Evaluation of Land Carrying Capacity of 31 Provinces in China Based on a Natural–Societal-Supply–Demand Framework

Abstract

Land carrying capacity (LCC) refers to the ability of land resources to support human activities, and has become an important tool for research into the man–land relationship. Due to the 31 provinces of China possessing huge differences in resource and social development, it is necessary to understand the key factors and differences in LCC in each province. This paper provided a framework for the Natural–Societal-Supply–Demand of LCC, and analyzed the LCC of the 31 provinces of China via the cross relationship between the four subsystems. In total, 22 indicators were selected, and the weight of the indicators was calculated via the gray correlation coefficient. Through this framework, the LCC is endowed by natural resources and is improved by social development; the comprehensive LCC of the 31 provinces was evaluated using geographic information data and statistical data, and the main causes of overload were analyzed by using the obstacle model. The results show that (1) The natural resources of most provinces in China cannot support the current population; (2) Social development has significantly improved the LCC; and (3) The shortage of natural resources is the key factor in LCC overloading in most provinces. Insufficient food supply, insufficient carbon sequestration, the shortage of construction land, and insufficient water supply are the main causes of overload in China.

Assessing Land Resource Carrying Capacity in China’s Main Grain-Producing Areas: Spatial–Temporal Evolution, Coupling Coordination, and Obstacle Factors

“…Both a higher LRCC score and a lower LRCC score do not represent a suitable carrying capacity of land resources. Instead, an ideal LRCC score that ensures a balance between the carrying capacity of land resources and the pressure exerted by human activities is deemed appropriate for realizing the sustainable use of land resources [25,72]. Specifically, in regions with relatively better socioeconomic development, LRCC scores tend to be higher due to the relatively larger consumption of land resources.…”

“…When viewed from a spatial dimension, the overall spatial distribution of LRCC in the MGPAs displays a characteristic pattern of "higher in the south, lower in the north". The evaluation of LRCC requires a comprehensive examination that takes into account the supply capacity of land resources as well as the pressure generated by human activities [47,72,86]. Provinces with a surplus of LRCC tend to be concentrated in the less densely populated northern regions, such as Heilongjiang and Liaoning.…”

“…Identifying obstacle factors and key obstacle indicators aids in analyzing the critical factors influencing LRCC and provides targeted policies and recommendations to promote the future development of MGPAs [72]. With the rapid development of the economy and society, MGPAs continuously adjust and improve relevant land-use policies, while enhancing the protection of the ecological environment.…”

“…Indicators of the state subsystem and pressure subsystem, as direct reflections of the ecological environment of land resources, have become the primary obstacles affecting LRCC, especially with the accelerated urbanization process. Therefore, in the context of the new economic normal, if MGPAs aim to enhance LRCC, they should focus on the pressure and state subsystems, adopt more scientifically rational land planning, increase efforts in environmental protection, alleviate pressure on land resources, and improve the state of land resources [47,72,91].…”

A Study on Resource Carrying Capacity and Early Warning of Urban Agglomerations of the Yellow River Basin Based on Sustainable Development Goals, China

“…The origin of the carrying capacity concept comes from biology and ecology [4], and the resource carrying capacity has been widely evaluated, from studies that assess single elements to much more comprehensive evaluations [5], which primarily focus on the study of the land carrying capacity [6], water carrying capacity [7,8], ecological carrying capacity [9], and other elements with potent relevance. The specific measures of resource carrying capacity include the system dynamics model [10,11], multiobjective function [12], neural network [13], ecological footprint method [14], principal component analysis [15], hierarchical analysis [16], and the TOPSIS method [17], which have all undergone development from an original focus on single resource constraints to comprehensive assessments of the human occupation of resources.…”