Trade-Offs and Synergies among 17 Ecosystem Services in Africa: A Long-Term Multi-National Analysis

Abstract

The proper management of multiple ecosystem services (ESs) in a balanced manner is an important and challenging responsibility. However, due to infrastructural constraints, we need to understand more about the spatial interactions among ESs in most African countries. Therefore, we took 48 African countries, 5 African geopolitical regions, and the African continent as case studies to diagnose the spatial trade-offs and synergies among 17 ESs and 8 types of land use and land cover (LULC) in 2000 and 2019. The implications of our findings at the national, regional, continental, and global levels were explored. To achieve this, we mapped the spatial distributions of the 17 ESs at the continental level using classified land cover data from MODIS remotely sensed data, with a spectral band between 0.405 and 14.385 µm and a spatial resolution of 500 m. Then, we used Spearman’s rank correlation coefficient to determine the spatial interactions among the 17 ESs. The results show that regulation services showed synergies at the continental level in gas regulation (0.66), climate regulation (0.71), disturbance regulation (0.14), water regulation (0.53), water supply (0.71), and waste treatment (0.06). Moreover, we found moderate levels of interactions among most ESs in the 48 countries, with most regulating services and supporting services exhibiting trade-offs with other categories of ESs, among other findings. The results will inform scientific communities and authorities at all levels on how to deliver human well-being and quality of life, and usher in a sustainable change where we expect better ecosystem management and ecological conservation.

1. Introduction

The concept of natural or ecosystem services [1] was originally developed to draw attention to the benefits that ecosystems bring to society and to raise awareness of biodiversity conservation. Since the definition of ecosystem services depends on ecological functions, revealing their value should theoretically attract managers and policymakers to protect these functions. In early attempts, the monetary value of 17 ESs was estimated to be from US$16 to US$54 trillion per year, sparking a boom in research on how to assess the value of ecosystem services. An ecosystem service is any positive benefit that wildlife or ecosystems provide to humans. Benefits can be direct or indirect and small or large [2].

Ecosystem services are the benefits provided to humans through the conversion of resources (or environmental assets, including land, water, vegetation, and atmosphere) into essential streams of goods and services, such as clean air, water, and food [3]. Human survival and well-being are highly dependent on ecosystems, with various forms of ecosystem services (ESs) provided by nature [4,5]. Human dependence on ESs is evident in increasingly rapid economic development [6,7] and is manifested in Brazil, Russia, India, China, and South Africa (BRICS). Important aspects of ESs include the following: agriculture provides habitat for wild species and creates aesthetic landscapes, animal excrement can serve as an important source of nutrients, seed dispersal can maintain soil fertility in grazing grasslands, and sustainable and integrated aquaculture can enhance the flood protection of mangroves, which are well known to help understand and quantify what occurs according to refs [5,8,9]. However, ESs (which have impacts on ecosystem management) have rarely been attempted, especially on a continental scale [5,8,9]. Ref [5,10,11] have noted that as an evolving area of research, ESs help to provide an inherent way to understand the synergies and trade-offs between humans and their natural environment [5,10,11]. In addition, studying the interactions and relationships among ESs can contribute to the optimal management of natural resources [5,12].

1.1. Trade-Offs and Synergistic Relationships among ESs

Trade-offs in ESs occur when the provision of a service is improved at the expense of reducing the provision of another or more services [6,7,10,13]. On the other hand, synergies arise when many ESs are improved at the same time. Positive (synergistic) and negative (trade-off) relationships among ESs are influenced by drivers of change, such as policy interventions and environmental variability, and mechanisms for linking these drivers to ecosystem service outcomes. These relationships arise in response to exogenous or endogenous changes to the system and are called drivers [14]. Drivers may be related to human interventions and natural variability, including policy tools, climate change, and technological advances. Failure to consider these drivers and mechanisms can lead to poor management decisions and reduced-provision ESs.

1.2. Mapping and Assessing the Spatial Distribution of ESs

Researchers have developed several interdisciplinary methods for mapping and assessing the spatial distribution of ESs and determining the interactions and relationships among them. These models include the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) [15,16,17] (a biophysical simulation model), as well as remote sensing analysis, statistics, and geographic information systems [5,18,19]. These models and methods have become an important tool over the past few decades as a powerful tool for improving the efficiency and effectiveness of conservation ecological restoration programs (conservation plans aimed at intentionally renewing and restoring degraded, damaged, or destroyed ecosystems, a new dimension of conservation practice, and managing natural goods and services [5,18]. For example, in the application of ES trade-offs and synergies and collaborative analysis, ref [20] proposed that the key management strategy of riparian meadows improved the regulating and provisioning services of the Miyun Reservoir in Beijing [20]. There is no doubt that such analyses can provide important guidance on prioritizing potential conservation and restoration projects that are often resource-limited [5,12,18].

Over the past few decades, although more and more studies have been conducted, they have been unequal around the world, especially in Africa and across its countries [21,22] and regions. The research here is inadequate and limited due to weak infrastructure (e.g., internet facilities, power supply, and little or no funding). In addition, the existing studies in Africa have considered few ES subtypes (e.g., ref [23] examined nine ES subtypes in Egypt in 2022) [23], and most studies have been limited in size, space, and time points. Therefore, our understanding of the spatiotemporal interactions among most ecosystems and ESs needs to be modified [5,7,14], especially in Africa, as supported by ref [24].

1.3. Increasing Population and Anthropogenic Impact on ESs

According to the United Nations World Population Prospects 2019, Africa is home to an estimated 1.3 billion people, accounting for 17% of the world’s population, and it is expected to reach about 2.5 billion in 2050, with a growth rate of 1.76% [25]. Currently, Africa is the world’s second most populous continent in the world after Asia, with a population expected to surpass Asia by 2100, with the ref [25] showing that Africa has the highest population growth rate among major regions; it further highlights that more than 50% of global population growth is expected to occur in Africa between now and 2050 [25]. The livelihood and well-being of a population depend on the ESs provided by local ecosystems [5], and this is also true in Africa where the majority of the population lives in rural areas [25] and relies mainly on agriculture (peasant farming) for food production, water, and logs for heating etc., [15,24,26,27,28,29].

