GEP-based ecological functional zoning and land use drivers in coastal southeast China

Summary

Gross ecosystem product (GEP) assesses ecological value, but aggregate growth in urbanizing coastal zones often masks severe structural imbalances. Using the Fujian coast (2003–2023), we integrated spatiotemporal GEP accounting and spatial clustering to quantify land-use drivers and delineate functional zones. We reveal that while GEP doubled, foundational regulating services plummeted from 66.95% to 43.89%. Urban expansion triggered a “value substitution trap”, where surging monetized cultural services mathematically masked the physical depletion of regulating capacities, creating a deceptive illusion of sustainability. Based on these structural shifts, five distinct ecological-economic zones were identified. These findings demonstrate that coastal governance must transcend aggregate GEP targets. Implementing spatially differentiated strategies based on internal service structures is essential to mitigate hidden ecological deficits and ensure sustainable coastal planning.

Graphical abstract

Highlights

• •The GEP growth conceals critical regional ecological risks
• •The value substitution trap masks ecological loss in urbanization
• •Explicit economic value doubled while implicit ecological value dropped
• •Five functional zones were identified based on GEP density and its structure

Earth sciences; Environmental management; Environmental science

Introduction

As the interface between land and ocean, the coastal zone is one of the regions with the richest global biodiversity and ecological functions, as well as a hotspot for intensive socio-economic activities.^1,2 However, with the rapid urbanization and industrialization since China’s reform and opening-up, high-intensity economic development and widespread coastline modification have led to the degradation of coastal wetlands, intensifying the conflict between regional economic growth and ecological protection.^3,4 In this context, gross ecosystem product (GEP), as an indicator quantifying the aggregate value of ecological products and services provided by ecosystems for human well-being,⁵ has become a critical policy instrument for assessing regional sustainable development and practicing the concept that “lucid waters and lush mountains are invaluable assets”.^6,7,8 However, while aggregate GEP serves as a vital macroeconomic indicator, a critical scientific question emerges: does a rising GEP always signify improving ecosystem health, or might it mask internal structural vulnerabilities induced by intense human activities?

Although existing studies have yielded rich results regarding GEP accounting methods and spatiotemporal evolution analysis,^9,10,11 the current literature predominantly focuses on the net increase or decrease of total value. Few studies have delved into the structural shifts of internal GEP service functions and the underlying land-use driving mechanisms. Particularly in economically developed coastal areas, the continuous growth of GEP is often accompanied by a drastic transformation of its internal structure. On one hand, urban expansion leads to the loss of regulating services (e.g., water purification and flood regulation) by encroaching upon water bodies and wetlands. On the other hand, it generates a monetized surge in cultural service value through tourism development and the improvement of human settlements.^12,13 This dynamic can create a value substitution trap, where explicit market values mask implicit ecological losses, leading to an apparent growth in GEP. If left unexamined, this apparent growth can cause decision-makers to overlook a “potential ecological deficit risk”—a state where the ecosystem’s fundamental, irreversible regulating functions are structurally compromised. Consequently, it is urgent to move beyond mere total quantity analysis and quantitatively reveal the deep-seated mechanisms of GEP structural transformation driven by land use changes.

Facing the complex ecological pressures induced by land use changes, implementing differentiated spatial regulation is key to achieving refined governance of coastal zones.^14,15,16 Although some studies have attempted functional zoning based on ecosystem service value (ESV) or GEP,^17,18most rely on simple hierarchical classifications based on total values (i.e., high, medium, or low-value zones). However, coastal regions exhibit significant spatial heterogeneity in resource endowments and development patterns. Relying solely on total value fails to identify complex ecological-economic patterns such as “high provision-low regulation” or “high regulation-low culture.” Therefore, introducing multi-dimensional feature indicators and utilizing unsupervised learning algorithms, such as K-means clustering, to identify functional zones based on service structure characteristics is of great significance for formulating localized and tailored management strategies.

To address these critical gaps, this study selects the coastal zone of Fujian Province—China’s first National Ecological Civilization Pilot Zone—as an ideal case study to examine the human-land relationship under extreme spatial constraints. Located on the southeast coast of China (23°30′N–28°22′N, 115°50′E−120°40′E), the region features a tortuous coastline stretching 3,752 km and a subtropical marine monsoon climate that nurtures valuable ecological resources, including mangroves, coastal wetlands, and seagrass beds. However, the terrain presents a unique “mountain-sea-city” configuration where steep hills and low mountains severely constrain available construction land. Despite these limitations, the coastal zone serves as a highly concentrated socio-economic hub. With the rapid economic rise of six major coastal cities (Fuzhou, Xiamen, Quanzhou, Zhangzhou, Putian, and Ningde), high-intensity urbanization has placed immense pressure on the local environment. The intense spatial competition often leads to direct encroachment on narrow but ecologically critical aquatic and forest systems, making it an excellent case study for examining the human-land relationship under extreme spatial constraints.^19,20,21 To facilitate consistent socio-economic and spatial analysis, the 37 coastal districts and counties under these cities were consolidated into 24 distinct spatial units (Figure 1).

Figure 1.

Overview of the study area

Therefore, this study focuses on the coastal zone of Fujian Province from 2003 to 2023. Based on the construction of a localized GEP accounting framework, this research aims to: (1) calculate the spatiotemporal evolution of GEP at five key time points (2003, 2008, 2013, 2018, and 2023) and analyze the overall trends and structural changes; (2) construct a unit area GEP change intensity index to quantitatively analyze the potential “value replacement” mechanism driven by land use changes; and (3) select the densities of provisioning, regulating, and cultural services in 2023 as core indicators to identify ecological-economic functional zones using the K-means clustering algorithm. This study aims to uncover the ecological costs behind GEP evolution and provide a zoning scheme based on service structure characteristics, thereby offering a scientific basis for sustainable coastal development, refined territorial space management, and the formulation of ecological compensation policies.

Results

GEP accounting results

Based on the constructed GEP accounting framework for the Fujian coastal zone, the GEP values from 2003 to 2023 were calculated (Table 1). As shown in Table 1, the GEP of the Fujian coastal zone exhibited a rapid growth trend from 2003 to 2023, increasing from 575,626 million CNY at the beginning of the period to 1,093,293.08 million CNY at the end. This represents a net increase of 517,666 million CNY, nearly doubling over the study period.

Table 1.

