The Spatiotemporal Coupling and Synergistic Evolution of Economic Resilience and Ecological Resilience in Africa

Abstract

Investigating the spatiotemporal coupling and coordinated evolution of economic and ecological resilience in Africa provides theoretical support and scientific foundation for the continent’s green and high-quality development. From the perspective of evolutionary resilience, this study constructs an evaluation model for Africa’s economic resilience and ecological resilience. Using kernel density models, namely the “economic-ecological” resilience zoning method, the coupling coordination degree model, and the Haken model, this study explores the spatiotemporal alignment, coupling, and synergistic evolution of economic and ecological resilience in Africa in a step-by-step manner. The results show that (1) the overall level of economic resilience in Africa is relatively low, with increasing regional disparities. Spatially, economic resilience exhibits a distribution pattern of “low values widely spread, high values concentrated”; the level of ecological resilience, in contrast, shows a more pronounced dispersion, with a spatial distribution of “low values concentrated, high values dispersed”; (2) based on the “economic-ecological” resilience zoning method, most African countries and regions fall into the low economic resilience category, with weak economic resilience and prominent issues related to economic instability. The seven major high economic resilience zones largely overlap with the high economic resilience-high ecological resilience areas, demonstrating good spatiotemporal alignment between economic and ecological resilience; (3) in terms of the spatiotemporal coupling relationship between economic resilience and ecological resilience, most of Africa falls into the disordered category, with an increasingly obvious polarization trend in the coupling coordination degree; (4) from the perspective of the synergistic relationship between economic resilience and ecological resilience, ecological resilience dominates the symbiotic system formed by economic resilience and ecological resilience. The development of ecological resilience and economic resilience is mutually inhibitive, with prominent contradictions between the economy and the environment. Ecological and economic resilience have formed an internal mechanism of positive feedback in the synergistic system. The regional differences in the synergistic value have expanded, while the differences within regions have narrowed, indicating an emerging trend of spatial differentiation.

1. Introduction

The coordinated and sustainable development of the economy and ecology has become a key agenda for achieving sustainable development by 2030 [1]. The economic system is deeply rooted in the ecological environment system, which moderately transforms natural capital and ecological resources into economic products for human use. The ecosystem, in turn, provides the material foundation for the development of the economic system and serves as the most critical resource for economic security. However, due to the highly efficient and profit-driven nature of factors like capital within the economic system, the “fundamental relationship” between ecology and economy has gradually deteriorated, even leading to the potential collapse of both systems. On one hand, the rapid pursuit of high efficiency in economic development often results in industrial clustering and upgrading, but it also causes regional imbalances in ecosystems, degradation, and even the collapse of local ecological functions. On the other hand, ecosystems on the brink of collapse significantly weaken their ability to support economic resources and materials, potentially triggering environmental pollution and natural disasters, which cause substantial economic losses and severely hinder sustainable economic development.

Since the 21st century, Africa has experienced rapid economic growth, leveraging its rich natural resource endowments and advantageous geographic conditions. The total GDP has increased from $670.86 billion in 2000 to $2.8 trillion in 2023. Among Africa’s 54 countries, 18 countries had GDP growth rates exceeding 5% in 2023, making Africa the second fastest-growing region globally, after Asia [2]. However, the development model reliant on resource extraction and foreign investment has also led to a series of environmental issues, such as desertification and water resource depletion. Consequently, the issue of coordinated and sustainable economic and ecological development has become a forward-looking topic for promoting green, high-quality development in African countries.

The concept of “resilience” is interdisciplinary and spans multiple fields. Initially applied in engineering, Holling adapted the idea of multiple equilibria from systems theory and introduced it into the field of ecology [3]. As economic crises have periodically erupted, the application of resilience in economics has also become increasingly widespread. At its core, economic resilience and ecological resilience refer to the system’s overall capacity to withstand external disturbances and return to its steady state in both the economic and ecological dimensions. This capacity has become an essential indicator for measuring system equilibrium, stability, and high-quality development.

Research on ecological resilience began relatively early and has developed a relatively well-established system, which can be broadly divided into four categories: (1) Ecological Resilience Assessment Models and Spatiotemporal Pattern Research: The evaluation of ecological resilience has evolved from single indicators to multi-level, multi-dimensional indicator systems. Specific indicator selection often utilizes models such as the “Pressure-State-Response” (PSR) model [4], the “Resistance-Recovery-Adaptive Capacity” model [5], the “Stress Risk-Ecosystem Connectivity-Recovery Potential” model [6], and the robustness model [7]. For comprehensive indicator methods, multi-factor evaluation approaches are often applied, such as entropy weighting [8], game theory-based composite weighting [9], and the TOPSIS method [10]. In terms of spatiotemporal distribution analysis, methods such as spatial autocorrelation [4], kernel density estimation [11], and social network analysis [12] are frequently used based on regional ecological resilience assessments. (2) Exploration of Coupling and Adaptation Relationships: This includes studies on the coupling relationship and adaptation between ecological resilience and urbanization [13,14,15], ecological resilience [16] and ecological risks [17], as well as ecological resilience and urban renewal. (3) Analysis of Influencing Factors, Driving Forces, and Mechanisms of Ecological Resilience: In this area, methods such as the gray relational model [4], set pair analysis [18], exploratory spatiotemporal data analysis (ESTDA) [19], and geographically weighted regression (GTWR) models [20] are employed to analyze the economic geography of ecological resilience. (4) Application of Ecological Resilience: Ecological resilience is applied in various fields, including ecological zoning of urban clusters [21], urban renewal policies [22], and predicting and identifying gaps in protected natural areas [6]. These four categories highlight the comprehensive and multidimensional nature of ecological resilience research, as well as the broad range of analytical methods and applications across different regions and sectors.

Research on economic resilience began relatively late, and while the methodologies adopted are closely related to those in ecological resilience studies, economic resilience research incorporates more regional economic thinking in the construction of evaluation indicator systems. The goal of economic resilience research is to explore the ability of regional economies to recover and restore equilibrium in response to changes in the external economic environment and external economic threats. However, as academic definitions of regional economic equilibrium, external threats, and localized economic crises are not standardized, the concept of economic resilience remains more abstract and diverse.

From the perspective of indicator composition, the evaluation methods are divided into single-indicator and multi-indicator system approaches. The advantage of using a single indicator is its ability to observe economic resilience over long periods and fit economic cycles. Commonly, indicators such as GDP and GDP growth rate are employed, alongside methods such as those by Martin et al. [23] for analyzing the time dimensions of maintenance, recovery, reorientation, and revitalization to measure economic resilience over long time scales. The advantage of the multi-indicator system lies in its comprehensiveness and integration. When conducting multi-indicator system assessments, the focus is often on factors such as industrial structure [24], land use types [25], industrial collaborative agglomeration [26], industrial ecological transformation, globalization level, and population density [27]. These systems are gradually refined according to different focal points of the indicators, such as marine economic resilience [28], agricultural economic resilience [29], and tourism economic resilience [30], each with distinct strengths.

As for influencing factors, the clustering of productive services [31], industrial coordination and clustering [32], market integration [33], the unified national market [34], the business environment [35,36], and tax structures [37] are all hot topics in economic resilience research. While substantial progress has been made in the study of economic resilience, there is still room for further exploration. One significant limitation is that much of the existing literature tends to focus on internal macroeconomic factors, primarily using economic logic to explain economic issues. Currently, there is a relative scarcity of studies that adopt a geographically integrated perspective on economic resilience.

However, the stability of natural ecosystems is a crucial safeguard for promoting economic development. Only a stable and healthy ecosystem can reliably supply the resources needed for economic growth, thereby fundamentally enhancing the regional economy’s ability to withstand external risks. Yet, the literature on the relationship between ecological resilience and economic resilience remains sparse, and further research is needed to refine the underlying logic of their interconnections.