In addition, Africa faces serious environmental threats, including desertification, climate change, and human activities [15,30,31]. These threats negatively impact ESs, resulting in one or more service degradations and imbalance among ESs. For example, countries such as South Africa have experienced shortages of water-regulating services and supplies in recent years due to land-use patterns and anthropogenic activities [15]. This is mainly due to climate change, which has led to delayed rainfall and will eventually lower the dam level, leading to droughts within the country [15,28]. Notably, environmental degradation and population growth are some of the factors affecting ESs; they all determine ecosystem trade-offs and synergies [32]. Refs. [4,5,7,33] have demonstrated the complexity of the relationships among ESs [4,5,7,33]. These associations suggest that improving one ecosystem service may lead to changes in other ESs (trade-offs and synergies) [7].

Although Nigeria ref [24], Senegal [34], Zimbabwe [27], South Africa [15], Kenya [28,29], Tanzania [28], Rwanda [28], Burundi [28], and Uganda ref [28] have been the subjects of global reviews and studies, including those by refs [32,35,36,37,38,39], most African countries have not conducted adequate national level assessments, nor have they conducted continental-level assessments.

Our study aims to evaluate the relationships among 17 ESs in 2000 and 2019 on the African continent, in 5 regions, and in each of the 48 African countries (Figure 1) to inform and track progress according to the African Union’s “Agenda 2063” initiatives (a sustainable development initiative [40] and the United Nations Sustainable Development Goals [41] and to enrich the scientific literature. It is worth noting that identifying trade-offs and synergies is important for regional and continental sustainable development. Therefore, this study also aims to demonstrate the economic importance of each ESV and natural resource to the continent. Identifying the trade-offs and synergies among ESs in 2000 and 2019 and the LULC between 2000 and 2019 provides insight into a country’s ecosystem management strategy and ES priorities; These implications for the AU and the UN will not only monitor the performance of Member States in achieving sustainable development and ‘Global Goals’ but will also help to improve sustainable development initiatives. Countries with trade-offs among ESs can explore the ecosystem management strategies and policies of countries with synergies among their 17 ESs to achieve synergies.

Our objectives are to (1) quantify the 17 main ESs; (2) evaluate the spatial relationships among ESs in each of the 48 African countries and the continent as a whole; (3) discuss the implications of our findings at the national and continental levels. Therefore, our study mapped the continental-scale distribution of each of the 17 ESs by constructing spatial biophysical models. First, for correlation analysis, we use Spearman’s rank correlation coefficient to quantify the spatial relationships among ESs [5]. Next, we analyze and present the spatial variations in the 17 ESs for each of the 48 African countries and continents, and finally, we discuss the implications of our findings at the national and continental levels.

2. Materials and Methods

2.1. Study Area

Africa is located at the coordinates 8.7832° South latitude and 34.5085° East longitude. (Figure 1). It is bordered by the southern half of the Mediterranean Sea, the Atlantic Ocean to the west, and the Indian Ocean to the southeast. Africa stretches south of the equator and covers an area of more than 12 million square miles, making it the world’s second-largest continent [42]. It is also the second most populous continent in the world. Africa is one of the most diverse places on earth, with a wide variety of terrains, wildlife, and climates. Currently, there are 54 sovereign states recognized by the United Nations. Ref. [43] confirmed that 66% of the land area has arid and desert conditions [43]. The remaining 44% have conditions favorable for human habitation (area 123,408 km²) and food production (2,292,000 km²) [44,45]. These areas also have a high potential for industrial development (supply of raw materials) and conservation activities [46]. The average annual rainfall ranges from 1500 mm on the coast of West Africa [47] to 100–200 mm in the Northern and Sahel regions [48].

The average annual rainfall in the equatorial region is relatively high, with 400–1600 mm, and the average annual rainfall in some areas exceeds 1600 mm [48]. There is less than 100 mm of rainfall per year in desert areas [48]. The central region is characterized by evergreen tropical forests, such as the Congo Basin in the Democratic Republic of Congo (DRC) and the Kakamega forest in Kenya. These areas are “repositories of biodiversity, timber, medicinal plants, and play a key role in watershed conservation” [49]. The southern region is characterized mainly by jungles, bushlands, woodlands, and savannas. African tropical forests and savannahs contain biodiversity hotspots that Myers et al. recognized and mapped in 2000 [50].

Africa is known for its geographical features, such as the Great Rift Valley and Mount Kilimanjaro, Africa’s highest mountain at 5895 m above sea level [51], Lake Victoria (the second largest freshwater lake in the world), with a total area of 68,800 km² [28], Lake Tanganyika (the second deepest lake in the world), with a depth of 1470 m [52], and unparalleled archaeological evidence of human evolution in Africa [53]. Thirty-nine African countries, including the island nations, are bordered by the ocean. The coastlines of the African continent are a mixture of ecosystems, including estuaries, deltas, barrier islands, lagoons, wetlands, mangroves, and coral reefs [54]. In general, the coastline is relatively straight, the coastal zone is low-lying, the continental shelf is narrow, and there are few large natural harbors [55].

Algeria is the largest country in Africa by land area, and Nigeria is the most populous country on the continent. The African Union (AU) is the broadest cooperation group in Africa. There are also regional groups, among which the Economic Community of West African States (ECOWAS) is one of the most prominent. The continent is also the second most densely populated continent in the world. As mentioned earlier, the United Nations World Population Prospects 2019 states that Africa’s population of 1.3 billion, accounting for 17% of the world’s population, is expected to reach about 2.5 billion in 2050, with a growth rate of 1.76% [25,56].

The LULC types, according to 2000 and 2019 Moderate Resolution Imaging Spectroradiometer (MODIS) remotely sensed data, were downloaded from the NASA portal, with a spectral band between 0.405 and 14.385 µm and a spatial resolution of 500 m × 500 m. The continental land cover data were classified using a hybrid classification algorithm, which combines the benefits of supervised and unsupervised techniques. The LULC data for the years 2000 and 2019 were acquired. Then, the 2000 and 2019 LULC classes were converted into polygons using ArcGIS software 10.7 and dissolved using the African shapefile from the USGS website to obtain area sizes in hectares and by country. The change detection of the study area between 2000 and 2019 was analyzed. The land use/landcover types were grouped into eight classes, including forest (FS), Shrubland (SL), grassland (GL), bare land (BL), cultivated land (CL), wetland (WL), water bodies (WB), and urban and built-up land (UB). With the input of the LULC data, we derived our LULC ESs. With the Costanza ESV index, we analyzed the 17 ESVs and grouped them into 4 ecosystem services (regulating, provisioning, supporting, and cultural). Finally, with Spearman’s Rank Correlation Index, the trade-offs and synergies of both the LULC and ESVs were calculated (see technical flowchart, Figure 2, for more).