Valuation results of GEP in the Fujian coastal zone (unit: million CNY)

	Year	2003	2008	2013	2018	2023
Provisioning services	agriculture, forestry, animal husbandry, and fishery products	145,615.49	165,980.51	205,245.86	235,907.53	281,279.82
	water resource products	9,959.00	13,142.00	11,748.41	12,868.07	8,868.50
	Subtotal	155,574.49	179,122.51	216,994.26	248,775.60	290,148.32
Regulating services	soil conservation	26,636.46	36,115.74	33,295.79	39,755.55	43,006.91
	flood regulation	121,632.20	118,485.94	127,137.12	124,255.02	120,419.00
	water conservation	5,580.18	127,380.86	104,884.14	99,122.60	142,103.27
	water purification	214.98	221.59	205.58	174.14	147.82
	climate regulation	188,829.81	194,662.60	180,890.30	154,717.42	132,276.22
	carbon sequestration	113.12	115.53	117.02	114.72	119.68
	air purification	1,287.52	1,308.69	1,298.06	1,269.40	1,254.02
	oxygen release	1,415.36	1,445.55	1,464.20	1,435.44	1,497.50
	coastal protection	8,479.09	7,989.35	8,872.77	8,477.56	7,996.66
	biodiversity conservation	31,649.35	31,786.91	31,786.91	31,786.91	31,786.91
	Subtotal	385,838.07	519,512.75	489,951.90	461,108.77	480,607.99
Cultural services	tourism and sightseeing	34,214.09	72,826.28	133,715.16	326,629.43	322,536.78
GEP		575,626.65	771,461.54	840,661.32	1,036,513.80	1,093,293.08

Spatiotemporal evolution analysis of GEP

Time series trend analysis of ecological product value

The temporal analysis (Table 2) reveals that behind the aggregate GEP growth, the internal structure of the Fujian coastal zone underwent a fundamental transformation. M-K tests confirmed a significant upward trend in GEP and provisioning services (p = 0.027), with cultural services emerging as the primary growth engine (CAGR 11.87%). Conversely, regulating services exhibited a non-significant monotonic trend (p = 1.000), primarily due to the 2003 extreme drought anomaly. According to the Fujian Environmental Status Bulletin, 2003 experienced rare persistent heatwaves, resulting in an exceptionally low water conservation baseline. Excluding this outlier, regulating services showed a clear downward trend, highlighting the structural risks concealed beneath the apparent aggregate growth.

Table 2.

Annual average change, proportional composition, and trend analysis of GEP in the coastal zone of Fujian province

	Period	Provisioning Services	Regulating Services	Cultural Services	GEP
Annual Average Change (Million CNY)	2003–2008	4,709.60	26,734.94	7,722.44	39,166.98
	2008–2013	7,574.35	−5,912.17	12,177.78	13,839.96
	2013–2018	6,356.27	−5,768.63	38,582.85	39,170.50
	2018–2023	8,274.54	3,899.84	−818.53	11,355.86
	2003–2023	6,728.69	4,738.50	14,416.13	25,883.32
Annual Average Growth Rate (%)	2003–2008	2.86	6.15	16.31	6.04
	2008–2013	3.91	−1.17	12.92	1.74
	2013–2018	2.77	−1.21	19.56	4.28
	2018–2023	3.12	0.83	−0.25	1.07
	2003–2023	3.17	1.11	11.87	3.26
Proportion (%)	2003	27.09	66.95	5.96	100
	2008	23.26	67.28	9.46	100
	2013	25.86	58.21	15.93	100
	2018	24.03	44.41	31.55	100
	2023	26.57	43.89	29.54	100
Mann-Kendall test (p value)		0.027	1.00	0.086	0.027

Spatiotemporal evolution analysis of GEP

To intuitively visualize the basic distribution characteristics of GEP in the Fujian coastal zone, this study used the county as the spatial unit and applied the Natural Breaks (Jenks) classification method to divide the data into six levels. Using ArcGIS software, thematic maps of the spatial distribution of GEP and its three constituent services were generated for five time points: 2003, 2008, 2013, 2018, and 2023 (Figure 2).

Figure 2.

Spatial distribution of GEP in the Fujian coastal zone (2003–2023)

Note: EPS, ERS, and ECS represent ecosystem provisioning, regulating, and cultural services, respectively. The values are classified into six levels (L1–L6) based on the following intervals (Unit: Million CNY): (1) EPS: L1 (2,922, 5,331), L2 (5,331, 6,958), L3 (6,958, 8,810), L4 (8,810, 14,146), L5 (14,146, 19,875), L6 (19,875, 33,869); (2) ERS: L1 (1,130, 4,988), L2 (4,988, 9,351), L3 (9,351, 14,888), L4 (14,888, 19,800), L5 (19,800, 31,247), L6 (31,247, 47,647); (3) ECS: L1 (188, 4,201), L2 (4,201, 6,976), L3 (6,976, 12,015), L4 (12,015, 19,387), L5 (19,387, 34,705), L6 (34,705, 109,690); (4) GEP: L1 (5,359, 14,751), L2 (14,751, 23,262), L3 (23,262, 37,345), L4 (37,345, 52,444), L5 (52,444, 68,479), L6 (68,479, 142,021).

In summary, the spatial distribution of GEP and its three constituent services along the Fujian coast exhibits uneven patterns, revealing significant spatial heterogeneity and evolutionary characteristics. To more objectively and comprehensively identify this spatial heterogeneity and its scale effects, this study further employed global and local spatial autocorrelation analysis methods to examine the spatial connectivity of GEP at two distinct scales: the county scale and the 1 km grid scale.

Global spatial autocorrelation analysis

Spatial autocorrelation analysis (Table 3) demonstrated a pronounced scale effect. GEP distribution exhibited strong and strengthening positive spatial correlation at the 1 km grid scale (Moran’s I peaking at 0.717 in 2023), whereas county-level patterns remained weaker and unstable (0.102–0.294). However, analyzing the three constituent services at the county scale revealed critical spatial divergence. Regulating services exhibited strong and continuously enhancing clustering (Moran’s I rising from 0.278 to 0.627), indicating the distinct formation of contiguous ecological barriers. In contrast, provisioning services showed weak and fluctuating clustering, while cultural services presented a completely random distribution (Moran’s I near 0). This divergence proves that while administrative boundaries mask aggregate GEP patterns, they clearly capture the spatial decoupling of different ecosystem functions.

Table 3.