The relationship between economy and ecology was first mentioned in 1987 when the World Commission on Environment and Development introduced the concept of sustainable development. Scholars realized that the absolute growth of both economic and ecological systems often does not lead to the maximization of system benefits. Methods to handle the coordination between the two have become a key issue for achieving sustainable development [38]. Research on this relationship has evolved from coupling studies to the study of synergies and from quantitative to qualitative analysis. Scholars in the field of ecology were the first to introduce the coefficient of variation from statistics to quantify the proximity between the economic system and the ecological system [38,39,40]. Subsequently, while studying the relationship between urbanization and ecological resilience, Liu Yaobin and others introduced the concept of coupling from physics and electromagnetism, where two systems affect each other, and derived the coupling coefficient model to quantitatively describe the interaction between the two systems [41]. This model was then widely used in studies on the relationship between urbanization and ecological resilience [42], between ecological and economic systems [43], between land use and urban economic resilience [25], and between economic and ecological resilience [5].

Some scholars have extended and improved the traditional coupling coordination model, which describes the relationship between dual systems, and developed coupling coordination models capable of quantitatively describing the interrelationship of three or even more systems [38]. As static models, the coefficient of variation and coupling coordination models have the advantage of accurately reflecting the spatial coupling relationship between systems, directly measuring the relative distance between systems based on cross-sectional data. However, due to the lack of comparability over time, they are inadequate in revealing the underlying synergistic logic between systems and in promoting system synergy. To address this deficiency, scholars have incorporated the time dimension into the fitting process between systems, developing a series of dynamic models. Li Chongming and others, in studying the relationship between resource-environment and socio-economic systems in small towns, summarized the triangular function coordination model from a systems theory perspective [44]. The application of the triangular function coordination model depends heavily on the sample size and fitting accuracy as the key lies in fitting the system’s evolutionary trajectory curve equation. Rui Xiao and others, in studying the relationship between urbanization and ecosystem service value in the Shanghai-Hangzhou Bay Metropolitan Area, added an adjustment coefficient to the coupling coordination model to incorporate the time dimension, thus deriving the improved coupling coordination model [45].

With the development of synergetics, the coupling and synergy theory based on sequence parameters and the Haken model has gradually been improved and applied widely in fields such as green finance and green economy [46], financial technology and the green transformation of manufacturing industries [47], digital technology and zero-waste cities [48], and ecological resilience and efficiency [49].

This model, while overcoming the shortcomings of static models by incorporating the time dimension, seeks to find stable critical points of sequence parameters to determine whether the phase transition of the complex system has entered a more advanced stage of coordinated development. However, the use of this model requires data to meet fitting accuracy and “adiabatic approximation assumptions,” and is not suitable for systems where sequence parameters cannot be identified. Therefore, in analyzing the coupling and coevolution of economic resilience and ecological resilience in this paper, we employ both the coupling coordination model and the Haken model. The coupling coordination model is well suited for spatial analysis, while the Haken model excels in time-dimension analysis. By utilizing both models, we aim to reveal the spatiotemporal relationship between economic resilience and ecological resilience in Africa from multiple perspectives.

Africa, the continent with the highest concentration of developing countries, possesses vast natural resources and advantageous geographical conditions that offer significant potential for economic development. As a result, it has become an important supporter, participant, and beneficiary of China’s “Belt and Road” initiative. According to existing research, some regions in Africa have already experienced ecological problems such as water scarcity, large-scale crop yield reductions, and environmental degradation, all of which have been triggered by economic development and efforts to enhance economic resilience.

In this context, the spatial-temporal evolution characteristics of economic and ecological resilience in Africa, the spatial-temporal coupling relationship between the two during their evolution, whether the development of economic resilience directly hinders the protection of ecological resilience, and how to maximize the benefits of the economic-ecological system have become urgent issues that need to be addressed.

Based on this, this paper first constructs separate evaluation models for economic resilience and ecological resilience from the perspectives of the economy and ecology. The spatial-temporal evolution characteristics of economic and ecological resilience in Africa are analyzed at both the national and regional geographical grid scales. Next, the four-quadrant analysis method is used to analyze the spatial-temporal matching relationship between economic resilience and ecological resilience in Africa from a macro perspective. Finally, the coupling coordination model and the Haken model are employed to conduct a quantitative analysis of the spatial-temporal coupling and co-evolution of economic and ecological resilience in Africa from both static and dynamic perspectives.

On the one hand, through the construction of an economic and ecological resilience evaluation system, this paper reveals, to some extent, the current spatial-temporal evolution of economic and ecological resilience in Africa. On the other hand, by exploring the spatial-temporal coupling and co-evolution relationship between the two, it unveils the relationship between Africa’s economy and environment, as well as the relationship between industry and ecology, providing scientific support for promoting Africa’s green and sustainable development.

2. Materials and Methods

2.1. Study Area

Africa, the second-largest continent in the world, covers an area of approximately 30.2 million km². It is bordered by the Mediterranean Sea to the north, the Indian Ocean to the east, and the Atlantic Ocean to the west, spanning both sides of the equator. The region generally exhibits an elevation pattern that is higher in the southeast and lower in the northwest. The continent’s complex landscape is dominated by plains, plateaus, mountains, hills, and basins, with significant climatic variation, as shown in Figure 1.

The climate of North Africa is mainly characterized by Mediterranean and tropical desert climates. West Africa primarily experiences tropical desert and tropical savanna climates, while the Gulf of Guinea region has a tropical rainforest climate. In East and Central Africa, tropical savanna climates are widespread, and the eastern plateau areas are marked by a highland mountain climate. In Southern Africa, Mediterranean, tropical desert, and tropical savanna climates are all present.

2.2. Division of Study Units

In order to observe the spatiotemporal relationship between economic resilience and ecological resilience from multiple spatial perspectives, as well as considering the availability and operability of data, this study adopts two spatial scales: the national scale and the regional geographic grid scale. The national scale focuses on 52 African countries (or regions) as the research units (excluding Somalia and South Sudan), with spatiotemporal analysis conducted at a macro level. The regional geographic grid scale is based on the use of the Create Fishnet tool in the ArcGIS 10.6 software to construct a 20 km × 20 km geographic grid (with a total of 76,209 grid units), allowing for a more detailed, micro-level spatiotemporal analysis.

2.3. Data Sources

The research data include statistical yearbook data, population spatialization data, and land use data. Statistical Yearbook Data: The primary sources are from the “African Statistical Yearbook” (2009–2021), the “International Statistical Yearbook,” and national (or regional) statistical yearbooks. Population Spatialization Data: These data are primarily derived from the LandScan Global Population Dynamics Analysis Database (https://landscan.ornl.gov/, accessed on 16 July 2021). This population database has a resolution of approximately 1 km × 1 km and has been widely used in studies on population distribution. Land Use Data: The land use data mainly come from the European Space Agency’s (ESA) 2008–2020 land cover data (ESA-CCI), with a resolution of 300 m × 300 m. These data are reclassified into six categories based on the Intergovernmental Panel on Climate Change (IPCC) land use classification system, which includes 37 land use types (https://www.esa-landcover-cci.org/, accessed on 16 July 2021).

2.4. Research Methods

2.4.1. Construction of the Economic Resilience Assessment Model

Economic resilience refers to the ability of an economic system to achieve sustainable development by adjusting its economic structure and development model in response to external disturbances or challenges [5]. In this study, we refer to the research results of Song Yuchen, Jiang Daliang, and others and adopt the entropy weight method to construct the economic resilience assessment model for Africa [50,51]. The model specifically includes four dimensions: scale resilience, fiscal resilience, openness resilience, and structural resilience (

Table 1. System of economic resilience indicators for the African region.

