The purpose of this paper is to reveal the mechanism and process of the dynamic evolution of the economy-energy-environment (EEE) system and study the variables that determine the speed of system evolution and development. The innovation is to overcome the defect of single index by constructing a system evaluation index and reveal the regional EEE system’s evolution mechanism based on Haken’s synergetic theory to supplement a systematic perspective’s empirical evidence. It selects 30 provinces, municipalities, and autonomous regions in China to empirically analyse the evolution mechanism of the EEE system from 2005 to 2019. The empirical results show that economic development is the order parameter of the evolution and development of the EEE system. In the process of system evolution, the economic development subsystem is the decisive factor in EEE system evolution. In terms of the positive feedback mechanism of order parameter combination, the EEE system forms a positive feedback mechanism for increasing economic development in the stage of evolution and development. The negative feedback mechanism of decreasing environmental pollution emissions is formed in the EEE system. The increase in pollutant emissions has a certain inhibitory effect on economic growth. The improvement in the economic development level has no promoting effect on pollutants, and economic development, in turn, reduces environmental pollution.

In the context of the continuous promotion of China’s big data development strategy, this paper quantitively analyses China’s existing national-level big data policies from the perspective of policy instruments and coword networks, discusses the rationality of existing policies, explores ways to improve policies, and provides a reference for the innovation of China’s big data policies. This paper carries out a quantitative textual analysis of China’s national big data policy from the perspective of policy instruments using word frequency analysis to obtain a keyword coword matrix and visualization analysis tools to obtain a coword network. This paper further studies the network node characteristics and structure using social network analysis methods, including degree centrality, clustering analysis, and multidimensional scale analysis, to identify the policy structure and characteristics. Improving big data policy requires improvements in policy instruments on the supply side, resolving existing policy gaps, and strengthening coordination with other policies.

The transboundary characteristics and multisectoral factor interaction mechanism of haze pollution have aroused widespread attention but remain understudied. This article proposes a comprehensive conceptual model that clarifies regional haze pollution, further establishes a theoretical framework on a cross‐regional, multisectoral economy–energy–environment (3E) system, and attempts to empirically investigate the spatial effect and interaction mechanism employing a spatial‐econometrics model based on China's province‐level regions. The results demonstrate that (1) regional haze pollution is a transboundary atmospheric state formed by the accumulation and agglomeration of various emission pollutants; moreover, there is a “snowball” effect and a spatial spillover effect. (2) The formation and evolution of haze pollution are driven by the multisectoral factors of 3E system interaction, and the findings still hold after theoretical and empirical analysis and robustness tests. (3) Significant spatial autocorrelation exists for the 3E factors, presenting different clustering modes with a dynamic spatiotemporal evolution, particularly in the high‐high (H‐H) mode and low‐low (L‐L) mode. (4) Significant heterogeneous impacts of economic and energy factors on haze pollution are identified, namely, an inverted “U‐shaped” relationship and a positive linear association, respectively. Further spatial analysis demonstrates a strong spatial spillover and obvious path dependence among local and neighboring regions. Policymakers are advised to consider multisectoral 3E system interaction and cross‐regional collaboration. Integr Environ Assess Manag 2023;00:1–19. © 2023 SETAC