Multi-scale global Moran’s I for GEP in the Fujian coastal zone (2003–2023)

Scale	Year	2003	2008	2013	2018	2023
County Scale	GEP	0.219	0.294	0.227	0.102	0.154
	provisioning services	0.245	0.207	0.091	0.058	0.152
	regulating services	0.278	0.476	0.458	0.507	0.627
	cultural services	−0.017	−0.02	−0.012	−0.034	−0.011
1 km × 1 km	GEP	0.626	0.610	0.630	0.706	0.717

Local spatial autocorrelation analysis

Consistent with the global analysis, county-scale LISA maps (Figure 3) exhibited mostly insignificant or transient clustering, reaffirming that aggregate administrative units mask true spatial patterns. In contrast, the 1 km grid scale revealed a pronounced “cold spot-hot spot-cold spot” segmented pattern along the coast. Spatiotemporally, “low-low” cold spots expanded across the central-southern regions (e.g., Zhangpu, Putian) between 2008 and 2013, driven by rapid urbanization and industrialization. Conversely, “high-high” hot spots emerged and stabilized around the Xiamen and Fuzhou metropolitan circles from 2013 to 2023, which spatially coupled with the explosive growth of cultural service premiums. Notably, the northern Ningde region only exhibited “low-low” clustering during the 2003 extreme weather anomaly, rapidly diminishing thereafter to demonstrate robust ecological resilience.

Figure 3.

LISA cluster map of GEP in the Fujian coastal zone (2003–2023)

Note: EPS, ERS, and ECS represent ecosystem provisioning services, ecosystem regulating services, and ecosystem cultural services, respectively. “high-high” (Red) indicates significant spatial clusters where high values are surrounded by high values; “low-low” (Blue) indicates clusters where low values are surrounded by low values; “high-low” and “low-high” represent spatial outliers where a unit’s value differs significantly from its neighbors. “Not significance” (Gray) implies a random spatial distribution.

Coupling analysis of GEP and land use

Spatiotemporal characteristics of land use change

Observing the land use distribution map of the Fujian coastal zone from 2003 to 2023 (Figure 4), it can be found that the land use changes show significant regional heterogeneity. The land use types in the study area are mainly forest land and cropland land, with forest land widely distributed in the hilly and mountainous areas, and cropland mainly concentrated in the coastal plains and river valleys. The urban land expansion shows a spreading pattern from point to point, highly concentrated in the core cities such as Fuzhou, Xiamen, and Quanzhou as well as their neighboring districts and counties. Meanwhile, the loss of cropland mainly occurs in the agricultural areas of coastal plains and river valleys, and its spatial distribution is highly coincident with the urban expansion areas. In order to explore the inner law of land use change, this study adopts the transfer matrix method to quantitatively analyze the land use conversion process from 2003 to 2023 (Table 4), and draws the land use conversion Sankey diagram (Figure 5) to visually present the main conversion paths and flow directions. The results indicated that land use change in the study period presents the dual characteristics of urban expansion and ecological land use restructuring.

Figure 4.

Land use distribution in the Fujian coastal zone (2003–2023)

Note: The legend identifies seven land use categories distinguished by color: cropland (yellow), forest (dark green), shrub (green), grassland (light green), water (blue), barren (beige), and urban land (pink).

Table 4.

Land use transfer matrix of the Fujian coastal zone from 2003 to 2023 (unit: km²)

Year	Land Use Type	2023							Total
		Cropland	Forest	Shrub	Grassland	Water	Barren	Urban Land
2003	cropland	6,123.45	982.7	0.01	3.63	62	2.43	1,108.30	8,282.51
	forest	1,241.71	15,211.77	0.82	1.04	2.08	0.08	75.73	16,533.24
	shrub	1.7	6.36	1.9	0.52	0	0	0.02	10.5
	grassland	19.35	12.65	0.01	3.75	1.23	1.21	15.34	53.56
	water	172.71	6.52	0	2.35	557.52	7.12	189.99	936.21
	barren	2.65	0.07	0	0.15	0.41	2.15	4.68	10.11
	urban land	7.72	0.24	0	0.09	20.45	0.21	1,785.71	1,814.41
Total		7,569.29	16,220.31	2.75	11.53	643.7	13.21	3,179.77	27,640.54

Figure 5.

Land use transition in the Fujian coastal zone (2003–2023)

Note: The diagram visualizes the transition of land use types over five period nodes. The vertical bars represent the total area of each land use type in the respective year, and the numbers indicate the specific area values (Unit: km²). The colored flows between the bars represent the source and destination of land use conversion, with the width of the flow lines proportional to the transferred area. The colors correspond to the seven land use types: cropland (yellow), forest (dark green), shrub (olive), grassland (light green), water (blue), barren (beige), and urban land (red). (Figure S1).

Driven by rapid urbanization, urban land surged by 75.25% (from 1,814.41 to 3,179.77 km²). Specifically, cropland and water bodies constituted 81.2% (1,108.30 km²) and 13.9% (189.99 km²) of the newly expanded urban footprint respectively, highlighting an intense spatial squeeze on foundational ecological assets.

Consequently, cropland and water bodies suffered the most severe net losses. Water bodies shrank drastically from 936.21 to 643.70 km², driven by both urban encroachment and tidal flat reclamation. Meanwhile, forest land—the largest spatial component—maintained a dynamic large-scale equilibrium with cropland through frequent mutual conversions. For instance, while 1,241.71 km² of forest was cleared for agriculture, 982.70 km² of cropland successfully reverted to forest, demonstrating the localized effectiveness of the grain-for-green program. In summary, the evolution of the study area’s land use spatial pattern primarily manifested as urban land encroaching upon farmland and water bodies, alongside mutual conversion between farmland and forest land. This reflects the combined impact of rapid urbanization and regional ecological conservation policies on the land use pattern.

Analysis of GEP contribution by land use type

Through Zonal Statistics, we quantified the specific GEP contributions of diverse land-use types (Table 5). As shown in Table 5, forests and water bodies functioned as the absolute ecological pillars. Except for the impact of abnormal weather in 2003, Forest ecosystems consistently provided the largest GEP share (reaching 43.68% by 2023) with stable growth. Conversely, while Water bodies ranked second, their proportional contribution suffered a sharp and continuous decline, plunging from 43.73% in 2003 to 24.31% in 2023. Meanwhile, driven by explosive cultural service premiums and continuous spatial expansion, the GEP contribution of urban land more than quadrupled, with its share expanding from 4.65% to 10.38%. These structural shifts intuitively reflect how land-use adjustments fundamentally reshape the composition of regional GEP (Figure S1).

Table 5.