Primary Indicators	Secondary Indicators	Measurement Indicators	Unit of Measurement	Direction
Scale Resilience	Economic Size	GDP at Current market prices	Million US Dollars	Positive
	Asset Size	Gross Capital Formation	Million US Dollars	Positive
	Population Size	Economic Active Population	Thousands	Positive
	Consumer Market Size	Residential Consumer Expenditure	Million US Dollars	Positive
Fiscal Resilience	Government Revenue	General Budgetary Revenue	Million US Dollars	Positive
	Government Payment Ability	Government Fiscal Surplus	Million US Dollars	Positive
	Government Endogenous Debt Repayment Ability	Unpaid External Debt/GDP	%	Negative
	Government Exogenous Debt Repayment Ability	External Debt Service/Gross Exports	%	Negative
	Government Fiscal Self-Sufficiency Level	General Budgetary Revenue/Expenditure	%	Positive
	Government International Balance of Payments	Current (Transactions) Account Balance/GDP	%	Positive
		Trade Balance/GDP	%	Positive
Openness Resilience	Foreign Trade Dependence Ratio	Total Import and Export/GDP	%	Positive
	Foreign Capital Dependence Ratio	Foreign Direct Investment/GDP	%	Positive
	Official Aid Dependence Ratio	Government Development Assistance/GDP	%	Positive
Structural Resilience	Manufacturing Industry Index	Manufacturing Industry Output/GDP	%	Positive
	Mining and Energy Industry Index	Mining and Energy Industry Output/GDP	%	Negative

The data source is from the African Statistical Yearbook, and the years covered in the study are 2008–2020.

) [50]. These four aspects are key to understanding the overall economic resilience of African countries and regions as they reflect the capacity to maintain stability, recover from disruptions, and adapt to changing circumstances.

2.4.2. Construction of the Ecological Resilience Assessment Model

This study uses the “PSR” model to construct the ecological resilience indicator system for Africa, mainly referring to the work of Zhu Rong and Wang Xinxing [5,16]. The ecological resilience assessment model is built from three dimensions: pressure, state, and response. Pressure refers to the disturbances and stresses that the system faces. Ecological risk is used to measure the likelihood of real and potential threats to the ecosystem, with the sources of these threats primarily coming from human economic activities and natural events. Therefore, the “Pressure” dimension is closely aligned with the concept of ecological risk. State refers to the system’s ability to resist external forces and maintain a stable state when under threat. Its essence is closely related to resistance and adaptation. Response refers to the system’s feedback response when disturbed. In essence, it is aligned with the concept of recovery capacity (

Table 2. Ecological resilience evaluation indicator system.

Target Layer	Criterion Layer	Indicator Layer	Type
Ecological Resilience Level	Pressure	Ecological Risk Index	Positive
	State	Ecological Resistance Index	Positive
		Ecological Adaptation Index	Positive
	Response	Ecological Recovery Index	Positive

).

• Ecological Risk Index

Different land use types in the region directly affect the ecological risk status. The ecological risk index is calculated based on the proportion of each land use type in the study unit and the ecological risk weight. The formula is as follows:

$$ER={\sum }_{k=1}^n{\displaystyle \frac{S_k{W}_k}{S}}$$

(1)

In the formula, $S_k$ represents the area (in hm²) of the k-th land use type. $S$ represents the total area (in hm²) of the study unit. $W_k$ is the ecological risk weight for the k-th land use type. The ecological risk weights for different land use types are based on existing academic research. The specific weights are as follows: arable land: 0.32, forest land: 0.12, grassland: 0.16, built-up land: 0.85, unused land: 0.82, and water bodies: 0.53 [15,22,42,52].

2.

Ecological Resistance Index

Ecological resistance is the ability of an ecosystem, derived from its structure and function, to resist external disturbances and threats. This paper, referencing the research findings of Zhao Wei and Li Su, uses ecosystem service value to represent ecological resistance [42,53]. Based on the socio-economic development status of Africa, adjustments are made according to the research findings of Costanza and Mawuk Daniel Ocloo in West Africa, resulting in the “Unit Area Value Equivalent Factor Table” based on the monetary value of Africa in 2020 [54,55]. By combining the land use data of the study area, the ecological service value of each research unit is obtained. The formula is as follows:

$$OP=ESV={\sum }_{i=1}^n{A}_i\times {VC}_i$$

(2)

In the formula, $ESV \mathrm{is}$ the ecological service value of the study area (USD/year). $A_i$ is the area of the i-th land use type (hm²). ${VC}_i$ is the ecosystem service value coefficient for the i-th land use type (USD/(hm²·a)).

3.

Ecological Adaptation Index

Ecological resistance refers to the ability of an ecosystem to maintain its steady state in response to external disturbances. It is closely related to the stability of the system’s internal landscape structure. This paper, referencing the research findings of Xia Chuyu, uses landscape structure stability-related indicators to express its adaptation (AD) [52]. Landscape structure stability is primarily influenced by landscape heterogeneity and landscape connectivity, which describe two aspects of the ecosystem’s landscape structure. These aspects are not interchangeable, and both are essential. Their weights can be assumed to be equal. Landscape fragmentation is measured using patch density (PD). The weights are shown in

Table 3. Landscape indices and weights.

Target Layer	Criterion Layer	Indicator Layer	Weight
Landscape Index	Landscape Connectivity	Patch Density (PD)	0.5
	Landscape Heterogeneity	Shannon Diversity Index (SHDI)	0.4
		Patch Cohesion Index (COHESION)	0.1

.

$$PD={\displaystyle \frac{NP}{A}}$$

(3)

$$SHDI=-{\sum }_{i=1}^n\ln P_i$$

(4)

$$COHESION=\left[1-{\displaystyle \frac{{\sum }_{j=1}^n{P}_{ij}}{{\sum }_{j=1}^n{P}_{ij}\sqrt{a_{ij}}}}\right]{\left[1-{\displaystyle \frac{1}{\sqrt{A}}}\right]}^{-1}\times 100$$

(5)

$$AD=0.5PD+0.4SHDI+0.1COHESION$$

(6)

In the formula, $PD$ is the patch density. $NP$ is the number of patches. A is the total area of the patches (hm²). $SHDI$ is the Shannon diversity index. $P_i$ is the proportion of the total landscape area occupied by each patch type (%). $n$ is the total number of patch types in the landscape. $COHESION$ is the patch cohesion index. $a_{ij}$ is the area (m²) of each patch in each landscape type. $P_{ij}$ is the perimeter (m) of each patch in each landscape type.

4.

Ecological Resilience Index

Ecological resilience refers to the ability of an ecosystem to restore its original structure and function when subjected to external disturbances or threats. It is expressed as the system’s internal regulatory capacity and is commonly represented by the ecological elasticity coefficient. The formula is as follows:

$$RC={\sum }_{i=1}^n{A}_i\times {RC}_i$$

(7)

In the formula, $A_i$ is the area of the i-th land use type (hm²). ${RC}_i$ is the ecological resilience coefficient of the i-th land use type. This study refers to the research of Wei Guoen and Liu Xiaoping and assigns the following weights to different land use types: arable land: 0.49, forest land: 0.85, grassland: 0.73, built-up land: 0.27, unused land: 0.44, and water bodies: 0.77 [56,57].

Finally, the ecological risk index, the ecological resistance index, the ecological adaptability index, and the ecological resilience index are standardized, and then the ecological resilience index is calculated. The calculation formula is as follows:

$$RES=\sqrt[4]{ER\times OP\times AD\times RC}$$

(8)

In the formula, $RES$ is the ecological resilience index. $ER$ is the ecological risk index. $OP$ is the ecological resistance index. $AD$ is the ecological adaptability index. $RC$ is the ecological resilience index.