2.2. Correlation Analysis

There are several ways to evaluate trade-offs and synergies among different ESs, including (i) a set of correlation analysis methods that distinguish trade-offs from synergies, such as Pearson correlation analysis [6,58,59], (ii) Spearman’s rank correlation coefficient [60,61], and (iii) other statistical methods [62]. These methods are based on correlation coefficients that directly reflect the strength of each pairwise ES and the positive or negative linear or monotonic values that can simply express trade-offs or synergies among ESs [63]. Our study utilized Spearman’s rank correlation coefficients to evaluate the relationships among various ESs [64].

This method can reflect the monotonicity of bivariate correlations. According to ref [3], when ESs are significantly correlated (p < 0.05), they are considered trade-offs (negative correlation) or synergies (positive correlation) [20,65], while an insignificant correlation indicates no interaction or no interactive effect [18].

Spearman’s correlation does not require the distribution of raw variables, it is a widely applicable non-parametric statistical method. Spearman’s correlation is based on individual data. First, the ES data were ranked from the highest to the lowest in 2000, and from the lowest to the highest in 2019, and then we calculated the Spearman correlation coefficients using the following expression:

$$r_{xy}=\frac{{{\displaystyle \sum }}_{i=1}^n{{\displaystyle \sum }}_{i=1}^n\left(x_{\mathrm{ij}}-\overline{x}\right)\left(y_{\mathrm{ij}}-\overline{y}\right)}{\sqrt{{{\displaystyle \sum }}_{i=1}^n{{\displaystyle \sum }}_{j=1}^n}{\left(x_{ij}-\overline{x}\right)}^2\sqrt{{{\displaystyle \sum }}_{i=1}^n{{\displaystyle \sum }}_{j=1}^n}{\left(y_{ij}-\overline{y}\right)}^2}$$

(1)

where r_xy is the correlation coefficient, and the values range is −1 to +1. r_xy > 0 indicates a positive correlation, meaning that the service has synergy; r_xy < 0 indicates a negative correlation, which means that there is a trade-off for the service. x_ij and y_ij represent the data values of different ESs and LULC spatial data.

Correlation aims to quantify the strength of a link that connects two different characteristics. Spearman’s rank is a non-parametric correlation analysis [5]. This method is usually applied to pairs of variables whose prior distribution is unknown. In contrast to Pearson’s, the most used correlation coefficient, Spearman’s does not assume variables that are normally distributed and of the same scale. In addition, due to the length of the time series, Spearman’s analysis is less sensitive to outliers and captures nonlinear relationships among variables. This is why Spearman scales (expressed as coefficients) have been used across multiple disciplines and in previous studies, such as those by Sesnie et al. (2017) and Sidney et al. (2017) [66,67].

Coefficients range between −1 and +1, indicating a negative or positive correlation. If the coefficient is 0, there is no correlation, but this does not mean that they are independent. Assuming that the zero hypothesis H0 (variables are not correlated) proves that correlation is not accidental, the alternative form is H1. Since the purpose of not proving the null hypothesis of correlation is to quantify the strength of the link connecting two different features, the correlation coefficient becomes important. The p-value measures the likelihood that the observed correlation is due to chance: close to zero. Below the significance level (a = 0.05), it is unlikely that the data sample is observed to be correlated (e.g., 95% confidence). Thus, the null hypothesis H0 can be rejected, and the alternative H1 must be accepted.

Spearman r values greater than or equal to positive values represent synergy (positive interaction). If r is less than or equal to a negative value, it represents a trade-off (unfavorable exchange) [68]. This analysis was conducted for 48 countries in Africa.

2.3. Quantification of the Spatial Distributions of Ecosystem Services

Ecosystem functions refer to the habitat, biological or system properties, or processes of an ecosystem. Ecosystem goods (for example, food) and services (for example, waste assimilation) represent the benefits derived directly or indirectly by humans from ecosystem functions. We refer to ecosystem goods and services collectively as ecosystem services (ESs) in this study. Considering the importance of ESs and the data availability proposed by ref. [69], our study selected 17 ESs that belong to 4 ecosystem service categories according to ref. [43], which are: provisioning ecosystem services (food production, and raw materials); regulating ecosystem services (gas regulation, climate regulation, disturbance regulation, water regulation, water supply, waste treatment, and erosion control); supporting ecosystem services (soil formation, nutrient cycling, pollination, biological control, habitat/refugia, and genetic resources); and cultural services (recreation and culture) [2,70,71].

3. Results

Ecosystem service trade-offs arise from human management choices, which can change the type, magnitude, and relative mix of ecosystem services. Trade-offs occur when the provision of one ES is reduced because of the increased use of another ES. In some cases, a trade-off may be an explicit choice; in others, trade-offs arise without premeditation or even awareness that they are taking place. These unintentional trade-offs happen when we are ignorant of the interactions among ESs, e.g., refs. [72,73], when our knowledge of how they work is incorrect or incomplete [74], or when the ESs involved have no explicit markets. But even when a decision is the result of an explicit, informed choice, the decision may have negative implications. For example, adverse impacts may arise because of the scale mismatch between the intent of a particular management decision, the expected outcome, and the long-term or broad spatial scale of the decisions [38]. Synergies (or co-benefits), according to refs. [57,75,76] refer to the enhancement of two or more ESs at the same time. No relationship means that two or more types of ESs do not appear to increase or decrease the situation.

3.1. Spatial Trade-Offs and Synergies among ESs at the Continental (African) Scale

At the continental level, regulation services showed positive correlations (synergies) among gas regulation (0.66), climate regulation (0.71), disturbances regulation (0.14), water regulation (0.53), water supply (0.71), and waste treatment (0.06). For provisioning services, food production (0.72) and raw materials (0.73) increased, indicating a positive correlation between these ecosystem services for many years. In addition, the cultural services of culture (0.62) and recreation (0.60) also showed positive correlations, as did the supporting services: pollination (0.68), biological control (0.65), erosion control (0.58), nutrient cycling (0.01), genetic resources (0.69), soil formation (0.88), and habitat (0.58) (Figure 3). The positive correlation at the continental level can be attributed to the continent’s increasing population, so most countries are trying to develop regulations to protect the ecosystems. Government policies in each region also contributed to the synergies, as most regions are committed to legislating to improve the ecosystems. The AU urges all Member States to put in place systems and structures to fully utilize the global mechanisms to support climate change mitigation and adaptation [77].

