Relationship between Industrialization, Urbanization and Industrial Ecology in Western China: A Panel Vector Auto-Regression Model Analysis

doi:10.5814/j.issn.1674-764x.2021.01.007

Abstract

Abstract:

As the foundation of modern economic development, industry is the engine of industrialized and urbanized development. Industrial ecology is a high-level form of industry that is achieved after it has reached a certain stage, which guides the coordinated industrial development balancing mankind and nature. The implementation of industrial ecology is an important method and effective approach to realize the sustainable development of industrialization and urbanization. In this article, based on the inter-provincial panel data of western China during 2003-2018, the spatial development trends of industrialization, urbanization and industrial ecology are analyzed, and an empirical method is employed to conduct a robustness test based on the Panel Vector Auto-Regression (PVAR) model to determine the long-term interactions among these three aspects. The results show that it is difficult to manifest the short-term causal relationships among industrialization, urbanization and industrial ecology. After lagging for three periods, they present the Granger causality, the industrial ecology and industrialization have promoted urbanization, and the coefficient for the influence of industrial ecology on urbanization is 0.4612. However, industrialization and urbanization have negative impacts on industrial ecology, and with a 1% increase in industrialization or urbanization, the industrial ecology will decline by 0.2261% or 0.2850%, respectively. With the continuation of the lagging period, industrial ecology will have better interpretability than industrialization and urbanization, and industrialization and eco-friendly development have strong self-accumulation development mechanisms, while the self-accumulation mechanism of urbanization is not obvious, and it might even have a decline. By fulfilling the role of the regional leading industry, the state of unbalanced internal development can be improved, so as to realize mutual promotion between industrialization and urbanization. By improving the utilization rates of resources and energy, efforts should be made to implement green production, significantly promote industrial ecology, and boost high-quality development of both the regional economy and society.

Key words: industrialization, urbanization, eco-friendly development, PVAR model

WANG Yajun. Relationship between Industrialization, Urbanization and Industrial Ecology in Western China: A Panel Vector Auto-Regression Model Analysis[J]. Journal of Resources and Ecology, 2021, 12(1): 68-79.

Figures/Tables 9

References 30

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Destination layer	Criterion layer	Index layer
	Economic development level	Per capita GDP (22.9)
Industrialization	Economic scale	Proportion of the gross product of tertiary industry in total GDP (18.1) Proportion of labor force population from secondary and tertiary industries in the total labor force population (16.5)
Government promotion Modernization level of agriculture	Proportion of R&D expenditure in GDP (14.9) Proportions of fiscal revenue and fiscal expenditure (15.4) Total power of agricultural machinery per hectare (12.2)
Urbanization	Spatial distribution of population among cities and rural areas	Proportion of urban population in total population (100)
	Industrial development level	Growth speed of industrial added value (21.6)
Industrial ecology	Energy and resource utilization	Total energy consumption (17.4) Employment of extractive industry/ Total employment (18.2)
	Environmental pollution degree	Industrial waste water discharge (14.1) Industrial sulfur dioxide emission (14.5) Industrial solid waste utilization rate (14.2)

Destination layer	Criterion layer	Index layer
	Economic development level	Per capita GDP (22.9)
Industrialization	Economic scale	Proportion of the gross product of tertiary industry in total GDP (18.1) Proportion of labor force population from secondary and tertiary industries in the total labor force population (16.5)
Government promotion Modernization level of agriculture	Proportion of R&D expenditure in GDP (14.9) Proportions of fiscal revenue and fiscal expenditure (15.4) Total power of agricultural machinery per hectare (12.2)
Urbanization	Spatial distribution of population among cities and rural areas	Proportion of urban population in total population (100)
	Industrial development level	Growth speed of industrial added value (21.6)
Industrial ecology	Energy and resource utilization	Total energy consumption (17.4) Employment of extractive industry/ Total employment (18.2)
	Environmental pollution degree	Industrial waste water discharge (14.1) Industrial sulfur dioxide emission (14.5) Industrial solid waste utilization rate (14.2)

Variable	Levin-Lin-Chu (LLC) test statistic value	Augmented Dicey-Fuller (ADF) test statistic value	Result
lnIN	3.4592	8.0304	Non-stationary
lnUR	2.4235	10.0871	Non-stationary
lnEC	8.7021	11.7962	Non-stationary
d_lnIN	-2.9323^**	-4.9669^**	Stationary
d_lnUR	-7.4089^**	-10.1276^*	Stationary
d_lnEC	0.4426^**	-1.2723^***	Stationary

Variable	Levin-Lin-Chu (LLC) test statistic value	Augmented Dicey-Fuller (ADF) test statistic value	Result
lnIN	3.4592	8.0304	Non-stationary
lnUR	2.4235	10.0871	Non-stationary
lnEC	8.7021	11.7962	Non-stationary
d_lnIN	-2.9323^**	-4.9669^**	Stationary
d_lnUR	-7.4089^**	-10.1276^*	Stationary
d_lnEC	0.4426^**	-1.2723^***	Stationary

Lag	Bayesian Information Criterion (BIC)	Akaike Information Criterion (AIC)	Hannan Quinn Information Criterion (HQIC)
PVAR(1)	-6.433	-5.831	-6.279
PVAR(2)	-7.542	-3.955	-4.171
PVAR(3)	-8.496	-6.548	-7.098
PVAR(4)	-6.028	-4.103	-5.864