GEP and proportion of different land use types in the Fujian coastal zone (2003–2023) (unit: Million CNY/%)

Land Use Type	2003	2008	2013	2018	2023
Cropland	106,451.76 (18.49)	131,742.23 (17.08)	154,863.84 (18.42)	219,023.76 (21.13)	235,413.61 (21.53)
Forest	189,647.09 (32.95)	315,095.42 (40.84)	341,476.09 (40.62)	430,038.38 (41.49)	477,567.97 (43.68)
Shrub	100.59 (0.02)	137.31 (0.02)	94.27 (0.01)	84.22 (0.01)	79.20 (0.01)
Grassland	778.32 (0.14)	1,393.05 (0.18)	827.97 (0.10)	599.41 (0.06)	445.87 (0.04)
Water	251,709.51 (43.73)	281,979.71 (36.55)	282,164.13 (33.56)	281,728.36 (27.18)	265,827.07 (24.31)
Barren	166.31 (0.03)	323.15 (0.04)	522.65 (0.06)	719.35 (0.07)	484.71 (0.04)
Urban Land	26,773.07 (4.65)	40,790.67 (5.29)	60,712.39 (7.22)	104,320.31 (10.06)	113,474.66 (10.38)
Total	575,626.65 (100)	771,461.54 (100)	840,661.32 (100)	1,036,513.80 (100)	1,093,293.08 (100)

Note: Values in parentheses represent the proportion (%) of the GEP in the corresponding column.

Intensity of GEP change driven by land use transfer per unit area

To eliminate the influence of area differences among varying land use transfers on GEP changes and accurately quantify the driving mechanisms of land-use changes on GEP, this study calculated the unit area GEP change intensity matrix (Table 6).

Table 6.

GEP change intensity matrix per unit area in the Fujian coastal zone (2003–2023) (unit: Million CNY/km²)

Year	Land Use Type	2023							Total
		Cropland	Forest	Shrub	Grassland	Water	Barren	Urban Land
2003	cropland	17.77	14.42	0.00	19.28	178.44	17.28	20.65	18.96
	forest	17.66	18.45	20.73	14.42	159.62	12.50	13.14	18.39
	shrub	15.88	16.35	17.89	23.08	0	0	0.00	16.95
	grassland	19.12	12.89	0.00	22.13	172.36	15.70	22.95	22.40
	water	−136.71	−128.83	0	−141.28	121.02	−147.61	−144.79	15.09
	barren	29.43	57.14	0	33.33	160.98	25.12	30.34	34.42
	urban land	15.16	12.50	0	22.22	161.27	4.76	20.93	22.48
Total		14.23	18.14	18.55	−12.49	128.07	−70.63	10.77	18.73

As shown in Table 6, the transition paths to urban land were the most intense, exposing the dual driving mechanisms of rapid urbanization.

First, urban expansion drives a severe physical depletion of high-value ecosystems. Specifically, the conversion from Water bodies to urban land exhibited an extreme negative change intensity, causing a net GEP loss of 144.79 million CNY/km². This quantitatively proves that the ecological regulating functions of aquatic ecosystems are virtually irreplaceable by subsequent urban development.

Second, urban expansion triggers a deceptive “value substitution trap.” Notably, the transition paths from Cropland and Forest to urban land achieved a seemingly counter-intuitive net growth in unit area GEP (+20.65 million CNY/km² and +13.14 million CNY/km,² respectively). This does not imply ecological improvement; rather, it reveals a profound structural driving mechanism: rapid urbanization drastically inflated monetized cultural service premiums (e.g., real estate and coastal tourism). Under the aggregate accounting framework, this massive cultural increment mathematically masked the concurrent loss of foundational provisioning and regulating capacities. This matrix directly quantifies how land-use transitions drive GEP evolution, highlighting that policymakers must scrutinize structural trade-offs rather than relying solely on total net growth.

In summary, the unit area GEP change intensity matrix quantitatively reveals the impact of different land use transition processes on regional GEP. It not only quantifies the cost of encroaching on key ecosystems such as water bodies but also uncovers the complex value substitution mechanism behind urban expansion and its potential impact on GEP changes, while validating the feasibility of ecological restoration projects.

Identification of ecological economic functional zones

Clustering results of functional zoning

Based on the K-means clustering analysis of the density of provisioning, regulating, and cultural services in each county in 2023, combined with the elbow method, the optimal number of clusters (K) was determined to be 5. Visualizations via heatmap (Figure 6) and radar chart (Figure 7) reveal profound structural heterogeneity across the coastal zone, effectively delineating five typical ecological-economic functional zones (Figure 8).

• (1)Zone I: high-value composite zone (Dongshan County). Characterized by peak densities in both provisioning and regulating services, its radar chart presents a full and balanced morphology. This zone represents an optimal synergistic model of fishery production, ecological conservation, and tourism, successfully realizing the comprehensive marketization of ecological products. It serves as an optimal practice sample of the concept that “lucid waters and lush mountains are invaluable assets.”
• (2)Zone II: cultural service-led zone (Xiamen urban area). Dominated by extreme cultural service premiums (standardized value near 1.0) with negligible provisioning functions. This typifies a highly urbanized economic model where tertiary industries drive aggregate GEP, reflecting the withdrawal of agricultural and fishery functions in highly urbanized areas.
• (3)Zone III: ecologically imbalanced zone (Shishi City). Exhibits a severely distorted “high output, low regulation” structure. While cultural and provisioning densities are high, its regulating service density (0.07) ranks at the absolute bottom. This zone epitomizes the “value substitution trap,” where high-intensity industrial and urban expansion severely overdraws foundational environmental capacities.
• (4)Zone IV: Regulation Conservation Zone (9 counties/districts, including Ningde, Pingtan, Xiapu). Structurally skewed toward regulating services, with densities significantly above the regional average. This zone functions as the indispensable northern green barrier, undertaking critical water and climate regulation to maintain the baseline of regional ecological security.
• (5)Zone V: Comprehensive Potential Zone (12 counties/districts, including Quanzhou, Zhangzhou, Putian). Features a balanced but moderate density across all services without prominent advantages. This may be related to the dilution of unit density caused by the large administrative areas of some regions, or the fact that industrial ecological transformation is still in its infancy. Consequently, this zone possesses significant potential for quality improvement and efficiency enhancement.

Figure 6.

Heatmap showing the spatial differentiation of normalized ecosystem service intensity across coastal counties in Fujian Province

Figure 7.

Radar chart illustrating the service structure characteristics of the five ecological-economic functional zones in the Fujian coastal zone

Figure 8.

Spatial distribution of ecological-economic functional zones in the Fujian coastal zone

Spatial distribution patterns of functional zones

Spatially (Figure 8), the ecological economic functional zones in the Fujian coastal zone exhibit significant north-south differentiation characteristics. The northern coast (predominantly the Ningde region) is extensively clustered with the Zone IV Regulation Conservation Zone. Benefiting from mountainous terrain, dense forest coverage, and minimal urban disturbance, this continuous stretch functions as a robust ecological security barrier. Conversely, the southern coast—centered on the highly urbanized “Golden Triangle” (Xiamen-Zhangzhou-Quanzhou)—presents a complex and fragmented spatial mosaic. Its core consists of the Xiamen urban area (Zone II) and Shishi City (Zone III), forming a high-intensity development nexus characterized by peak cultural agglomeration but severe regulatory scarcity. Surrounding this nexus, vast expanses (e.g., Zhangzhou, Quanzhou) act as Zone V hinterlands, providing buffer and support capacity. Notably, Dongshan County (Zone I) emerges as an isolated high-value ecological-economic highland within this southern matrix, demonstrating the spatial feasibility of optimal synergistic development.

