Analysis of material stock accumulation in China’s infrastructure and its regional disparity

Abstract

Materials stocked in infrastructure provide necessary personal and economic services, and are also closely linked with massive resource extraction, energy consumption and waste generation. To support policy deliberations toward regional harmony and sustainable development, this paper examines the temporal change during 1978–2008 and spatial patterns of ten types of materials stocked in four major infrastructures (residential buildings, roads, railways, and water pipelines) in 31 provinces in China, and diagnoses regional disparity and driving factors by Theil index and multivariable regression based on panel data. It was found that the total material stock has boomed to 42.5 billion tons in 2008, with its per capita level increased by nine times over that in 1978. Over 90 % of materials are concentrated in residential buildings and roads, and are spatially inclined to decrease from coastal regions to inland areas. Since China has shifted its strategy from an inclined to harmonious regional development, the overall inequality of per capita material stock has been changing toward equality with its scale contributed mainly by inter-regional inequality, and downward trend affected dominantly by intra-regional inequality. To balance the growth speed across regions meanwhile, to develop economy and attract foreign investment in each region, would be a promising route towards reducing regional inequality. Moreover, the enhancement of governmental performance and construction of each sector’s share would also be effective for decreasing inter-regional gaps.

Appendix: the deduction process of Theil index

According to Eq. (4), the overall Theil can be decomposed into intra-regional (T _intra) and inter-regional (T _inter) disparity, which is expressed as follows:

$$ \left\{ \begin{gathered} T_{\text{intra}} = \sum\limits_{i} {\frac{{P_{i} }}{P}} T_{i} = \sum\limits_{i} {\frac{{P_{i} }}{P}} \left[ {\sum\limits_{j} {\frac{{P_{ij} }}{{P_{i} }}} \ln \left( {\frac{{y_{i} }}{{y_{ij} }}} \right)} \right] \hfill \\ T_{\text{inter}} = \sum\limits_{i} {\frac{{P_{i} }}{P}} \ln \left( {\frac{{y}}{{y_{i} }}} \right) \hfill \\ \end{gathered} \right. $$

Thus, the summary of T _intra and T _inter is

$$ \begin{aligned} T_{\text{intra}} + T_{\text{inter}} & = \sum\limits_{i} {\frac{{P_{i} }}{P}} \left[ {\sum\limits_{j} {\frac{{P_{ij} }}{{P_{i} }}} \ln \left( {\frac{{y_{i} }}{{y_{ij} }}} \right)} \right] + \sum\limits_{i} {\frac{{P_{i} }}{P}} \ln \left( {\frac{y}{{y_{i} }}} \right) \\ & = \sum\limits_{i} {\frac{{P_{i} }}{P}} \left[ {\sum\limits_{j} {\frac{{P_{ij} }}{{P_{i} }}} \ln \left( {\frac{{y_{i} }}{{y_{ij} }}} \right) + \ln \left( {\frac{y}{{y_{i} }}} \right)} \right] \\ & = \sum\limits_{i} {\frac{{P_{i} }}{P}} \left[ {\sum\limits_{j} {\frac{{P_{ij} }}{{P_{i} }}} \ln \left( {\frac{{y_{i} }}{{y_{ij} }}} \right) + \sum\limits_{j} {\frac{{P_{ij} }}{{P_{i} }}} \ln \left( {\frac{y}{{y_{i} }}} \right)} \right]\left( { \because \sum\limits_{j} {\frac{{P_{ij} }}{{P_{i} }}} = 1} \right) \\ & = \sum\limits_{i} {\frac{{P_{i} }}{P}} \left[ {\sum\limits_{j} {\frac{{P_{ij} }}{{P_{i} }}} \ln \left( {\frac{{y_{i} }}{{y_{ij} }} \times \frac{y}{{y_{i} }}} \right)} \right] \\ & = \sum\limits_{i} {\frac{{P_{i} }}{P}} \left[ {\sum\limits_{j} {\frac{{P_{ij} }}{{P_{i} }}} \ln \left( {\frac{y}{{y_{ij} }}} \right)} \right] \\ & = \sum\limits_{i} {\frac{{P_{i} }}{P}} \left[ {\frac{1}{{P_{i} }}\sum\limits_{j} {P_{ij} } \ln \left( {\frac{y}{{y_{ij} }}} \right)} \right] \\ & = \sum\limits_{i} {\sum\limits_{j} {\frac{{P_{ij} }}{P}} } \ln \left( {\frac{y}{{y_{ij} }}} \right)T = T \\ \end{aligned} $$