Academic journal article Journal of Sustainable Development

Cultivated Land Area Change in Shenzhen and Its Socio-Economic Driving Forces Based on STIRPAT Model

Academic journal article Journal of Sustainable Development

Cultivated Land Area Change in Shenzhen and Its Socio-Economic Driving Forces Based on STIRPAT Model

Article excerpt

Abstract

The purpose of this paper is to analyze the relationship between prosperous level and cultivated land area were analyzed in Shenzhen city, Method of the STIRPAT model. The results showed that there was no main cause for cultivated land reduction in Guangzhou city (Zhang, 1999). However, population change, changes of the urbanization rate and proportion of the tertiary-industry added value to regional GDP of the area all play important role in the cultivated land reduction. In the scope of observational data, the relationship between the prosperous level and the cultivated land area was not similar to the environmental kuznets curve (EKC). Accordingly, several suggestions were proposed in the study to mitigate the pressure of cultivated land reduction, including population control, urbanization level improvement, industrial structure adjustment, and economic growth mode transition, etc. (Cai & Zhang, 2005).

Keywords: cultivated land area change, socio-economic driving forces, STIRPAT model, Shenzhen city

1. Introduction

Land use and land cover, the most significant landscape of the earth surface system, are restricted and affected by natural and human driving force. In terms of the shorter timescale, human driving force plays a dominant role in change of the land use, especially in the process of accelabrating urbanization in recent years. Rapid growth of the economy and big increase in the number of urban population formed a high demand for industrial sites, residential housing and public areas, which essentially leaded to an increasingly sharper contradiction between arable land protection and the augment of urban construction sites. Therefore, analysing human driving force in the quantity of arable land has a very important significant in predicting cultivated land demand in the condition of urbanization and establishing corresponding policies and measures. The research in the mechanism of driving forces for change in the cultivated land areas has attracted great attentions of many researchers, especially the research in the quantification and modeling of the driving forces (Zhao, 2006).

Some researchers studies the driving factors for conversion in farmland with the method of principal component analysis, replacing previous more variables by fewer new information(keeping the original information as much as possible, in order to reveal the dominant driving force for conversion in farmland. A mathematics analysis is presented on artificial factors of conversion in farmland and space variability with the method of land statistics by other researchers (Su WeizhongYang & Gu Chaolin, 2007). In recent years, the STIRPAT model, which is in the environment research field, has been introduced into the studying of driving forces in cultivated land change. Some researchers have discussed and analysed it from the theory and demonstration aspect, seldom related researches have been released yet. This essay tries to use STIRPAT model, and discusses how socioeconomic factors, such as population, rich extent, industrial structure and urbanization level, affect the cultivated land change in rapid urbanization city, Shenzhen with its open policy (Yang, 2001; 2002; 2004).

2. STIRPAT Model Summary

STIRPAT model inherits from IPAT equation about environmental stress (Chertow, 2000). IPAT equation have been got different types of reconstruct or extend in practical applications. And Rose and Dietz expressed IPAT equation as random form, which analysis the driving force's effect to the environment by stochastic regression of population, affluence and technology, simply called STIRPAT model .the forms are as follows:

I = aPbAcT de (1)

I, P, A, T Respectively Represents environmental stress, the number of population, affluence and technology (York et al., 2003). And the total energy consumption denote environmental pressures. A is the coefficient of the model, b, c, d are the number of population, affluence and technology index of human driving forces, e-model errors. …

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