Discussion

Spatiotemporal evolution characteristics and structural transformation risks of GEP

From 2003 to 2023, the GEP of the Fujian coastal zone showed a significant growth trend; however, profound structural transformation and crisis are concealed behind this quantitative growth. The growth of GEP was not driven by the enhancement of the foundational functions of the ecosystem (i.e., regulating services) but rather by the stable growth of provisioning service value and the explosive growth of cultural service value.

Specifically, the value of provisioning services increases steadily from 155,574.49 million CNY in 2003 to 290,148.32 million CNY in 2023, nearly doubling. Its proportion remained relatively stable across different years, always at around 25%. In contrast, the value of cultural services increased from 34,214.09 million CNY in 2003 to 322,536.78 million CNY in 2023, with an annual average growth rate as high as 11.87%, an increase of nearly 10-fold. Its proportion rose from 5.96% in 2003 to 29.54% in 2023. A review of statistical yearbooks reveals that the growth trends of provisioning and cultural services are consistent with the rapid development of the primary and tertiary industries in the region over the same period.

In sharp contrast, the value of regulating services, traditionally the mainstay of GEP, declines sharply from 66.95% in 2003 to 43.89% in 2023 (Table 2). This structural shrinkage is driven by two main factors. First is the “denominator effect” caused by the explosive growth of cultural services, which diluted the relative share of regulating services. Second, and more critically, is the absolute physical loss of foundational ecological spaces. Although the Mann-Kendall test showed no significant monotonic change in the total value of regulating services over the 20 years (p = 1.000), this stems mainly from an unusually low value of some regulating services due to an extreme drought in 2003. Excluding this outlier and considering the negative trend exhibited by key services such as climate regulation and water purification, it is highly likely that the overall regulation service is on a downward trend. This decline is directly linked to the massive encroachment on aquatic ecosystems; as shown in our land-use analysis, water bodies shrank from 936.21 km² to 643.70 km² due to coastal reclamation and urban expansion. This structural shift suggests that regional development is overly reliant on marketable services while fundamentally compromising the life-supporting functions of ecosystems. This finding coincides with the phenomenon of structural transformation of ecosystem service values observed by some scholars in other rapidly urbanizing coastal regions.^22,23

Spatially, GEP values generally exhibited strong positive spatial correlation (Moran’s I > 0.61) and a clustered distribution at the fine scale of the 1 km grid. LISA analysis further revealed a “cold spot-hot spot-cold spot” spatial clustering pattern. The “low-low” cold spot areas were concentrated in the central-southern region, and their range expanded between 2008 and 2013, coinciding with the period of accelerated urbanization and industrialization. The “high-high” hot spot areas were concentrated in the Xiamen and Fuzhou metropolitan circles and their peripheries (e.g., Shishi, Dongshan). The clustering intensity strengthened continuously after 2013, which was spatially coupled with the explosive growth of cultural service value during this period. Meanwhile, the northern cold spot area (Ningde City) shrank rapidly after overcoming the impact of abnormal weather in 2003, demonstrating good ecological resilience. Simultaneously, at the county scale, the strong and increasingly enhanced positive spatial correlation of regulating services (Moran’s I increased from 0.278 to 0.627) revealed that ecosystems providing core regulating functions tended to become spatially concentrated among counties, mainly within the scope of Ningde City in the north, which is related to the abundant local forest resources. Conversely, provisioning services showed weak spatial clustering among counties, while cultural services exhibited spatial randomness. This heterogeneity among service types suggests that differentiated spatial management strategies should be adopted for different service types in regional policy formulation.

Driving mechanism of land use change on GEP

Land use change is one of the direct factors driving GEP evolution. By establishing a land use transfer matrix and a unit area GEP change intensity matrix, this paper further empirically verified the complex and diverse drivers of land use change. This provides a fundamental mechanistic explanation for the spatiotemporal evolution of GEP and the formation of functional zones observed in (spatiotemporal evolution characteristics and structural transformation risks of GEP) and (spatial pattern and driving factors of ecological economic functional zones). Furthermore, it is essential to trace the macro-socioeconomic drivers behind these intense land-use shifts. The massive expansion of urban land and the shrinking of water bodies were profoundly driven by regional development strategies—specifically the construction of the “economic zone of the western straits” and rapid port industrialization, which accelerated population influx and infrastructural demands in the Fujian coastal zone.

Driven by these macro-factors, urban land expansion became the most significant characteristic of land use change during the study period. This is basically consistent with the findings of other scholars regarding the impact of urban land expansion on ecosystem service value (ESV) or GEP,^24,25 and its impact on GEP acts as a “double-edged sword.”

On one hand, the expansion of urban construction land directly encroached upon high-value cropland and water body ecosystems. In particular, the conversion path from water bodies to urban land resulted in a GEP change of −144.79 million CNY/km.² This quantifies the huge ecological damage caused by the destruction of important aquatic ecosystems such as wetlands and tidal flats. Comparing our findings with existing literature reveals that this physical depletion represents a universal challenge across China’s coastal gradient. For instance, in the northern Yellow River Delta, the expansion of artificial aquaculture ponds caused the shrinkage of natural tidal wetlands, weakening the regulating function of water bodies,²⁶ in the central Yangtze River Delta urban agglomeration, studies have similarly confirmed that rapid urbanization and high-intensity land development were the main causes of the continuous decline in ESV between 2006 and 2020. Moreover, high-value areas (such as woodlands) are highly sensitive to land development; once encroached upon, the value loss is often irreversible. Similar to the Yangtze River Delta, for the Fujian coastal zone, the value of regulating services lost due to the encroachment on aquatic ecosystems far exceeds the cultural services added by urban construction. This provides direct data support for delineating coastal ecological red lines and implementing strict controls.