2.4.3. Quadrant Analysis Method

The economic resilience index and the ecological resilience index, calculated for each of the 52 countries and regional geographic grid research units, are standardized using range normalization. Then, the quadrant analysis method is applied to determine the spatiotemporal matching relationship between economic resilience and ecological resilience. The X-axis represents the ecological resilience index, and the Y-axis represents the economic resilience index, dividing the analysis into four quadrants. Quadrants I, II, III, and IV represent the four ecological zones: high economic resilience-high ecological resilience, low economic resilience-high ecological resilience, low economic resilience-low ecological resilience, and high economic resilience-low ecological resilience.

2.4.4. Coupling Coordination Degree Model

The coupling coordination degree model is used to describe the interaction, regulation, and coordination relationships between different systems. Economic resilience and ecological resilience are not isolated from each other; rather, they are interdependent and mutually influential. In this paper, the coupling coordination degree model is applied to quantitatively measure the spatiotemporal coupling interaction between economic resilience and ecological resilience at both the national scale and regional geographic grid scale.

$$C={\left[{\displaystyle \frac{U_1{U}_2}{{\left(\frac{U_1+U_2}{2}\right)}^2}}\right]}^{{\displaystyle \frac{1}{k}}}$$

(9)

$$T=\alpha U_1+\beta U_2$$

(10)

$$D=\sqrt{CT}$$

(11)

In the formula, $C$ represents the coupling degree between economic resilience and ecological resilience, with a range of [0, 1]; $U_1$ and $U_2$ represent economic resilience and ecological resilience, respectively; k is the adjustment coefficient, typically set to 2; $T$ is the comprehensive coordination index; $\alpha$ and $\beta$ are the weights of the two systems, typically both set to 0.5; $D$ is the coupling coordination degree between economic resilience and ecological resilience. Referring to Wang Shujia’s research, the coupling coordination degree is divided into two categories: disordered and coordinated, with 10 levels. The disordered category is [0, 0.5], and it is subdivided into five levels: extreme (0, 0.1], severe (0.1, 0.2], moderate (0.2, 0.3], slight (0.3, 0.4], and near (0.4, 0.5]; the coordinated category is (0.5, 1], and it is also subdivided into five levels: barely coordinated (0.5, 0.6], primary (0.6, 0.7], intermediate (0.7, 0.8], good (0.8, 0.9], and excellent (0.9, 1] [56].

2.4.5. Kernel Density Model

Kernel density analysis is a non-parametric estimation method that uses probability density curves to depict the distribution patterns and dynamic evolution of variables [5]. In this study, kernel density analysis is applied to measure the spatiotemporal coupling characteristics of economic resilience and ecological resilience.

$$f\left(\mathrm{x}\right)={\displaystyle \frac{1}{Nh}}{\sum }_{i=1}^{N}K({\displaystyle \frac{X_i-x}{h}})$$

(12)

$$K\left(x\right)={\displaystyle \frac{1}{\sqrt{2\pi }}}exp\left(-{\displaystyle \frac{x^2}{2}}\right)$$

(13)

where $f\left(\mathrm{x}\right)$ is the probability density function of economic resilience; $K\left(·\right)$ is the Gaussian kernel function, and $K\left(x\right)$ is the expression of the Gaussian kernel function. N is the number of observations; h is the bandwidth; $X_i$ represents the sample observation values, and $x$ is the mean of the observation values.

2.4.6. Haken Model

Since the coupling coordination degree model is a static model that does not incorporate the time element into the coupling process, it only reflects relative values within the current year and regional scope, rather than absolute values, and thus lacks comparability across different years and regions [58]. To address this limitation, the Haken model from synergy theory is introduced to measure the synergistic relationship between economic resilience and ecological resilience.

The Haken model is an important model in synergy theory used to measure the order degree of a system. It employs mathematical models to identify order parameters and evaluate the evolutionary stage of the system. By viewing economic resilience and ecological resilience as an open and symbiotic system, traditional coupling models often assume that the two systems are in equilibrium or assign weights to the systems through empirical methods. In reality, the states of the two systems are difficult to balance and are often fuzzy. The model first assumes the existence of two subsystems within the system: fast and slow variables. It then uses the adiabatic approximation principle to eliminate the fast variables of the system and calculates the order parameter equation and the system’s evolution equations [9,56,58,59]. Finally, it analyzes the collaborative evolution process of the complex system.

(1) Hypothesis: The Haken model assumes that there are two subsystems ($q_1$ and $q_2$) in the system, where $q_1$ is the driving subsystem that evolves the system (slow variable), and $q_2$ is the servo subsystem (fast variable). The slow variable governs the fast variable, controlling the direction of the entire system’s evolution. In this study, $q_1$ and $q_2$ represent the state variables of economic resilience and ecological resilience, respectively. It is assumed that q₁ is the order parameter, and the synergistic evolution equations for $q_1$ and $q_2$ are as follows:

$$\dot{q_1}=-{\gamma }_1{q}_1-a{q}_1{q}_2$$

(14)

$$\dot{q_2}=-{\gamma }_2{q}_2+b{q_1}^2$$

(15)

In the equation, $\dot{q_1}\mathrm{and} \dot{q_2}$ represent the time derivatives of the state variables. $a \mathrm{a}\mathrm{n}\mathrm{d} b$ denote the strength coefficients of the mutual interaction between the two systems, with larger values of $\left|a\right|$ and $\left|b\right|$ indicating a more significant interaction. ${\gamma }_1 \mathrm{a}\mathrm{n}\mathrm{d} {\gamma }_2$ are the damping coefficients.

(2) Solving the evolution equations: According to whether the equations satisfy the “adiabatic approximation condition”, determine if $q_1$ can be considered as the order parameter. The “adiabatic approximation condition” is $\left|{\gamma }_1\right|\gg \left|{\gamma }_2\right|$, and γ₂ > 0. If this condition is satisfied, then $q_1$ can be treated as the order parameter; otherwise, return to the first step to determine the system’s order parameter.

(3) The evolution equation of the order parameter and potential function: If the adiabatic approximation condition is satisfied at this point, $q_2$ is instantaneously removed, and the order parameter $q_1$ is assumed to change too slowly. Setting $\dot{q_2}$ = 0, the evolution equation of the order parameter can be obtained as follows:

$$\dot{q_1}=-{\gamma }_1{q}_1-{\displaystyle \frac{ab}{{\gamma }_2}}{q_1}^3$$

(16)

Integrating the negative of $q_1$, the potential function of the system is obtained as follows:

$$v={\displaystyle \frac{1}{2}}{\gamma }_1{q_1}^2+{\displaystyle \frac{ab}{4{\gamma }_2}}{q_1}^4$$

(17)

(4) Solving for the equilibrium point of the potential function and the system score: The equilibrium points of Africa’s economic and ecological resilience are determined by setting the formula equal to zero. Solving this yields the stable points $\left[q^{\ast },v\left(q\right)\right]$. The evaluation function for the distance between the system’s state variables and the stable points is as follows, where the smaller the value of $d$, the higher the degree of synergy.

$$d=\sqrt{{\left(q-q^{\ast }\right)}^2+\left[v\left(q\right)-v\left(q^{\ast }\right)\right]}$$

(18)

3. Results

3.1. Temporal and Spatial Characteristics of Economic Resilience

To reflect the temporal and spatial distribution of economic resilience in Africa, a kernel density model was used to measure the economic resilience of 52 African countries (regions) (Figure 2). The kernel density curve shows that the economic resilience index of Africa predominantly falls within the 0–0.1 range, indicating an overall mediocre economic resilience. The peak of the curve demonstrates a movement from left to right, suggesting an initial decline followed by an increase in economic resilience. In terms of distribution pattern, the peak height shows a clear downward trend, while the width of the main peak remains relatively constant, indicating an expanding regional disparity in economic resilience across Africa. Regarding polarization, the economic resilience distribution in Africa in 2008 and 2012 was mainly unimodal, while in 2016, a bimodal distribution emerged with “one large and one small peak”. By 2020, the distribution returned to a unimodal pattern, reflecting a fluctuation from “unipolar-bipolar-unipolar”. From 2008 to 2020, the kernel density curve of Africa’s economic resilience exhibits a more pronounced “right tail”, corresponding with the decline of the main peak and signaling an increasing regional disparity in economic resilience and a higher probability of multipolar differentiation.