3.2. Trade-Offs and Synergies across Five Regions

3.2.1. Synergies and Trade-Offs among LULC Types across the Five Regions

Six of the eight countries in the Central African regions (including Angola and Chad, Appendix B, Figure A1) showed trade-offs in UB (

Table 1. Trade-off and synergy analysis among the 48 countries and their ESs.

COUNTRY	FP	RM	GR	CR	DR	WR	WS	WT	EC	SF	NC	Po	BC	Ha	GeR	Re	Cu
Algeria	0.56	0.31	0.23	0.54	−0.77	−0.55	0.07	−0.84	0.17	−0.24	−0.91	0.88	0.59	0.84	0.81	0.21	0.81
Angola	0.65	0.74	0.67	0.89	−0.13	−0.28	0.64	−0.66	0.61	0.49	−0.52	0.66	0.51	0.35	0.73	0.76	0.44
Benin	0.62	0.54	0.72	0.56	0.41	0.34	0.43	0.38	0.59	−0.06	0.37	0.69	0.65	0.71	0.68	0.41	0.71
Botswana	0.75	0.83	0.55	0.97	−0.15	−0.44	0.81	−0.06	0.38	0.07	−0.15	0.63	0.59	0.46	0.65	0.25	0.89
Burkina Faso	0.64	0.58	0.74	0.49	−0.99	−0.66	0.47	0.06	0.57	0.48	−0.99	0.73	0.67	0.73	0.69	0.24	0.74
Burundi	0.83	0.58	0.16	0.49	0.21	0.69	0.94	0.36	0.95	0.81	−0.34	0.51	0.61	−0.21	0.63	0.86	−0.21
Cameroon	0.88	0.82	0.52	0.53	0.33	0.54	0.97	0.34	0.51	0.57	0.27	0.63	0.72	0.57	0.63	0.46	0.61
Central African Republic	0.76	0.88	0.58	0.53	0.38	0.55	0.93	0.88	0.59	0.92	0.35	0.64	0.69	0.62	0.64	0.48	0.62
Chad	0.69	0.54	0.79	0.42	−0.99	0.01	0.38	−0.84	0.05	−0.05	−0.99	0.76	0.61	0.64	0.74	0.06	0.73
Côte d’Ivoire	0.77	0.82	0.55	0.55	0.67	0.78	0.92	0.36	0.78	0.67	0.69	0.63	0.05	0.69	0.63	0.55	0.63
Democratic Republic of the Congo	0.66	0.06	0.62	0.76	0.57	0.82	0.87	0.05	0.72	0.97	0.34	0.62	0.57	0.25	0.66	0.75	0.25
Djibouti	0.85	0.57	0.91	0.66	−0.99	0.17	0.37	−0.93	−0.35	−0.05	−0.99	0.9	0.53	0.52	0.89	−0.02	0.75
Egypt	0.42	0.31	0.83	0.55	−0.97	0.26	0.46	−0.88	−0.35	0.39	−0.98	0.55	0.08	−0.29	0.52	0.01	0.12
Equatorial Guinea	0.91	0.88	0.44	0.47	0.87	0.45	0.67	0.35	0.81	0.56	0.11	0.6	0.72	0.75	0.57	0.54	0.39
Eritrea	0.81	0.76	0.85	0.63	−0.97	0.68	0.72	0.16	0.72	0.52	−0.98	0.84	0.82	0.85	0.83	0.65	0.85
Ethiopia	0.67	0.63	0.76	0.71	−0.23	0.41	0.57	−0.1	0.62	0.51	−0.62	0.72	0.68	0.73	0.72	0.64	0.74
Gabon	0.61	0.73	0.58	0.52	0.95	0.93	0.61	0.56	0.65	0.52	0.38	0.65	0.91	0.56	0.62	0.56	0.87
Gambia	0.66	0.72	0.67	0.94	0.88	0.65	0.68	0.86	0.85	0.74	0.88	0.65	0.71	0.71	0.67	0.69	0.65
Ghana	0.78	0.82	0.53	0.34	0.56	0.6	0.81	0.87	0.85	0.45	0.23	0.63	0.82	0.82	0.62	0.27	0.71
Guinea	0.73	0.75	0.62	0.47	0.68	0.15	0.73	0.82	0.61	0.78	0.77	0.66	0.69	0.66	0.65	0.35	0.66
Guinea-Bissau	0.72	0.63	0.64	0.12	0.78	0.69	0.68	0.82	0.61	0.52	0.82	0.68	0.75	0.11	0.64	0.45	0.81
Kenya	0.69	0.75	0.61	0.92	−0.05	0.64	0.74	0.17	0.71	0.91	−0.51	0.64	0.64	0.56	0.66	0.75	0.57
Lesotho	0.71	0.74	0.62	0.61	−0.89	0.34	0.77	0.75	0.65	0.86	−0.89	0.65	0.67	0.62	0.65	0.47	0.62
Liberia	0.41	0.93	0.28	0.16	0.82	0.98	0.35	0.43	0.44	0.51	0.21	0.61	0.91	0.49	0.55	0.18	0.68
Libya	0.24	−0.22	0.96	−0.51	−0.99	−0.17	−0.32	−0.97	−0.71	−0.75	−0.99	0.67	0.01	0.14	0.56	−0.62	0.57
Madagascar	0.75	0.77	0.62	0.78	0.19	0.44	0.77	−0.18	0.67	0.35	−0.23	0.64	0.63	0.53	0.66	0.75	0.55
Malawi	0.66	0.65	0.67	0.69	−0.31	0.6	0.61	−0.15	0.52	0.67	−0.66	0.66	0.63	0.53	0.67	0.62	0.65
Mali	0.76	0.63	0.88	0.49	−0.99	0.28	0.47	−0.71	0.28	0.21	−0.99	0.84	0.73	0.77	0.82	0.26	0.84
Mauritania	0.77	0.67	0.79	0.66	−0.48	0.69	0.66	−0.38	0.19	0.49	−0.48	0.79	0.66	0.66	0.78	0.43	0.74
Morocco	0.43	0.23	1.03	0.61	0.62	−0.29	−0.01	−0.12	0.59	−0.44	0.08	0.79	0.63	0.99	0.73	0.56	0.53
Mozambique	0.84	0.91	0.42	0.67	0.01	0.65	0.98	−0.03	0.67	0.57	−0.36	0.59	0.66	0.29	0.63	0.58	0.32
Namibia	0.82	0.84	0.74	0.87	−0.99	−0.08	0.81	−0.61	0.45	0.62	−0.99	0.77	0.73	0.68	0.77	0.33	0.72
Niger	0.75	0.51	0.31	0.18	−0.99	0.01	0.38	−0.63	0.41	−0.39	−0.99	0.95	0.82	0.99	0.91	0.25	0.65
Nigeria	0.67	0.62	0.68	0.33	0.73	0.43	0.58	0.91	0.61	0.56	0.75	0.68	0.69	0.74	0.66	0.28	0.74
Republic of Congo	0.84	0.69	0.64	0.65	0.66	0.63	0.74	0.74	0.65	0.29	0.67	0.65	0.67	0.63	0.65	0.65	0.62
Rwanda	0.74	0.82	0.44	0.58	−0.65	0.68	0.79	−0.52	0.49	0.86	−0.86	0.59	0.53	0.07	0.65	0.71	0.17
Senegal	0.66	0.72	0.65	0.8	0.94	0.48	0.66	0.91	0.82	0.72	0.94	0.65	0.69	0.69	0.66	0.65	0.66
Sierra Leone	0.91	0.89	0.39	0.44	0.52	0.84	0.69	0.64	0.54	0.79	0.57	0.62	0.74	0.52	0.61	0.39	0.51
Somalia	0.77	0.76	0.77	0.77	−0.85	0.69	0.74	0.26	0.73	0.66	−0.94	0.77	0.76	0.77	0.77	0.73	0.77
South Africa	0.84	0.92	0.62	0.91	−0.22	0.46	0.95	0.33	0.79	0.11	−0.62	0.71	0.76	0.57	0.75	0.58	0.58
Sudan	0.62	0.57	0.71	0.52	0.54	0.42	0.51	0.54	0.58	0.33	0.54	0.67	0.64	0.67	0.66	0.49	0.69
Swaziland	0.86	0.72	0.12	0.59	0.11	−0.48	0.36	0.88	0.05	0.83	0.32	0.5	0.64	−0.35	0.63	0.21	−0.36
Tanzania	0.72	0.85	0.52	0.96	0.25	0.65	0.79	−0.06	0.78	0.55	−0.28	0.59	0.58	0.13	0.66	0.84	0.16
Togo	0.64	0.61	0.7	0.44	0.72	0.16	0.54	0.57	0.71	0.44	0.91	0.68	0.68	0.73	0.67	0.28	0.72
Tunisia	0.34	0.02	0.97	0.03	−0.98	−0.6	−0.11	−0.94	−0.49	−0.15	−0.99	0.64	0.12	0.24	0.55	−0.46	0.64
Uganda	0.72	0.78	0.51	0.84	−0.52	0.68	0.73	0.05	0.59	0.93	−0.71	0.61	0.62	0.42	0.64	0.67	0.45
Western Sahara	−0.26	−0.37	−0.77	0.24	−0.99	0.78	0.48	−0.96	−0.91	0.11	−0.99	−0.54	−0.77	−0.93	−0.48	0.07	−0.88
Zambia	0.81	0.78	0.51	0.62	−0.77	0.24	0.76	−0.85	0.15	0.53	−0.99	0.64	0.49	0.22	0.66	0.35	0.38
Zimbabwe	0.83	0.93	0.28	0.67	−0.31	0.56	0.98	0.99	0.68	0.92	−0.41	0.57	0.73	0.34	0.62	0.45	0.34