On the other hand, this study identified a significant value substitution trap. In the conversion path from cropland land and forest land to urban land, GEP still shows a net increase (+20.65 million CNY/km² and +13.14 million CNY/km,² respectively). This does not imply an improved ecological environment; rather, the urbanization process generated immense monetized cultural premiums (e.g., coastal tourism and real estate), which numerically offset the physical loss of regulating and provisioning capacities. This dynamic mirrors the structural trade-offs observed in other highly developed estuaries. For example, the ecological evolution of the Yellow River Delta similarly demonstrated that while aquaculture inflated provisioning values, it implicitly degraded original wetland functions. Moreover, we must critically acknowledge that this surge in cultural service value comes with unmeasured negative environmental externalities. Dense tourist activities inevitably increase carbon footprints, coastal water pollution, and habitat disturbance for marine biodiversity. Currently, the standard GEP accounting framework does not deduct these “ecological costs.” This limitation exacerbates the value substitution trap, as the calculated cultural value may be an overestimation that masks simultaneous environmental degradation. This phenomenon reminds us that when interpreting the ecological impacts of urbanization, it is important not to focus only on GEP indicators, but also to look at the depletion of resources and the loss of basic ecological functions caused by internal structural changes.

At the same time, the study validated the high payback potential of ecological restoration pathways. The most effective way to enhance regional GEP is to convert wasteland into water bodies or forests (Table 6). Forests and water bodies are major contributors to GEP in coastal Fujian, and implementing land management measures that prioritize these key natural assets will genuinely amplify ecological benefits without falling into the value substitution trap.

Spatial pattern and driving factors of ecological economic functional zones

Based on GEP density and structural types, the five ecological economic functional zones identified by K-means clustering represent a comprehensive synthesis of the spatiotemporal heterogeneity of GEP. Their formation mechanism is the concrete manifestation of the combined effects of GEP internal structural transformation and spatial differentiation on geographical units, clearly depicting the status of spatial differentiation in the Fujian coastal zone under the interaction between natural endowments and human activities.

• (1)The “Dongshan sample” of synergistic development: the formation of zone I (high-value composite zone) benefits from its unique “ecology+” industrial path. This proves that high-intensity fishery supply and tourism development do not necessarily lead to the degradation of regulating services; the key lies in the degree of industrial ecologization.
• (2)The “double-edged sword” effect of urbanization: zone II (Xiamen) and zone III (Shishi) are both high-intensity development zones but exhibit significant differentiation. Xiamen has achieved the dominance of cultural services through industrial upgrading, while Shishi has retained high-intensity industry, trade, and fisheries, leading to an excessive ecological load. This difference reflects the reshaping role of different urbanization stages on the ecosystem service structure.
• (3)The “geopolitical pattern” of north-south differentiation: zone IV (regulation conservation zone) is concentrated in the northern Ningde region. This is the spatial realization of the “strong clustering of regulating services at the county scale” (Moran’s I reached 0.627) observed in spatiotemporal evolution characteristics and structural transformation risks of GEP. Influenced by terrain constraints (mountainous with few plains) and policy orientation (ecological barrier positioning), this region has retained a relatively complete natural background. Meanwhile, the vast southern region (Zone V) is trapped in the “comprehensive potential zone,” reflecting that during the wave of rapid industrialization, many counties have not yet found a characteristic path for realizing ecological product value and remain in a state of low-efficiency equilibrium.

Policy implications and management insights

Based on our spatiotemporal analysis and the identified value substitution trap, simply restricting land development is insufficient to resolve the conflict between regional economic growth and ecological protection. To effectively internalize ecological externalities, it is essential to build a comprehensive policy framework that integrates scientific accounting, institutional synergy, and market-based transformation, tailored to the specific functional zones of the Fujian coastal area.

The prerequisite for sustainable ecological management lies in establishing a rigorous accounting system to prevent the illusion of “apparent growth.” For cultural service-led zones (zone II, e.g., Xiamen), policymakers must recognize that the monetized surge in cultural GEP does not negate the associated environmental externalities, such as the carbon footprints of tourism. Rather than conflating these metrics, regional assessments must introduce an “ecological footprint” (EF) cross-reference mechanism alongside GEP. This dual-ledger approach ensures that the monetized surge in cultural services does not overshadow the underlying environmental costs unique to high-intensity coastal development. Concurrently, for ecologically Imbalanced zones (zone III, e.g., Shishi) undergoing high-intensity industrial development, natural resource departments must mandate a “GEP impact assessment.” Developers must quantitatively restore or financially compensate for the specific regulating services they encroach upon, ensuring that physical ecological assets are protected at the source.

To address the spatial mismatch of ecological benefits, institutional innovations are urgently needed to facilitate cross-regional cooperation and equitable cost-sharing. Highly urbanized areas like zone II should leverage their fiscal advantages to establish a “Bay area ecological compensation fund.” Using the unit-area regulating GEP deficit calculated in this study as a pricing baseline, these cities can purchase regulating service quotas (e.g., water purification) from upstream comprehensive potential zones (zone V), thereby balancing regional environmental carrying capacities through horizontal fiscal transfer payments. Furthermore, local organizational departments must optimize performance evaluations by enforcing a “GDP + GEP dual-target appraisal system.” Setting a rigid minimum threshold for the proportion of regulating services ensures that regulation conservation zones (zone IV, e.g., Ningde) are politically and financially supported to maintain regional ecological security without sacrificing their development rights.

Ultimately, translating conserved ecological assets into sustainable economic returns requires localized market mechanisms customized for different resource endowments. For the high-value composite zone (zone I, e.g., Dongshan), local governments should pioneer a GEP-backed green credit system, allowing enterprises to use certified “abalone farming—seaweed carbon sink” assets as collateral for green financing. Similarly, the ecological barriers in zone IV should operationalize “blue carbon tickets” for coastal mangroves and salt marshes, linking these tickets to the national carbon emission trading market to provide a sustained financial return. Finally, to overcome low-level equilibrium in zone V, local governments should establish “two mountains” resource aggregation platforms (Liangshan Bank). Functioning as centralized hubs for ecological resource collection, enhancement, and trading, these platforms can package fragmented rural ecological assets into large-scale, standardized modern agriculture and eco-tourism projects, successfully attracting social capital while strictly guarding ecological bottom lines.

Limitations of the study

While this study has achieved certain research results, there are still some limitations in some aspects due to the limitations in data acquisition, accounting methodology and depth of analysis. First, there is inherent uncertainty in the assessment of non-market ecological values. Because GEP accounting relies heavily on substitute market methods (e.g., using industrial oxygen prices or reservoir construction costs as proxy parameters), the selection of these socioeconomic parameters can influence the absolute GEP values. To enhance the robustness of the accounting framework, future research should introduce one-at-a-time (OAT) sensitivity analyses—such as systematically adjusting key price parameters by ±50% to calculate the coefficient of sensitivity (CS). This standard parameter perturbation approach will help quantitatively evaluate whether the GEP estimation is elastic or inelastic to specific parameter uncertainties. Second, in terms of driving mechanisms, this study mainly focuses on the impact of land use, and future studies should incorporate more natural and socio-economic drivers, such as temperature, rainfall, national policies, industrial structure, population density, and GDP per capita, etc., and conduct more in-depth attribution analyses using advanced methods such as geo-detectors and regression models. Third, the current analyses are limited to retrospective features of patio-temporal evolution. Future work should endeavor to project GEP dynamics under different future climate change scenarios. Finally, given the high complexity of coastal zone ecosystems, future research should endeavor to combine dynamic policy scenarios with disaster risk assessment, expand interdisciplinary approaches, and refine methods for identifying ecological function zones, so as to provide more systematic decision support for the sustainable development of Fujian’s coastal zone.

