Spatio‐temporal evolution of regional inequality and contribution decomposition of economic growth: A case study of Jiangsu Province, China

Abstract

Regional inequality in the process of economic growth has recently been a popular topic. This paper creatively proposes contribution decomposition methods to divide various regions' contributions into economic contribution and population contribution. The methods consider not only economic growth but also population change, which may be more effective than the traditional method that uses the per capita GDP (gross domestic product) as its single measurement indicator. Moreover, the objective is to take a step forward and reveal the spatio‐temporal evolution characteristics of regional disparities by integrating barycentre model and economic development equilibrium model. Lastly, the applicability of the research method proposed in this paper is verified by a case study of Jiangsu province, China; the results show a new regional economic pattern in Jiangsu province.

Proposal for Planning an Integrated Management of Hazardous Waste: Chemical Park, Jiangsu Province, China

“…Jiangsu is a typical province with unbalanced economic development. Total contribution of the three regions to the economic growth of Jiangsu in 2007-2014 varied from 57.63% (Sounthern Jiangsu) to 18.43 (Central Jiangsu) [28]. As indicated in Figure 1 and Table 2, 53 chemical parks in Jiangsu Province are evenly distributed in three regions.…”

The effects of China’s growth slowdown on its provinces: Disentangling the sources

“… There is a large literature on the nature and sources of China's regional disparities; some recent ones include Peng, Peng, Chen, and Xia (), Wang and Rickman (), Wang, Wu, Wang, Zhou, and Ding (), Liu, Xu, Wang, Zhao, and Xie () and Su, Song, Ma, and Tao (). …”

“…ENDNOTES 1 There is a large literature on the nature and sources of China's regional disparities; some recent ones include Peng, Peng, Chen, and Xia (2018), Wang and Rickman (2018), Wang, Wu, Wang, Zhou, and Ding (2018), Liu, Xu, Wang, Zhao, and Xie (2019) and Su, Song, Ma, and Tao (2019). 2 See Chen and Groenewold (in press) and Dong and Liu (2018) for a recent discussion of this issue.…”