To intuitively reflect the spatial and temporal distribution pattern of economic resilience in Africa from a micro perspective, this study downscales and redistributes the economic resilience index measured at the national level using regional geographical grid population data as weights. This process generates a regional grid-scale map of economic resilience across Africa (Figure 3). Using the natural break method, the economic resilience is divided into five levels: low-level (0.000–0.001), lower-level (0.001–0.004), medium-level (0.004–0.008), higher-level (0.008–0.020), and high-level (0.020–1.000).

From a spatial distribution perspective, Africa’s economic resilience exhibits a distribution pattern of “broad low-value areas and concentrated high-value areas”. A total of 68.99% of Africa’s territory falls within the low-level economic resilience range. The high-level range accounts for only 2.20% of the area, with these regions primarily concentrated in seven areas: the Nile River Basin, the northern foothills of the Atlas Mountains, the coastal region of the Gulf of Guinea, the Bié Plateau, the southeastern part of the Republic of South Africa, the area surrounding Lake Victoria, and the Ethiopian Highlands.

From a temporal perspective, between 2008 and 2020, the low-level range area experienced fluctuations with a “expansion-contraction-expansion” pattern, maintaining a 3% variation. In 2012, the area expanded by 3.23% compared to 2008, but it then contracted by 3.01% in 2016 before expanding again by 2.31% in 2020. The high-level range area saw a significant contraction of 19.39% in 2012, followed by a turning point, with expansions of 17.74% in 2016 and 6.7% in 2020. By 2020, both low-level and high-level areas saw expansion, intensifying the trend of dual polarization.

At the same time, there were changes in the distribution of high-level areas. In 2008, the Bié Plateau exhibited a significant high-level area, but by 2020, high-level areas in this region were nearly nonexistent, indicating a weakening of economic resilience. Conversely, the Ethiopian Highlands exhibited the opposite trend, with a marked improvement in economic resilience.

3.2. Temporal and Spatial Characteristics of Ecological Resilience

To reflect the temporal and spatial distribution of ecological resilience in Africa on a macro scale, a kernel density model is used to measure the ecological resilience of 54 African countries/regions (Figure 4). The kernel density map reveals that the ecological resilience index in Africa is mainly concentrated in the 0–0.2 range. Compared to economic resilience, the ecological resilience kernel density curve exhibits a broader peak, indicating a more pronounced dispersal trend in ecological resilience indices. Like economic resilience, ecological resilience also displays a skewed distribution (left-skewed). The peak height and the position of the peak’s central line of the ecological resilience curve remain relatively stable, suggesting a certain degree of overall stability in its distribution. In terms of polarization, the distribution from 2008 to 2020 predominantly exhibits a single-peak pattern with a noticeable right-tail phenomenon, even showing signs of a long tail, indicating a trend toward increasing polarization in ecological resilience.

To visually reflect the temporal and spatial distribution pattern of ecological resilience in Africa from a micro perspective, this study divides the ecological resilience of Africa at the regional geographic grid scale into five levels using the natural break method (Figure 5): low-level (0.000–0.130), lower-level (0.130–0.300), medium-level (0.300–0.450), higher-level (0.450–0.600), and high-level (0.600–1.000).

From a spatial distribution perspective, Africa’s ecological resilience exhibits a “concentration of low values and a dispersion of high values” distribution pattern. The areas of the five levels are relatively balanced, with the proportions of low-level, lower-level, moderate-level, higher-level, and high-level regions accounting for 29.07%, 10.37%, 17.15%, 27.63%, and 15.78%, respectively. The low-level range is predominantly concentrated in the Sahara Desert, with only scattered distributions in sub-Saharan Africa. High-level areas are widely distributed in the Nile River Basin, the Mediterranean coastal regions, and sub-Saharan Africa.

From a temporal perspective, between 2008 and 2020, the area of the high-level range exhibited some fluctuations, while the area of the low-level range clearly contracted, reflecting an overall improvement in ecological resilience. From 2008 to 2012, the proportion of the high-level range increased significantly, rising from 12.74% in 2008 to 18.88% in 2012. However, it dropped to 15.07% in 2016 before rising again to 16.44% in 2020. The area of the low-level range contracted by 1.58% in 2012 compared to 2008, decreased by 0.27% in 2016, and contracted again by 2.29% in 2020 compared to 2016.

3.3. Construction of the “Economy-Ecology” Resilience Zones

To quantitatively analyze the spatiotemporal matching of economic resilience and ecological resilience in Africa, the economic and ecological resilience indices of 52 African countries (or regions) were standardized using the range method. The four-quadrant analysis method was then applied, with the mean value as the axis, to establish the “Economic-Ecological” resilience partition diagram (Figure 6). Based on the quadrants, the 52 countries (or regions) were divided into four categories: high economic resilience and high ecological resilience (I), low economic resilience and high ecological resilience (II), low economic resilience and low ecological resilience (III), and high economic resilience and low ecological resilience (IV). The results show that, on average, 59.61% of African countries fall into the low economic resilience–low ecological resilience (III) category, facing a vicious cycle of economic poverty and ecological vulnerability. On average, 15.38% of the countries fall into the high economic resilience–high ecological resilience (I) category, mainly including South Africa, Angola, Ethiopia, Nigeria, and Tanzania. Countries in the high economic resilience–low ecological resilience (IV) category account for only 5.76%, mainly concentrated in North Africa, such as Egypt, Libya, and Morocco. Approximately 19.25% of the countries are classified in the low economic resilience–high ecological resilience (II) category, with representatives such as Madagascar, Botswana, and Mali. From the temporal perspective, the quadrant positions of the 52 countries (or regions) have not changed significantly. However, some countries near the mean value have experienced shifts in their classifications. For example, Kenya, initially in the low economic resilience–high ecological resilience (II) category in 2008, gradually started moving to the high economic resilience–high ecological resilience (I) category in 2016. Similarly, Algeria and Morocco, which were near the ecological resilience mean in 2008, shifted in 2016: Algeria moved to the high economic resilience–high ecological resilience (I) category, while Morocco entered the high economic resilience–low ecological resilience (IV) category.

The 76,209 regional geographic grids in Africa were standardized using range normalization and partitioned to obtain the spatial distribution of the “economic-ecological” resilience zones for Africa from 2008 to 2020 (Figure 7). In terms of spatial distribution, most regions in Africa fall within the low economic resilience range. Specifically, the areas in the low economic resilience–high ecological resilience zone and the low economic resilience–low ecological resilience zone have similar average proportions, 41.36% and 41.14%, respectively, reflecting that weak economic resilience and poor economic stability are the primary challenges for development in Africa. The low economic resilience–low ecological resilience grids are mainly concentrated near the Sahara Desert, while the low economic resilience–high ecological resilience grids are widely distributed in sub-Saharan Africa. The high economic resilience–low ecological resilience zone occupies only 2.33% of the area, indicating that economically developed yet ecologically fragile regions are not dominant in Africa; their distribution is more scattered and often adjacent to high economic resilience–high ecological resilience zones. A relatively concentrated area of this zone is found in the northeastern part of Nigeria near the Chad Basin. The high economic resilience–high ecological resilience zone occupies an average of about 13.89% of the area and is distributed in several regions, including the Nile River Basin, the northern foothills of the Atlas Mountains, the coastline of the Gulf of Guinea, the Bié Plateau, the southeastern part of the Republic of South Africa, the area around Lake Victoria, and the Ethiopian Highlands. This distribution demonstrates a relatively good spatiotemporal match between economic and ecological resilience.