Conclusions

This study systematically calculated the GEP of the Fujian coastal zone from 2003 to 2023 and analyzed the coupling relationship between its spatiotemporal evolution characteristics and land use changes. The main conclusions are as follows.

• (1)Aggregate GEP growth masked profound structural imbalances. While GEP doubled during the study period, its internal composition underwent a fundamental transformation. The proportion of foundational “implicit value” (regulating services) dropped drastically from 66.95% to 43.89%, whereas market-oriented “explicit value” (cultural services) climbed rapidly. This growth model indicates a potential ecological deficit risk, where aggregate volume increases mask the underlying degradation of life-supporting functions.
• (2)Urban expansion triggered a significant “value substitution trap.” The study verified that urbanization is the core driver of GEP structural changes. Specifically, the expansion of construction land by encroaching on water bodies caused a severe physical loss of regulating services (−144.79 million CNY/km²). Conversely, transitioning cropland to urban land yielded an “apparent” net GEP increase (+20.65 million CNY/km²) driven entirely by massive monetized cultural premiums. This empirically proves that relying solely on aggregate GEP assessments can mislead judgments regarding regional ecological security.
• (3)Structural clustering identified five distinct ecological-economic functional zones. Moving beyond traditional classifications based on total land area or gross value, K-means clustering based on service structures clearly portrayed the spatial heterogeneity of the Fujian coast. The identified zones effectively distinguish diverse development models, ranging from high-value composite zones (achieving synergy between supply and regulation) to ecologically imbalanced zones (facing severe regulatory deficits) and regulation conservation zones (serving as ecological barriers).
• (4)Sustainable coastal planning requires structure-based spatial strategies. To resolve the conflict between economic development and ecological protection, coastal management must transcend aggregate targets. Achieving genuine sustainable development requires implementing differentiated spatial control strategies and establishing structural ecological compensation mechanisms tailored to the unique resource endowments of these five functional zones.

Resource availability

Lead contact

Requests for further information and resources should be directed to and will be fulfilled by the lead contact, Zhongsheng He (jxhzs85@fafu.edu.cn).

Materials availability

This study did not generate new unique reagents.

Data and code availability

• •All data reported in this paper will be shared by the lead contact upon request.
• •This paper does not report original code.
• •Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.

Acknowledgments

We sincerely appreciate the data support provided by the Fujian Provincial Bureau of Statistics. We also gratefully acknowledge the assistance from the Fuzhou Municipal Bureau of Statistics, Fuzhou Forestry Bureau, Fuzhou Agriculture and Rural Affairs Bureau, Fuzhou Municipal Bureau of Water Resources, Zhangzhou Municipal Bureau of Statistics, Zhangzhou Municipal Bureau of Water Resources, Putian Municipal Bureau of Water Resources, and Ningde Municipal Bureau of Water Resources. Finally, the authors record sincere appreciation for the helpful and constructive comments made by the anonymous reviewers of the draft manuscript. This research was funded by the Natural Resource Science and Technology Innovation Project of Fujian Province, grant no KY-030000-04-2025-020; Fujian Province Forestry Science and Technology Project of China, grant no 2024FKJ32; Zhangzhou Institute of Surveying and Mapping Project, grant no KH240129A; Key Laboratory of Southeast Coast Marine Information Intelligent Perception and Application, Ministry of Natural Resources of the People's Republic of China, grant no 23202.

Author contributions

Data curation, X.L., X.Y., P.H., L.L., and C.Z.; formal analysis, X.L., X.Y., G.L., Y.X., and J.L.; investigation, X.L., X.Y., J.L., R.W., and Y.X.; methodology, X.L., X.Y. and Z.H.; writing – original draft, X.L. and X.Y.; funding, Z.H.; writing – review and editing, X.L., X.Y., J.L., and Z.H.

Declaration of interests

The authors declare no competing interests.

STAR★Methods

Key resources table

REAGENT or RESOURCE	SOURCE	IDENTIFIER
Deposited data
China Land Cover Dataset (CLCD)	Wuhan University	https://zenodo.org/records/18180184
Land Cover Type Product (MCD12Q1 V6.1)	NASA Earthdata	https://www.earthdata.nasa.gov/data/catalog/lpcloud-mcd12q1-061
SRTM Digital Elevation Model (DEM)	NASA Earthdata	https://www.earthdata.nasa.gov/data/catalog/lpcloud-srtmgl1-003
1-km monthly precipitation dataset for China (1901–2024)	National Tibetan Plateau Data Center	https://data.tpdc.ac.cn/zh-hans/data/faae7605-a0f2-4d18-b28f-5cee413766a2
ERA5 Daily air temperature data	ECMWF	https://cds.climate.copernicus.eu/datasets/reanalysis-era5-single-levels-timeseries?tab=overview
Landsat 5/8/9 satellite images	USGS	https://www.usgs.gov/landsat-missions/landsat-data-access
MODIS NPP product (MOD17A3HGF)	NASA Earthdata	https://www.earthdata.nasa.gov/data/catalog/lpcloud-mod17a3hgf-061
Harmonized World Soil Database (HWSD v2.0)	FAO	https://www.fao.org/soils-portal/data-hub/soil-maps-and-databases/harmonized-world-soil-database-v20/en/
China Reservoir Dataset (CRD)	NIGLAS, CAS	https://doi.org/10.5281/zenodo.6984619
Statistical yearbooks of Fujian Province	Statistics Bureaus of Various Regions in Fujian Province	https://data.cnki.net
Water Resources Bulletins of Fujian Province	Water Conservancy Bureaus of various regions in Fujian Province	https://slt.fujian.gov.cn/
Technical Guidelines for GEP Accounting	Fujian Provincial Department of Ecology and Environment	https://sthjt.fujian.gov.cn/zwgk/zfxxgkzl/zfxxgkml/mlstbh/202109/P020210917571834853463.pdf
Administrative division data	RESDC, CAS	https://www.resdc.cn/
Software and algorithms
ArcGIS 10.8	ESRI	https://www.esri.com/en-us/home
Origin 2021	OriginLab	https://www.originlab.com/2021
Google Earth Engine (GEE)	Google	https://developers.google.com/earth-engine