Note: Food production (FP), raw materials (RM), gas regulation (GR), climate regulation (CR), disturbance regulation (DR), water regulation (WR), water supply (WS), waste treatment (WT), erosion control (EC), soil formation (SF), nutrient cycle (NC), pollination, biological control (BC), habitat (Ha), genetic regulation (GR), recreation (Re), culture (Cu).

). Most people in the Democratic Republic of Congo have not benefited from this wealth. Protracted conflict, political instability, and dictatorship have led to a severe and prolonged humanitarian crisis. In addition, there has been forced population displacement. These characteristics have not changed significantly since the end of the Congo wars in 2003. In addition, the instability of the government has led to trade-offs for the UB. Most of the time, instability in power leads to conflict, causing people to abandon their homes and stay in remote areas. In contrast, countries such as Cameroon and the Central African Republic have seen synergies in UB due to government stability. The Central African Republic, Cameroon, and the Democratic Republic of Congo have seen trade-offs in GL owing to increased construction for residential and commercial use.

The Northern African region (Appendix B, Figure A2) showed trade-offs of the countries of the region in terms of UB, which can be attributed to the desertification affecting the region and climate change caused by desertification in Libya and Western Sahara, which showed natural vegetation, forests (Libya), SL, and GL (Western Sahara) (Table 1). In general, the area showed a trade-off of artificial cover, while it mainly showed the synergy of natural vegetation cover. All 15 countries in the West African region (Appendix B, Figure A3) indicated trade-offs in UB. With the exception of Senegal, all other countries in the region have had trade-offs in terms of one or more natural vegetation covers. Senegal has had the most attractive synergies in land use and land cover. Overall, the natural vegetation of the area is seriously threatened [24]. According to the National Action Program to Combat Desertification) [78] of the federal Ministry of Environment in Nigeria, desertification is by far the most pressing environmental problem in the dryland areas of the country [79,80]. A clear sign of this phenomenon is the gradual transformation of vegetation from grass, shrubs, and occasionally trees, to grass and shrubs, and at the final stage, to a vast desert area with sand [81,82,83,84,85].

It is estimated that 50 to 75 per cent of Nigerian states, such as Bauchi, Borno, Gombe, Jigawa, Kano, Katsina, Kebbi, Sokoto, Yobe, and Zamfara, have been affected by desertification. These states have a population of about 27 million, which is about 38% of the country’s total land area. In these areas, demographic pressure has led to overgrazing and the over-exploitation of marginal lands, exacerbating desertification and drought [78]. In the northernmost regions of Katsina, Sokoto, Jigawa, Borno, and Yobe, entire villages and main roads have been buried in sand dunes [84].

In East Africa (Appendix B, Figure A4), there is a trade-off between artificial land covers and predominantly natural vegetation covers. The natural vegetation of one type of land cover in Zimbabwe, Uganda, Rwanda, and Kenya showed trade-offs. The Southern African region showed trade-offs in terms of UB. Lesotho and Namibia indicated trade-offs in FS. South Africa, Namibia, and Botswana expressed trade-offs in WL. In general, there are trade-offs for UB on the African continent. See Appendix B, Figure A5 for LULC in Southern Africa.