From the time series analysis, the areas of the four main zones fluctuate but do not show significant changes. Notably, the high economic resilience–high ecological resilience zone has seen a marked reduction, specifically a decrease of 2.47% in 2012 compared to 2008, a further decrease of 1.84% in 2016, and another slight decline of 0.04% in 2020. While there were fluctuations, the area of the low economic resilience–low ecological resilience zone did not expand significantly. The areas of the low economic resilience–high ecological resilience zone and the high economic resilience–low ecological resilience zone exhibited inverse fluctuations, with mutual gains and losses. In 2012, the area of the low economic resilience–high ecological resilience zone expanded by 5.47% compared to 2008, while the area of the high economic resilience–low ecological resilience zone shrank by 3.49%. In 2016, the area of the high economic resilience–low ecological resilience zone expanded by 12.41% compared to 2015, while the area of the low economic resilience–high ecological resilience zone shrank by 3.06%. In 2020, the area of the low economic resilience–high ecological resilience zone showed a small expansion of 0.7%, while the area of the high economic resilience–low ecological resilience zone contracted by 4.95%.

3.4. Coupling Coordination Degree Analysis of Economic Resilience and Ecological Resilience

Using Formulas (9)–(11), the coupling coordination degree of the 52 African countries and the African regional geographic grids are calculated from both macro and micro perspectives.

From a macro perspective, from 2008 to 2020, the coupling coordination degree between economic resilience and ecological resilience in most African countries was below 0.5, falling into the disordered category (Figure 8). Among these, more than half of the countries were in the 0.1–0.4 range, which corresponds to the severe disorder, moderate disorder, and mild disorder categories. The average proportion of severely disordered countries and mildly disordered countries were the same, both reaching 21.15%, with Equatorial Guinea and Guinea-Bissau, as well as the Central African Republic and Zimbabwe, as representative regions. The average proportion of moderately disordered countries was 19.23%, with Benin, Guinea, and Tunisia as representatives. Countries with a coupling coordination degree below 0.1 (extremely disordered) and those in the 0.4–0.5 range (near-disordered) accounted for 11.54% and 5.77%, respectively, with Mauritius and Madagascar as representatives. Only 21.15% of the countries had a coupling coordination degree above 0.5, entering the coordinated category. Among these, the proportion of barely coordinated countries was the largest, around 9.62%, with Egypt and Morocco as representatives. Countries in the primary coordination range accounted for 7.69%, with Algeria and Ethiopia as representatives. Very few countries reached the intermediate or good coordination levels, with Nigeria and South Africa being examples. From the temporal trajectory, most countries showed little changes in their coupling coordination degree, and none broke through the interval limits. A few countries exhibited “upgrading” or “downgrading” phenomena, such as Sudan, which dropped from the coordination range to the disordered range in 2020, and the Central African Republic shifted from moderate disorder to mild disorder starting in 2012.

From a micro perspective, the vast majority of regions in Africa fall into the disordered category (Figure 9). Among these, the area in the extremely disordered range occupies the largest average proportion, around 45.27%, with the Sahara Desert region being the most concentrated. Distributions are also scattered in areas south of the Sahara, such as the Namib Desert and the Kalahari Desert along the Atlantic coast in the southwestern part of Africa. The area in the severely disordered range follows closely, with an average proportion of 40.05%, and it is widely distributed in West Africa, south of the Sahara Desert, as well as around the Ethiopian Highlands in East Africa. The areas in the coordinated category occupy a relatively small proportion of Africa, being quite rare. These regions are primarily distributed along the Nile River Basin, the northern foothills of the Atlas Mountains, the Guinea Gulf coast, the southeastern part of South Africa, around Lake Victoria, and in the Ethiopian Plateau. The areas with good coordination are mainly located in the North African city of Cairo and its surrounding regions, as well as the Johannesburg–Pretoria line in South Africa.

From the temporal trajectory, the overall coupling coordination degree in Africa shows a trend of polarization. The area in the extremely disordered range expanded from 12.9653 million square kilometers in 2008 to 14.1676 million square kilometers in 2020. On the other hand, the area in the good coupling range did not appear across Africa in 2008, but by 2020, it expanded to 3955.99 square kilometers. The areas in the near-disordered and barely coordinated ranges also expanded by 20.18% and 45.17%, respectively, from 2008 to 2020. However, after 2016, many regions in the moderate coordination range merged into the good coordination range, resulting in a zero area for the moderate coordination range, creating a gap in the intervals. The regional polarization of coupling coordination degrees became increasingly pronounced.

3.5. Analysis of the Synergistic Relationship Between Economic Resilience and Ecological Resilience

3.5.1. Verification of the Haken Model

The Haken model was originally developed to address the issue of synergetic relationships in the field of physics. Its direct application to the socio-economic domain, without considering the discrete nature of economic data, may lead to model bias. To scientifically simulate the co-evolutionary relationship between economic resilience and ecological resilience, this study references the approach of Sun Caizhi, Wang Songmao, and others to improve the Haken model [9,59]. The co-evolutionary equations of economic resilience and ecological resilience are modified as follows:

$$\ln q_1\left(t\right)=\left(1-{\gamma }_1\right)\ln q_1\left(t-1\right)-a{\ln q}_1\left(t-1\right)\ln q_2\left(t-1\right)$$

(19)

$$\ln q_2\left(t\right)=\left(1-{\gamma }_2\right)\ln q_2\left(t-1\right)+b\ln {q_1}^2\left(t-1\right)$$

(20)

Taking the economic resilience (ENR) and ecological resilience (EGR) of African countries as the order parameters, the model assumptions are proposed, and the equations of motion are solved using Eviews13. The results are presented in

Table 4. Construction of the synergy model for economic resilience and ecological resilience in Africa.

Model Setup	Motion Equation	Parameter Information	Conclusion
$q_1$ = ENR	$\begin{array}{cc}\mathit{ln}{q}_1\left(t\right)=\hfill & {0.931992}^{\ast \ast }\mathit{ln}{q}_1\left(t-1\right)\hfill \\ & -0.011816{\mathit{ln}q}_1\left(t-1\right)\mathit{ln}{q}_2\left(t-1\right)\hfill \end{array}$	${\gamma }_1$= 0.068008, $a$= 0.011816	The motion equation is invalid and does not satisfy the adiabatic approximation assumption, and the model setup is not valid.
$q_2$ = EGR	$\begin{array}{cc}\ln q_2\left(t\right)=& {0.990806}^{\ast \ast \ast }\ln q_2\left(t-1\right)\hfill \\ & -0.000836\ln {q_1}^2\left(t-1\right)\hfill \end{array}$	${\gamma }_2$= 0.009194, b = −0.000836
$q_1$ = EGR	$\begin{array}{cc}\mathit{ln}{q}_1\left(t\right)=\hfill & {0.985233}^{\ast \ast \ast }\mathit{ln}{q}_1\left(t-1\right)\hfill \\ & -{0.002255}^{\ast }{\mathit{ln}q}_1\left(t-1\right)\mathit{ln}{q}_2\left(t-1\right)\hfill \end{array}$	${\gamma }_1$= 0.014767, $a$= 0.002255	The motion equation is valid and satisfies the adiabatic approximation assumption, and the model setup is valid. EGR is the order parameter.
$q_2$ = ENR	$\begin{array}{cc}\ln q_2\left(t\right)=\hfill & {0.907813}^{\ast \ast \ast }\ln q_2\left(t-1\right)\hfill \\ & -{0.016386}^{\ast \ast \ast }\ln {q_1}^2\left(t-1\right)\hfill \end{array}$	${\gamma }_2$= 0.092187, b = −0.016386

Note: *, **, *** indicate significance at the 10%, 5%, and 1% levels, respectively.

