A Dynamic Performance and Differentiation Management Policy for Urban Construction Land Use Change in Gansu, China

Abstract

Making efforts to promote rationalized urban construction land change, distribution, allocation, and its performance is the core task of territory spatial planning and a complex issue that the government must face and solve. Based on the Boston Consulting Group matrix, a decoupling model, and a GIS tool, this paper constructs a new tool that integrates “dynamic analysis + performance evaluation + policy design” for urban construction land. We reached the following findings from an empirical study of Gansu, China: (1) Urban construction land shows diversified changes, where expansion is dominant and shrink cannot be ignored. (2) Most cities are in the non-ideal state of LH (Low-High) and LL (Low-Low), with a small number in the state of HH (High-High) and HL (High-Low). (3) Urban construction land change and population growth, economic development, and income increase are in a discordant relationship, mostly in strong negative decoupling and expansive negative decoupling. (4) The spatial heterogeneity of urban construction land change and its performance are at a high level, and they show a slow upward trend. Additionally, the cold and the hot spots show obvious spatial clustering characteristics, and the spatial pattern of different indexes is different to some extent. (5) It is suggested that in territory spatial planning Gansu should divide the space into four policy areas—incremental, inventory, a reduction development policy area, and a transformation leading policy area—to implement differentiated management policies and to form a new spatial governance system of “control by zoning and management by class.” The change of urban construction land, characterized by dynamics and complexity, is a direct mapping of the urban growth process. The new tools constructed in this paper will help to reveal the laws of urban development and to improve the accuracy of territory spatial planning in the new era. They are of great theoretical significance and practical value for promoting high-quality and sustainable urban development.

Traditional Village Morphological Characteristics and Driving Mechanism from a Rural Sustainability Perspective: Evidence from Jiangsu Province

“…For layer h and the study area, Nh and N represent the number of traditional villages, σ 2 h and σ 2 denote the variance of sustainable rural development levels (Y i ), and SSW and SST represent the sums of squares. The interaction relationship can be classified into five types based on the relationship between the interaction influence and direct influence (Max (q (X i )), q (X j )), min (Min (q (X i ), q (X j )) and sum (q (X i ) + q (X j )) [51].…”

Research on the Geographical Pattern, Evolution Model, and Driving Mechanism of Carbon Emission Density from Urban Industrial Land in the Yangtze River Economic Belt of China

“…For example, for the driving factors X i and X j , q(X i ) and q(X i ∩ X j ) represent their single-factor driving force and dual-factor interaction, while Min(q(X i ), q(X j )), Max(q(X i ), q(X j )), q(X i ) + q(X j )) represent the minimum, maximum, and sum of their single-factor driving forces. In the analysis of the interaction, the interactions were classified into five types by analyzing the dual-factor interaction in comparison with the maximum value of the one-factor interaction effect, the minimum value, and the sum of the two [47]. When q(X i ∩ X j ) < Min(q(X i ), q(X j )) and Min(q(X i ), q(X j )) < q(X i ∩ X j ) < Max(q(X i )), q(X j )), it is determined that the interaction between the driving factors X i and X j is nonlinear weakened and single nonlinear weakened, respectively.…”

The Dynamics and Driving Mechanisms of Rural Revitalization in Western China

“…where h is the number of spatial partitions, N h and N are the numbers of cities in partition h and the study area, σ 2 h and σ 2 are the squares of partition h and the study area, respectively; SSW is the within sum of squares in the partition; and SST is the total sum of squares in the study area. Interaction effects are classified into five types based on the relationship between interactive influence and direct influence [63]. Nonlinear weakening and single nonlinear weakening represent antagonistic effects between different factors, and measures should be taken to prevent the pairing of the two factors.…”