In a world of limited resources, finding the balance between development and sustainability is one of humanity's greatest challenges.
Imagine your hometown. Now, picture it doubling in population, with new factories, farms, and neighborhoods spreading across the landscape. A simple question arises: Can the local environment and resources actually support all this new growth?
This is not a theoretical exercise. It is the very real challenge that China has faced during its rapid urbanization. To answer it, the country has turned to a powerful planning tool: evaluating the Resources and Environment Carrying Capacity (RECC) to divide its provinces into "Principal Function Areas." Let's explore the science behind this massive undertaking.
The concept of carrying capacity originated in physics, referring to the maximum load an object can bear without breaking 1 . Ecologists later adopted it to describe the maximum number of a species an environment can support indefinitely.
Today, Resources and Environment Carrying Capacity (RECC) is understood as "the maximum amount of resources that nature can provide for human activities and sustaining social development without causing irreversible damage to the ecosystem" 8 .
RECC is a scientific measure of the relationship between human socioeconomic activities and the natural environment, and a crucial tool for managing sustainable development .
In China, the RECC evaluation is the foundation for Principal Function Area Division—a spatial planning strategy that breaks away from the old model of pursuing economic growth at all costs. Instead, it aims to coordinate development based on what each region's resources and environment can actually handle 3 .
So, how do you measure something as complex as a region's carrying capacity? Scientists have developed a systematic approach, building a detailed index system that acts as a diagnostic toolkit for the health of a region.
This model views RECC as a balance between two forces. Resource and Environmental Support (RES) represents the capacity of nature and human-made infrastructure to sustain life and economy. Resource and Environmental Pressure (REP) represents the demands placed by human activities . A region's carrying capacity is determined by the interplay between these two.
This approach, proposed by the Chinese government in 2012, classifies land based on its primary function 2 .
Creating the actual checklist of metrics is a meticulous process. A study focusing on land resources in 31 provinces, for instance, selected indicators from three critical land types 1 :
Evaluated using metrics like per capita grain output and per capita cultivated area.
Assessed based on its capacity to support urbanization and economic activity.
Its capacity was measured through its role in maintaining environmental stability.
To ensure the final index system is both comprehensive and efficient, researchers use statistical methods like correlation analysis and information contribution rate analysis to identify and remove redundant indicators, leaving only the most representative and impactful ones 5 .
| Target Layer | Criteria Layer (Subsystems) | Sample Indicator Layer |
|---|---|---|
| Provincial RECC | Resource Subsystem | Water resources per capita 5 , cultivated land area per capita 1 , mineral reserves |
| Environmental Subsystem | PM2.5 concentration, forest coverage rate 5 , wastewater treatment rate | |
| Ecological Subsystem | Biodiversity index, soil erosion control rate | |
| Socio-Economic Subsystem | GDP per capita, urbanization rate, R&D investment intensity |
To see how this works in practice, let's examine a real-world case study: the division of Principal Function Areas in Guiyang City 3 .
Researchers set out to classify Guiyang's ten administrative districts based on their Relative Resource Carrying Capacity (RRCC). This method compares a region's capacity to a larger reference area (in this case, the whole of China), rather than looking at absolute limits.
The team gathered population and resource data for Guiyang's districts and the national reference area from 2013 to 2017, using sources like the China Statistical Yearbook and the Guiyang Statistical Yearbook 3 .
They calculated the RRCC for each district based on key resources like land, water, and economic potential.
Districts were then classified into different Principal Function Areas based on their carrying capacity and development potential.
Finally, they analyzed the evolution of carrying capacity from 2013 to 2017 to understand dynamic changes.
The study successfully divided Guiyang into three types of functional zones, each with tailored development recommendations 3 :
Areas with sufficient carrying capacity to support concentrated urbanization and industrial growth.
Areas where carrying capacity was more limited, requiring optimization and transformation of existing development patterns to reduce environmental pressure.
Similar to Zone I, but with different resource constraints. Focus on sustainable transformation and reducing ecological pressure.
| Principal Function Area Type | Defining Characteristics | Development Policy Focus |
|---|---|---|
| Key Development Zone | Sufficient resource carrying capacity for further growth. | Concentrated urbanization, industrial clustering. |
| Optimizing Development Zone I | Limited carrying capacity requiring structural adjustment. | Optimizing and transforming existing industrial and urban patterns. |
| Optimizing Development Zone II | Similar to Zone I, but with different resource constraints. | Sustainable transformation and reducing ecological pressure. |
The analysis also revealed that Guiyang was primarily in an "optimizing development zone," and that changes in its relative resource carrying capacity had been relatively stable since 2013 3 . This kind of clear, data-driven zoning provides a scientific basis for regional planning, helping to prevent disordered development that exhausts local resources.
The credibility of RECC evaluation hinges on robust scientific methods. Researchers employ a diverse toolkit of statistical models and technologies to process complex data and ensure objective results.
| Tool/Method | Primary Function | Why It's Important |
|---|---|---|
| Coefficient of Variation Weighting 1 | Determines the weight of each indicator in the index system. | Objectively assigns importance based on data fluctuations, reducing human bias. |
| Combined Weighting TOPSIS Model 6 | Ranks and evaluates the carrying capacity of different regions. | Provides a clear, comparative score for each area, ideal for spatial ranking. |
| Projection Pursuit Model (with Genetic Algorithm) 8 | Handles high-dimensional, non-linear data (like many RECC indicators). | Simplifies complex data patterns for easier analysis and interpretation. |
| Geographic Information System (GIS) 1 2 | Visualizes spatial data and analysis results on maps. | Creates intuitive, visual maps of carrying capacity and function zones. |
| System Dynamics (SD) Models 1 | Simulates the long-term, dynamic interaction between resources, environment, and the economy. | Allows for forecasting future trends and testing policy impacts. |
System Dynamics models help researchers understand how changes in one variable (like population growth) affect the entire system over time, allowing for more accurate predictions of future carrying capacity scenarios.
GIS technology enables the visualization of RECC data on interactive maps, making it easier for policymakers to identify regional disparities and plan accordingly.
The evaluation of Resources and Environment Carrying Capacity is more than an academic exercise—it's a fundamental shift in how we manage our relationship with the planet. By moving from a one-size-fits-all growth model to a nuanced, region-specific approach, this science provides a practical pathway for balancing development with sustainability.
Studies show that China's RECC is extremely uneven, often characterized by "high pressure in the east and low pressure in the west," with a general trend of deterioration in some regions 1 . Furthermore, research from Hubei Province reveals that while social development has steadily advanced, RECC has shown a volatile "V-shaped" trend, indicating the delicate balance between progress and environmental pressure 8 .
The ultimate goal of this work is to create a "life community" for humanity and nature 1 . By scientifically delineating what a piece of land can and should be used for, we can hopefully build a future where cities thrive, farmlands are productive, and natural ecosystems remain healthy and resilient for generations to come.
Urban development aligned with local resource constraints
Agricultural areas protected from inappropriate development
Protected natural spaces maintaining biodiversity