3.2.2. Trade-Offs and Synergies among 17 ESs across Five Regions

In the Central African region (Appendix B, Figure A6) there are trade-offs in nutrient cycle (range −0.52 to −0.99), waste treatment (−0.66 to −0.84), water regulation (−0.28 in Angola), disturbance regulation (range from −0.13 to −0.99), and soil formation (−0.05 in Chad), and only in Angola or Chad or both. This indicates that the two countries may have similar ESs management strategies. On the other hand, the remaining 6 countries including the Republic of Congo showed synergies among all ESs. Overall, it can be said that there are synergies among the ESs of the Central African region. The Republic of Congo has a unified model of synergy. There are trade-offs in the UB across countries in the North African region. The trade-off of generators was recorded in the North African region overall (Appendix B, Figure A7), with the exception of Sudan, which showed synergies among all ESs. The biggest trade-off occurred in the Western Sahara, leaving only five ESs with synergy. West Africa, Niger, Mali, Burkina Faso, and Benin have experienced similar trade-offs, particularly in terms of nutrient cycling (Appendix B, Figure A8). The most important factor contributing to this trade-off in parts of North and West Africa is climate change.

In the East African region (Appendix B, Figure A9), there were trade-offs in nutrient cycling (ranging from −0.23 to −0.99) in all countries in the region. The most trade-offs took place in Djibouti, while the fewest trade-offs occurred in Tanzania. The Southern African region (Appendix B, Figure A10) mainly exhibited trade-offs between disturbance regulation and nutrient cycling.

Namibia and Botswana had the highest trade-offs, while South Africa had the lowest trade-offs (Appendix B, Figure A10). In general, there were trade-offs in nutrient cycling in these five regions (Figure A6, Figure A7, Figure A8, Figure A9 and Figure A10, in Appendix B).

3.3. Spatial Trade-Offs and Synergies among 17 ESs and LULC in the 48 African Countries

There are variations in the spatial trade-offs and synergies among 17 ESs and LULC in the 48 African countries, underlining the ESs prioritized by various nations. Cultural services were mainly synergistic in most countries, while regulating, provisioning, and supporting services varied, as shown in Table 1.

See Appendix A for results on a national scale.

See Appendix A and the Supplementary Information for the descriptions of each country.

See Appendix A and the Supplementary Information for the results on the spatial trade-offs and synergies among the LULC types in each of the 48 countries.

4. Discussion

4.1. Spatial Interactions among ESs at the Continental Scale

The continent showed positive values in all the ecosystem services. Regulation services demonstrated a positive (synergistic) contribution to land use/land cover. Implementing appropriate regulation has contributed to the development of the continent’s ecosystem and economies, demonstrating the effectiveness of the AU’s ‘Agenda 2063’ (Continental Sustainable Development Initiative). Proper regulation has led to positive growth in provisioning services. Human dependence on the provision of ecosystem services is widely recognized in developing regions, such as Africa, where many people are poor and rely on natural resources [31,42]. With drought, vegetation and socio-economic conditions, communities across Africa have become more reliant on natural resources [24]. In the humid and forested areas in West and Central Africa, food and raw materials, as well as agriculture, are important ecosystem services. Forests, shrublands, and water bodies play a synergistic role in cultural services to promote the development of tourism in Africa. This also shows that the synergies among supporting services are attributable to the benefits provided by the dense tropical forest. According to 2015 data from the Food and Agriculture Organization (FAO) of the United Nations, Africa accounts for about 15% of the world’s remaining forests, and its dense tropical forests are second only to South America in terms of the number of dense tropical forests, making it the most effective region for removing carbon from the atmosphere [86]. It is estimated that the vast forests of the Democratic Republic of Congo alone contain up to 8% of the carbon stored in the Earth’s vegetation [80].

4.2. Spatial Interactions among ESs across the Five Regions

North Africa showed synergies in most ecosystem services, but the trade-offs in waste treatment and nutrient cycling mean that much of the environment is likely to be negatively affected. The bare lands that can be seen everywhere in the region have been caused by desertification exacerbated by climate change. West Africa showed more synergies among all ecosystem services due to the dense forests and the characteristics of the Sahel region. It also borders the Atlantic Ocean [26], where the ecosystem services have grown over time. There is a degree of synergies and trade-offs among Central, East, and Southern Africa.

4.3. Interactions among ESs across the 48 Countries

Nationally, regulating services in Algeria have increased significantly over the past 20 years. However, the country still faces significant challenges in mitigating the trade-off between supporting and regulating services to improve the provision of ESs or to promote biodiversity. These ecosystem services imply growth in the agricultural and productive sector at the expense of the ecological environment and soil nutrients [87]. Cultural services also appeared to have strong positive values and synergy among the ecosystems. This shows that the country has good economic growth and strong development in cultural tourism. However, over time, it will face land and environmental degradation. In Angola, it is certain that the country’s cultural services have improved during this period and the country’s provisioning services are on the right path with positive values. These ecosystem services imply that the increase in agricultural and productive sectors have been at the expense of the ecological environment and soil nutrients [87]. However, the country needs to mitigate urbanization and environmental degradation by considering the trade-offs that exist among disturbance regulation, water regulation, and waste treatment.

In Benin, regulating and provisioning services have increased over time, and cultural services have a strong positive value, but there have been trade-offs in soil formation. This means that the country’s ecosystem and economy are moving in a positive direction, but proper studies must be carried out to curb land degradation. In Botswana, trade-offs in nutrient cycling, waste treatment, water regulation, and disturbance regulation have had a negative impact on the country’s urbanization. The synergy between climate regulation and biological control proves that ecosystems are appropriately regulated. Burkina Faso indicated that the resulting impact on ecosystems was that cultivated land, forests and shrubland contributed to the positive development of the ecosystems. UB, WB, and WL negatively impact ecosystems, due to overuse. Cultural services encourage the use of FS and WB.

Burundi’s forests, cultivated land, and water bodies have contributed to the country’s food production, raw materials, and water supply. In addition, at the same time, these have also harmed the habitats of these services. The built-up and bare lands have had a positive impact on the ecosystems. In Cameroon, the Central African Republic, Cote d’Ivoire, and the Democratic Republic of Congo, forests, shrublands, and grasslands have contributed positively to regulating services, provisioning, cultural and supporting services in food production, raw materials, recreation, and habitat services. Disturbance regulation, water regulation, waste treatment, and erosion control have had positive values that indicate appropriate environmental and ecosystem management. In Chad, the loss of nutrients and soil formation is attributed to the desertification in the region [80]. The trade-offs between disturbance and waste regulation show that built-up areas face environmental pollution, which can negatively impact the ecosystems in the area. In Djibouti, appropriate regulation has been implemented to help develop ecosystem and economies. Proper regulation has contributed to the positive growth of provisioning services. Forests, shrublands, and water bodies have played a synergistic role in cultural services and have contributed to the development of national tourism. The overuse of bare land and forests can also lead to soil formation and nutrient cycling trade-offs.

