Revealing the Synergetic Development Evolution Mechanism of Economic Growth, Energy Consumption, and Environment: An Empirical Analysis Based on Haken Model and Panel Data

Abstract

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.

Interaction mechanism and spatial effect of cross‐regional haze pollution based on a multisectoral economy–energy–environment (3E) model and the evidence from China

“…Previous studies indicate that the spatial spillover effect consists mainly of three parts: (1) the economic spillover characterized by the transfer of investment and trade activities, (2) the emission spillover characterized by energy production and consumption activities, and (3) the natural spillover characterized by atmospheric diffusion and transport across boundaries (Liu, Qiao, et al, 2022; Liu et al, 2017). As a result, it not only confirms a significant spatial autocorrelation of the variables above, but also affirms the necessity and effectiveness of constructing the spatial regression model for investigation (Hong, 2022; Pace & LeSage, 2009; Tang et al, 2016).…”

“…Furthermore, the estimation coefficient $${\theta }_{2}$$ of $$W\times \mathrm{ln}eg$$ is significantly positive, and the coefficient $${\theta }_{3}$$ of $$W\times {{\rm{ln}}}^{2}eg$$ is significantly negative, denoting that economic growth causes significant spillover on haze pollution, and an inverted U‐shaped curve relationship exists between the economic growth and haze pollution among different regions. As demonstrated by the previous literature (Grossman & Krueger, 1991; Hong, 2022; Shao et al, 2016), the impact mechanism and potential path can be decomposed into three aspects: the scale effect formed by the increase in massive total haze pollutant emissions, caused by the expansion of production and consumption processes; the technical effect formed by energy conservation and emission abatement, caused by the path of the green economy; and the structural effect formed by the transformation and upgrading of the economic structure, leading to the reallocation of factor endowment resources. Thus, the way economic growth ultimately affects haze pollution depends on the relative strength of scale, technical and structural effects; the EKC theorem demonstrates that there is a dynamic changing nexus between economic growth and environmental degradation.…”

“…First, it indicates that the estimated coefficient of the direct effect of energy consumption (Liu, Qiao, et al, 2022;Liu et al, 2017). As a result, it not only confirms a significant spatial autocorrelation of the variables above, but also affirms the necessity and effectiveness of constructing the spatial regression model for investigation (Hong, 2022;Pace & LeSage, 2009;Tang et al, 2016).…”

“…The total energy consumption is the aggregate based on the calorific value conversion of all energy types (including coal, petroleum oils, natural gas, hydro energy, renewable energy, etc.) and these are converted into a unified dimension of standard coal (Hong, 2022). In addition, a set of control variables are introduced into the regression equation based on existing studies and our theoretical analysis to alleviate endogeneity and estimation errors.…”

The synergistic evolution of supply-demand composite system for airport green development: A case study in Guangzhou Baiyun International Airport, China

“…Measuring the DSS orderliness U X ( t ) as an example, the measurement of the SSS orderliness U Y ( t ) follows a similar process. The specific steps for both measurements are as follows [ 41 ]: Step1. Constructing the entropy evaluation matrix: X = ( X 1 , X 2 , …, X n ), X j = ( x 1 j , x 2 j , …, x mj ) T ∀ j ∈ [ 1 , n ].…”