. The two hypotheses, based on ENR and EGR as order parameters, are evaluated to determine whether the “adiabatic approximation assumption” is satisfied. Comparing the two model setups in Table 4, it is evident that the assumption with ecological resilience as the order parameter satisfies the “adiabatic approximation assumption”. The parameters are as follows: ${\gamma }_1$ = 0.014767, $a$ = 0.002255, ${\gamma }_2$ = 0.092187, and b = −0.016386 The evolution equation is expressed as

$$\dot{q_1}=-0.014767q_1+0.00040082{q_1}^3$$

(21)

The potential function is

$$v=0.0073835{q_1}^2-0.000000851{q_1}^4$$

(22)

For $\dot{q_1}$ = 0, we obtain three solutions for the potential function: $q^1$ = 0; $q^2$ = 6.0681; and $q^3$ = −6.0681. Since both economic resilience and ecological resilience are positive values, we select $q>0$ and take the positive solution 6.0681. The stable point is then obtained as U (6.0681, 0.2707). The distance between any arbitrary state parameter point A and the stable point reflects the system’s state, also known as the coordination index d:

$$d=\sqrt{{\left(q-6.0681\right)}^2+{\left[v\left(q\right)-0.2707\right]}^2}$$

(23)

The synergy value is used to represent the state of the co-evolution between economic resilience and ecological resilience. The larger the d value, the more significantly the system deviates from equilibrium, indicating a lower degree of synergy. Therefore, a negative normalization process is applied. To prevent boundary issues, the maximum value of the original data is increased by 10%, and the minimum value is reduced by 10%. This results in the synergy values of economic resilience and ecological resilience for the African countries (regions).

3.5.2. Synergy Analysis

According to the results of the Haken model validation, it is evident that during the co-evolution of economic resilience and ecological resilience in Africa, the current dominant parameter driving the system’s evolution is ecological resilience. Economic resilience, on the other hand, functions as a dependent and auxiliary variable, reflecting the system’s evolutionary behavior as a control parameter. This suggests that solely pursuing economic resilience while neglecting ecological resilience to maximize the system’s synergistic effect is not feasible.

The values $a$ = 0.002255 > 0 and b = −0.016386 < 0 indicate that, at present, the protection of ecological resilience inhibits the enhancement of economic resilience, while improving economic resilience would also hinder the development of ecological resilience. This is consistent with the current situation in Africa, where the economic development model heavily relies on natural resources, especially the extraction of mineral resources. Traditional mining and agriculture remain the pillar industries of most African countries. Under this model, an increase in economic resilience inevitably leads to environmental degradation and a weakening of ecological resilience. Conversely, protecting ecological resilience would require a decline in economic output and the retreat of industries, highlighting the stark conflict between economic growth and environmental preservation.

The parameters ${\gamma }_1$ = 0.014767 > 0 and ${\gamma }_2$ = 0.092187 > 0 indicate that both economic resilience and ecological resilience have established positive feedback mechanisms that contribute to improving the overall system’s orderliness. This suggests that balancing the development of both economic resilience and ecological resilience plays a positive role in maximizing the synergistic effect of the system. The smooth and coordinated development of these two aspects is crucial for achieving sustainable and harmonious development across the African continent.

From the spatial distribution of synergy values, the South African region has the highest average synergy value, reaching 0.247, the highest among the five major African regions, followed by the Central African region with an average of 0.218. The West African region ranks the lowest, with an average synergy value of only 0.121. There is also a clear imbalance in the distribution of synergy values within the five major regions. In North Africa, Sudan stands out with a synergy value of 0.568, exceeding the regional average by 189.2%. In East Africa, Ethiopia and Tanzania are notable, with synergy values averaging 0.488 and 0.501, respectively. In the South African region, South Africa has the highest synergy value, averaging 0.601, making it the highest in Africa. In West Africa, Nigeria, and in Central Africa, Angola, both countries have the highest synergy values in their respective regions, with averages of 0.417 and 0.518 (Figure 10).

To systematically analyze the temporal distribution of synergy values, the kernel density model was used to measure the synergy values of the 52 African countries (regions). The kernel density curve shows that from 2008 to 2020, the peak of the curve shifted notably to the right, indicating an overall increase in the synergy between economic resilience and ecological resilience over time. In terms of distribution, the height of the peak decreased annually, and the width of the main peak narrowed, suggesting that the regional differences in synergy values have widened, while the internal differences within each region have contracted. Regarding polarization, the synergy values from 2008 to 2020 exhibited a clear unimodal shape with a pronounced right tail, highlighting the growing spatial differentiation of synergy values (Figure 11).

4. Discussion

4.1. Analysis of the Spatial-Temporal Evolution of Economic Resilience and Ecological Resilience

This study reveals a clear spatial imbalance in the distribution of economic resilience across Africa, with our findings aligning to some extent with those of Nerhum Sandambi in his research on African economies [60]. The spatial distribution of economic resilience results from the interaction of natural resource factors and regional economic elements. The seven major high-value clusters exhibit relatively high economic resilience, yet the driving forces behind them are complex and diversified. For example, the high-value economic resilience cluster in the Nile River Basin, centered around Cairo, Egypt, benefits from two key factors: firstly, the abundant freshwater resources from the Nile address agricultural irrigation issues, and secondly, it is strategically located at the crossroads of Africa, Asia, and Europe, near the Suez Canal. This enables Egypt to engage in European industrial division while leveraging its robust industrial system to radiate industries to the Arab Peninsula, resulting in significant economic resilience [61].

Similarly, the northern foothills of the Atlas Mountains, including port cities like Algiers, Tunis, and Casablanca, form another high-value economic resilience cluster, characterized by a significant dependence on the European economy. For instance, Casablanca, Morocco’s largest city, is a key European tourist destination. Historically a French colony, Morocco has capitalized on its rich oil and natural gas resources to trade energy with its former colonizer while simultaneously accepting European industrial relocation, leading to a relatively complete industrial system and robust economic resilience [62]. The rise in economic resilience along the Gulf of Guinea coast is driven by the energy industry, particularly the oil sector, which has spurred the development of port transportation and oil industries [63]. Nigeria, Africa’s largest oil producer and an OPEC member, generates nearly 90% of its national revenue from oil [64,65,66].

The Bié Plateau, centered around Luanda, represents another high-value economic resilience cluster. It benefits from fertile land, favorable agricultural and climatic conditions, abundant fisheries, and a self-contained oil and diamond extraction industry [66]. The southeastern region of South Africa, with inland cities like Johannesburg and Pretoria, as well as port cities such as Durban, Maputo, and Mandela City, also forms a high-value cluster of economic resilience, as it is one of the most developed areas in Africa. Rich in mineral resources and with a comprehensive industrial system spanning mining, manufacturing, agriculture, and services, this region’s economic resilience is supported by both its natural resources and its well-established industrial base [67].

The region around Lake Victoria, with agricultural dominance, forms a significant population cluster, benefiting from the lake’s abundant fishery resources while solving irrigation issues for surrounding cities. Key cities like Nairobi and Kigali stand out, with Kigali being a major hub for coffee, livestock, leather, and grain, where nearly 90% of the population is engaged in agriculture [68]. The Ethiopian Highlands, with Addis Ababa at its core, exemplify a high-value cluster driven by industrial transition from agriculture to manufacturing. Ethiopia, once an agrarian economy with a weak industrial base, has successfully implemented a strategy combining mineral resources and modern agriculture to promote industrialization; this has led to a steady increase in economic resilience [69].