In Egypt, appropriate regulation has been implemented to help develop the ecosystems and economies. Proper regulation has led to positive growth in provisioning services. Forests, shrublands, and water bodies have played a synergistic role in the cultural services and have contributed to the development of national tourism. The overuse of bare lands and forests can also lead to trade-offs in habitat and nutrient cycling. Equatorial Guinea has appropriate regulatory measures in place to help develop ecosystems and the economies. Proper regulation has contributed to the positive growth of provisioning services. Forests, shrublands, and water bodies have played a synergistic role in cultural services and have contributed to the development of national tourism. The analysis indicates that the synergistic effect of supporting services benefits from the dense tropical forest. In Eritrea, appropriate regulation has been implemented to help develop the ecosystems and economies. Proper regulation has contributed to the positive growth of provisioning services. Forests, shrublands, and water bodies have played a synergistic role in cultural services and have contributed to the development of national tourism. The overuse of bare land can also lead to trade-offs in nutrient cycling.

Ethiopia, Kenya, and Lesotho have implemented appropriate regulations to help develop the ecosystems and the economies. Proper regulation has led to a positive growth in provisioning services. Forests, shrublands, and water bodies have played a synergistic role in cultural services and have contributed to the development of national tourism. The overuse of bare land can also lead to the trade-offs in nutrient cycling. Gabon, Gambia, Ghana, Guinea, Guinea Bissau, and Liberia have appropriate legislation in place to help develop ecosystems and economies. Proper regulation has contributed to the positive growth in the provisioning service. Forest, shrubland, and water bodies have played a synergistic role in cultural services and have contributed to the development of national tourism. The analysis indicates that the synergistic effect of supporting services benefits from the dense tropical forest. Libya’s ecosystem appears to be an unfavorable and harsh environment, given its trade-offs with regulating services. This can be attributed to desertification [88].

Madagascar has appropriate regulations in place to help develop the ecosystems and economies. Proper regulation has led to positive growth in provisioning services. Forest, shrubland, and water bodies have played synergistic roles in cultural services and have contributed to the development of national tourism. The analysis indicates that the synergistic effect of supporting services benefits from the dense tropical forest. However, nutrient cycling showed a trade-off that negatively impacts the ecosystems. Malawi and Mali have implemented appropriate regulations to help develop the ecosystems and economies. Proper regulations have led to a positive growth in Mauritania’s provisioning service. Forest, shrubland, and water bodies have played a synergistic role in the cultural services and have contributed to the development of national tourism. Over the years, nutrient cycling has presented a trade-off that indicates a negative impact on the ecosystems of the region.

Moroccan regulating services have made a positive contribution to LULC, with the exception of built-up areas, which contribute to the trade-off between water regulation and waste treatment. This also indicates that there are insufficient water systems in the ecosystems of the region. Cultivated land, forests, shrublands, and wetlands have had synergistic roles in food production and raw materials, cultural services, etc., and have contributed to the development of national tourism. Over the years of study, soil formation presented trade-offs that showed negative impacts on the ecosystems in the area. Mozambican regulating services showed a positive contribution to the LULC, in addition to built-up areas that make trade-offs in waste treatment. Cultivated land, forests, shrublands, and wetlands have played a synergistic role in food production, raw material, and cultural services, etc., and have contributed to the development of national tourism. Nutrient cycling showed a trade-off over many years over the study period, indicating a negative impact on the ecosystems in the region.

Namibia’s analysis showed that regulation has been implemented to help develop ecosystems and the economies. Proper regulation has contributed to the positive growth of provisioning services. Forests, shrublands, and water bodies have played synergistic roles in cultural services and have contributed to the development of national tourism. Over the years, nutrient cycling has presented a trade-off that has shown a negative impact on the region’s ecosystems. The results of Niger show that the regulation to help develop the ecosystems and economies has been inadequate. The increase in provisioning services can be attributed to the fact that water supplied by the Niger River is used to irrigate cultivated land in exchange for increased food production and raw materials. Forests, shrublands, and water bodies have played synergistic roles in cultural services and have contributed to the development of national tourism. Over the years, nutrient cycling and soil formation have presented trade-offs that indicate negative impacts on ecosystems in the region.

In Nigeria, regulating services have made a positive contribution to LULC. This shows that implementing appropriate regulations has helped develop ecosystems and economies. Proper regulation has led to positive growth in provisioning services. Forests, shrublands, and water bodies have played synergistic roles in cultural services and have contributed to the development of national tourism. The analysis indicates that the synergistic effect of supporting services benefits from the dense tropical forest. In the Republic of Congo, regulating services have made a positive contribution to LULC, in addition to built-up areas, which make trade-offs in waste treatment. Cultivated land, forests, shrublands, and wetlands have played synergistic roles in food production, raw material, cultural services, etc., and have contributed to tourism development.

In Rwanda, regulating services have made a positive contribution to LULC, except for built-up areas, which make trade-offs between disturbance regulation and waste treatment. Cultivated land, forests, shrublands, and wetlands have played synergistic roles in food production, raw materials, cultural services, etc., and contribute to the development of national tourism. Nutrient cycling requires trade-offs that negatively impact ecosystems. In Senegal, regulating services have made a positive contribution to LULC. This shows that implementing appropriate regulation helps develop ecosystems and economies. Proper regulation has led to positive growth in provisioning services. Forests, shrublands, and water bodies play synergistic roles in cultural services and contribute to the development of national tourism. The analysis indicates that the synergistic effect of supporting services benefits from the dense tropical forest.

Sierra Leone, Sudan, and Togo have appropriate legislation in place to help develop the ecosystems and economies. Proper regulation leads to positive growth in provisioning services. Forests, shrublands, and water bodies play synergistic roles in cultural services and contribute to the development of national tourism. The analysis indicates that the synergistic effect of supporting services benefits from the dense tropical forest. In Somalia, regulating services have contributed positively to LULC, except in built-up areas, which has led to trade-off of disturbance. Cultivated land, forests, shrublands, and wetlands play synergistic roles in food production, raw materials, cultural services, etc., and contribute to the development of national tourism. On the other hand, there are some trade-offs in nutrient cycling that can negatively impact ecosystems.

