation of each pollution sources’ contribution. This paper comprehensively examines the drawbacks of Gobeil’s model based on a numerical analysis and proposes a revised numerical method that provides a more accurate estimation of Pb pollution sources. Briefly, the mathematical inaccuracy of Gobeil’s model mainly lies in the misinterpretation of “lead fingerprint ratio balance.” To address this problem, the new analytic model relies on the mass balance of total lead in the contaminated sites, and uses a set of linear equations to obtain the contribution of each pollution source based on the lead fingerprint. A subsequent case study from an industrial park in Guanzhong area of Shaanxi Province in China shows that we can calculate the lead contribution rates accurately with the new model.