The temporal evolution of economic resilience is closely related to economic cycles and the “Matthew effect” generated by regional economic elements. Between 2008 and 2020, African economic resilience exhibited a “polarized” temporal evolution that was closely linked to the post-crisis recovery of the U.S. and European economies; this led to a differentiated recovery pattern in Africa. After 2009, the U.S. economy showed strong signs of recovery, spreading to Japan and Europe, while industrialized African countries such as Egypt, Algeria, Nigeria, and South Africa, which had long participated in European industrial division, enhanced exports and showed noticeable economic recovery [70]. For example, Algeria’s per capita GDP rose by 16.33% in 2011 compared to 2009, marking a significant economic rebound [65]. The improved economic resilience generated positive market expectations, further attracting investment and creating a “Matthew effect”. In contrast, some West and Central African regions, with their single industrial structure and inadequate transportation infrastructure, showed weaker recovery and a widening gap in economic resilience compared to the seven major high-value clusters.

The spatial and temporal evolution of ecological resilience is more influenced by climatic and topographical conditions. The low ecological resilience areas are predominantly concentrated in the Sahara Desert, where the sub-tropical high-pressure system prevails year-round, coupled with northeast trade winds, creating a dry, low-precipitation climate. This region, one of the driest in the world, is characterized by extremely low organic matter in the soil and scarce flora and fauna [71]. Notably, the Namib Desert along the southern coast of Africa is also an ecological low-resilience area. Affected by the Benguela cold current, this strip of desert remains arid despite its proximity to the Atlantic Ocean. The Atlantic’s moisture is significantly diminished by the cold current, creating a coastal dry belt [72]. Harsh climatic and topographical conditions, coupled with an unsuitable combination of water and temperature for biological growth, directly lead to a lack of biodiversity, causing ecological systems to become structurally simple and functionally degraded, thereby exhibiting continuously declining ecological resilience.

4.2. Analysis of the Spatial-Temporal Coupling and Synergistic Evolution of Economic Resilience and Ecological Resilience in Africa

The study finds that the spatial-temporal coupling relationship between economic and ecological resilience in most African countries and regions is imbalanced, which is consistent with the findings of Guoen Wei [73] and others to some extent. In the “economic-ecological” resilience zones of Africa, 78.86% of the countries and 82.50% of the regional geographical grids fall into the low economic resilience half-zone. This indicates that, unlike China, the main cause of imbalance in African regions is the lack of economic resilience. In other words, weak economic development, poor economic stability, and widespread extreme poverty are the primary reasons for the economic and ecological dissonance in Africa.

From the perspective of spatial-temporal coupling, it might seem that focusing solely on solving economic resilience issues could address the imbalance between economic and ecological resilience. However, the analysis of their synergistic evolutionary relationship effectively corrects this development path. The synergistic evolution analysis reveals that the development of economic resilience and the protection of ecological resilience exhibit an antagonistic and obstructive relationship, highlighting a stark conflict between economy and environment. Pursuing economic benefits through the current development model will inevitably lead to ecological degradation. This is in line with Africa’s current industrial structure, which is heavily reliant on labor-intensive and mineral resource-intensive industries [73].

Further analysis shows that both economic resilience and ecological resilience have established positive feedbacks that promote the overall system’s orderliness. This indicates that focusing solely on economic benefits while neglecting ecological considerations not only damages ecological resilience but also fails to achieve the optimal outcome for the economic-ecological system as a whole. Shifting the development model to balance the development of both economic and ecological resilience is the essential path for maximizing synergistic effects and achieving sustainable development in Africa.

4.3. Shortcomings and Future Prospect

This study still has some limitations. Firstly, this study only reveals the current status of the spatial-temporal coupling and coordinated evolution of economic resilience and ecological resilience in Africa from 2008 to 2020, highlighting the existing issues. However, it does not fully uncover the underlying driving mechanisms and optimization pathways, which are areas for further research. Secondly, the assessment of economic resilience relies on data from statistical yearbooks. Given the varying levels of social, economic, and administrative development across African countries, statistical yearbooks cannot fully reflect all the issues related to economic resilience in Africa. The economic logic behind economic resilience deserves further exploration. Lastly, sustainable development requires a balance among the environmental, economic, and social dimensions. This study only discusses the economic and environmental dimensions, while the social dimension is lacking. Additionally, economic sustainability and environmental sustainability can be scientifically distinguished, and further research is needed in this regard.

5. Conclusions

This study focuses on Africa, constructing economic resilience and ecological resilience evaluation models. It investigates the spatiotemporal matching, spatiotemporal coupling, and synergistic relationships of economic resilience and ecological resilience in Africa from both national and regional geographical grid scales for the period 2008–2020. The key findings are as follows:

(1)

The overall economic resilience of Africa is relatively low, with increasing regional disparities. Spatially, economic resilience exhibits a “low value widespread, high value clustered” pattern, with high values concentrated in seven key regions: the Nile River Basin, the northern foothills of the Atlas Mountains, the Guinea Gulf coast, the Bié Plateau, southeastern South Africa, the Victoria Lake area, and the Ethiopian Highlands. Temporally, both low and high economic resilience areas have expanded, exacerbating the trend of polarization;

(2)

The level of ecological resilience in Africa shows a more pronounced spatial and temporal variability than economic resilience. Spatially, ecological resilience follows a “low value concentrated, high value dispersed” pattern, with low values concentrated in the Sahara Desert and its surrounding areas, and it is only sporadically distributed in sub-Saharan Africa. Over time, although the area of high ecological resilience fluctuated, its growth was not significant; the area of low ecological resilience consistently shrank, leading to an overall improvement in ecological resilience year by year;

(3)

By combining the “economic-ecological” resilience zones, the 52 African countries (regions) were divided into four categories: high economic resilience–high ecological resilience (I), low economic resilience–high ecological resilience (II), low economic resilience–low ecological resilience (III), and high economic resilience–low ecological resilience (IV). On average, 59.61% of African countries fall into the low economic resilience–low ecological resilience (III) category, while 15.38% are in the high economic resilience–high ecological resilience (I) zone, mainly including South Africa, Angola, Ethiopia, Nigeria, and Tanzania. When the “economic-ecological” resilience zones were further analyzed at the regional geographic grid level, it was found that most regions in Africa fall into the low economic resilience category. The weak economic resilience and poor economic stability are the primary contradictions in development. The high economic resilience–high ecological resilience areas largely overlap with the seven regions of high economic resilience, indicating good spatiotemporal matching between economic resilience and ecological resilience;

(4)

In terms of the spatiotemporal coupling relationship between economic resilience and ecological resilience in Africa, the majority of regions, both from macro and micro perspectives, fall into the disordered category. South Africa and Nigeria demonstrate better coupling, occupying the good coordination and intermediate coordination zones, respectively. At the micro level, well-coordinated areas are mainly found in Cairo and its surrounding areas in North Africa, as well as along the Johannesburg–Pretoria line in South Africa. From 2008 to 2020, the areas of both good coordination and extreme disorder expanded, with a noticeable polarization in coupling coordination across regions;

(5)

In terms of the synergistic relationship between economic resilience and ecological resilience, ecological resilience, as the leading parameter, governs the symbiotic system formed by economic and ecological resilience. Currently, there is a certain degree of mutual inhibition between the development of ecological resilience and economic resilience, and the conflict between the economy and the environment is highly prominent. The synergistic system of ecological resilience and economic resilience has formed an internal positive feedback mechanism. In terms of spatial distribution, South Africa has the highest synergy value, followed by regional powers such as Nigeria, Angola, Sudan, Ethiopia, and Tanzania, all of which exhibit high levels of synergy. The regional differences in synergy values have widened, while the intra-regional differences have narrowed. However, the spatial differentiation trend remains evident.