In South Africa, regulating services presented a positive contribution to LULC, except for built-up areas, which has led to trade-offs in disturbance regulation. Cultivated land, forests, shrublands, and wetlands play synergistic roles in food production, raw material, cultural services, etc., and contribute to the development of national tourism. Nutrient cycling requires trade-offs that negatively impact ecosystems. In Swaziland, regulating services showed that LULC also contributes positively, in addition to built-up areas, which contribute to water regulation trade-offs. Cultivated land, forests, shrublands, and wetlands play synergistic roles in food production, raw materials, cultural services, etc., and have contributed to the development of national tourism. Habitat trade-offs can have a negative impact on ecosystems.

Tanzania and Zambia show that regulating services have positively contributed to trade-offs in waste treatment. Cultivated land, forest, shrubland, and wetlands have synergy with food production and raw material as well as cultural services and have contributed to the country’s tourism development. On the other hand, nutrient cycling has trade-offs that have negatively impacted the ecosystem. As a result of the geographic location, most of the ecosystem functions in Tunisia have experienced trade-offs over time. The climate change effect has also played a significant role in the trade-offs. However, food production and raw materials show increased cultivated output. In Western Sahara, improper regulation has led to trade-offs in food production, genetic resources, nutrient cycling, and habitat trade-offs due to the overutilization of the forest and bare soil and harm to the ecosystem.

In Zimbabwe, regulating services have made a positive contribution to LULC, except in built-up areas, which has led to a trade-off of disturbance. Cultivated land, forests, shrublands, and wetlands play synergistic roles in food production, raw material, cultural services, etc. and have contributed to the development of tourism. On the other hand, trade-offs in nutrient cycling can negatively impact ecosystems.

5. Conclusions

5.1. Trade-Offs and Synergies among ESs and LULC Types

5.1.1. Trade-Offs and Synergies at the Continental Level

The continent shows positive values in all ecosystem services. Regulating services show synergies with LULC. Proper regulation has contributed to the development of the continent’s ecosystems and economies, highlighting progress in the AU’s ‘Agenda 2063’ Sustainable Development Initiatives, and the UN’s Global Goals. Proper regulation can promote positive growth in provisioning services. Forests, shrublands, and water bodies play synergistic roles in cultural services and promote tourism in Africa. This also shows that the synergies among the supporting services benefit from the dense tropical forest.

5.1.2. Trade-Offs and Synergies at the Regional Level

The analysis shows that trade-offs in North Africa (Appendix B, Figure A7) suggest that much of the environment could be severely negatively affected as climate change exacerbates desertification. There are more synergies among all ESs in West Africa (Appendix B, Figure A8), which is promising given the dense forests of the region and the Sahel region. In addition, the area bordering the Atlantic Ocean and the number of ESs have grown over time. There is a degree of synergies and trade-offs in Central (Appendix B, Figure A6), Eastern (Appendix B, Figure A9), and Southern Africa (Appendix B, Figure A10).

5.1.3. Trade-Offs and Synergies at the National Level

Nationally, while Equatorial Guinea, Gabon, the Republic of Congo, Sudan, and Senegal (Figure A1, Figure A2, Figure A3, Figure A4 and Figure A5 in Appendix B) all share common trade-offs in terms of UB, they have the highest synergistic effect among the remaining seven LULC types. The mainland’s environmental management system and policies are worth exploring. The UB’s trade-offs show that these countries have not prioritized urban sprawl because population growth is a factor in urbanization. The UN World Population Prospects highlight that Senegal’s annual population growth rate decreased from 2.73% in 2018 to 2.72% in 2019, and that it is expected to decrease sharply from 2030 (2.44%) to 2040 (2.21%) and beyond, which may explain the trade-off in UB.

There are synergies among the 17 ESs in Equatorial Guinea, the Republic of Congo, Gambia, Gabon, Sudan, Senegal, Sierra Leone, Nigeria, Ghana, Togo, Liberia, Guinea, Guinea-Bissau, Cote d’Ivoire, Cameroon, Central African Republic, and the Democratic Republic of Congo (Figure A6, Figure A7, Figure A8, Figure A9 and Figure A10 in Appendix B). In addition, Angola, Chad, Rwanda, Djibouti, Ethiopia, Malawi, Uganda, Kenya, Botswana, Lesotho, Namibia, South Africa, and Swaziland (in addition to culture and habitat) share similar trade-offs in waste treatment, nutrient cycling, disturbance regulation, water regulation, and most other functional synergies (Figure A6, Figure A7, Figure A8, Figure A9 and Figure A10 in Appendix B).

5.2. Policy Implication

Ecosystem services are the direct and indirect contributions that ecosystems (known as natural capital) provide to humans’ well-being and quality of life. In a practical sense, this can be providing food and water, regulating the climate, or cultural aspects, such as reducing stress and anxiety. The vast number of services provided by ecosystems can be categorized into four manageable groups: provisioning, regulating, cultural, and slightly less obvious supporting services. These services provided by ecosystems can bring benefits in the form of security, goods and materials, health, and well-being for humans. However, the use of the continent’s natural resources needs to be properly regulated so that frequent use does not lead to resource depletion and affect ecosystems. Whether we recognize ecosystem services or not, we all benefit. Most of us appreciate the ecosystem services we encounter every day, even if we do not yet know their true value. The loss of nature affects our economy, our culture, and the daily lives of individuals. Investing in natural biodiversity and ecosystems, geographical diversity, and aquatic and underwater landscapes will help improve the quality of these services and ensure their provision for future generations.

Overall, policymakers within African countries, regions, and the continent should pay attention to the use of land for construction, and the protection of forests, grasslands, and surface water bodies with high environmental benefits. They must delineate ecological redline areas to protect forests, grasslands, and surface water bodies with high ecological functions.

5.3. Limitations

Our study still has some limitations because the synergy/trade-off analysis was limited to pairwise targets. The accuracy of collaborative analytics improves as the data improves. Since the data for all indicators/targets are not available on an annual basis, the analysis was performed using a 19-year interval time frame. The availability of annual data can help track trade-offs/synergy on a regular basis. This will enhance the relevance and applicability of such analyses. Therefore, redefining indicators that reflect the Sustainable Development Goals (SDGs) can better reflect sustainability analyses. Nevertheless, the approach suggested here could serve as a starting point for envisioning the linkages among the SDGs and tracking sustainable development in a more integrated manner. This approach contributes to the development of coherent sustainable development policies. This collaborative analysis can be used to analyze transition paths to better sustainability. Future studies will evaluate the trade-offs and synergy among different years and determine the ecosystem service bundles.