Method details

GEP accounting framework

Scientifically and reasonably constructing a regional GEP accounting system is the primary task for assessing regional GEP.²⁷ Based on the Technical Guidelines for Gross Ecosystem Product (GEP) Accounting in Fujian Province (Trial) and existing GEP accounting research, this study constructed a GEP accounting system suitable for the coastal zone of Fujian Province (Table S1). This system was developed by closely integrating the unique ecosystem functions and socio-economic development status of the region, while fully considering data availability and operational feasibility. The framework covers three core functional categories provided by ecosystems for human well-being: provisioning services, regulating services, and cultural services.^28,29

In this study, the GEP was calculated as the sum of the values of provisioning, regulating, and cultural services. The core calculation formula is as follows:

$$\begin{array}{c}GEP=EPS+ERS+ECS\end{array}$$ (Equation 1)

Where: GEP represents the gross ecosystem product (Million CNY); EPS represents the value of Ecosystem Provisioning Services; ERS represents the value of Ecosystem Regulating Services; and ECS represents the value of Ecosystem Cultural Services.

Regarding provisioning services, the Fujian coastal zone possesses abundant resources in agriculture, forestry, animal husbandry, and fisheries. Meanwhile, water resources serve as the fundamental guarantee for regional sustainable development. Therefore, this study selected “agriculture, forestry, animal husbandry, and fishery products” and “water resource products” as the core secondary accounting indicators to reflect the material product value directly provided by the ecosystem.

Regarding regulating services, given the complex terrain and diverse ecosystem types in the study area, which face a series of ecological issues such as soil erosion, flood disasters, and water pollution, this study selected 10 secondary indicators: “soil conservation,” “flood regulation,” “water conservation,” “water purification,” “climate regulation,” “carbon sequestration,” “atmospheric purification,” “air quality maintenance,” “coastal protection,” and “biodiversity conservation.” This selection aims to comprehensively and systematically reflect the critical role of the Fujian coastal ecosystem in maintaining environmental stability, mitigating natural disasters, and ensuring ecological security.

Regarding cultural services, the Fujian coastal zone boasts unique natural scenery and rich ecological resources. The cultural service function refers to the process by which ecosystems provide humans with leisure, entertainment, eco-tourism, cultural experiences, and spiritual enjoyment through their unique natural landscapes, biodiversity, and cultural heritage.³⁰ Therefore, this study selected “tourism and sightseeing” as the core secondary indicator for cultural services.

The detailed GEP accounting system and parameters are presented in Table S1 in the Supplemental Information.

GEP accounting methods

This study adopts the functional value approach as the main method for GEP accounting.³¹ The core premise of the approach is to select appropriate valuation techniques to monetize the value of different services provided by ecosystems. Specifically, in order to assess the sub-indicators in the accounting system, a combination of valuation techniques such as the market value approach, the alternative cost approach, the shadow engineering approach, and the treatment cost approach are utilized. This integrated approach aims to quantify the economic value of each ecosystem service in a scientifically sound manner.

Provisioning service value

In this study, the provisioning services of the Fujian coastal ecosystem were categorized into five major types: agricultural, forestry, animal husbandry, and fishery products, as well as water resources. The actual yield of each product was used as the indicator for physical supply, and the market value method was applied to calculate the economic value. The specific calculation formula is as follow:

$$\begin{array}{c}{V}_S=\sum _{i=1}^5\left(M_{Si}\times P_i\right)\end{array}$$ (Equation 2)

Where: V_S represents the total value of provisioning services provided by the Fujian coastal ecosystem; M_Si denotes the annual yield of the i-th product type (agricultural, forestry, animal husbandry, fishery products, and water resources) (t/a); Pi represents the average unit price of the i-th product type (CNY/t).

Soil conservation value

This study assessed the physical amount of soil conservation by calculating the difference between potential soil erosion and actual soil erosion. The Revised Universal Soil Loss Equation (RUSLE),³²which is widely applied, was used to estimate soil erosion. Subsequently, the replacement cost method was employed to calculate its economic value. The specific formulas are as follows:

$$\begin{array}{c}{V}_{SW}=\lambda \times \frac{A}{\rho }\times P_{RD}\end{array}$$ (Equation 3)

$$\begin{array}{c}{A}_c={SR}_P-{SR}_A=R_z\times K_z\times L_z\times S_z-R_z\times K_z\times L_z\times S_z\times C_z\times P_z\end{array}$$ (Equation 4)

Where: V_sw is the value of soil conservation services; λ is the sediment deposition coefficient, set at 24%³³; A_c is the amount of soil conservation; ρ is the soil bulk density, taken as 1.297 (t/m³) according to the Technical Guidelines for Gross Ecosystem Product (GEP) Accounting; P_RD is the cost of reservoir dredging; SR_P and SR_A represent potential soil erosion and actual soil erosion, respectively; R_z, K_z, L_z, S_z, C_z and P_z represent the rainfall erosivity factor, soil erodibility factor, slope length factor, slope steepness factor, vegetation cover factor, and support practice factor, respectively.

Flood regulation value

Reservoirs, wetlands, and lakes within the Fujian coastal ecosystem all possess flood regulation functions.³⁴ However, given constraints regarding data availability and estimation methods, this study only assessed the flood regulation function of large and medium-sized reservoirs and wetlands, employing the shadow engineering method to calculate their economic value. The specific formulas are as follows:

$$\begin{array}{c}{V}_{fm}=P_{we}\times \left(M_{fs}+M_{cc}\right)\end{array}$$ (Equation 5)

$$\begin{array}{c}{M}_{fs}=0.35\times C_t\end{array}$$ (Equation 6)

$$\begin{array}{c}{M}_{cc}=2.47\times {10}^6\times S+1\times S\times {10}^6\end{array}$$ (Equation 7)

Where: V_fm is the value of flood regulation; P_we is the construction cost per unit of reservoir capacity (CNY/m³), taken as 30.79 CNY/m³ based on the Fujian GEP pilot accounting report; M_fs is the flood control storage capacity of reservoirs (m³); C_t is the total storage capacity of reservoirs (m³); M_cc is the flood regulation capacity of marshes/wetlands (10⁴ m³);S is the wetland area (m²).

Water conservation value

This study adopted the water balance method to calculate the physical quantity of water conservation.³⁵The shadow engineering method was then employed to estimate its economic value, using the construction cost per unit of reservoir capacity as the price parameter. The specific formulas are as follows:

$$\begin{array}{c}{Q}_{wr}=\sum _{i=1}^n{A}_i\times \left(P_i-R_i-E{T}_i\right)\times {10}^{-3}\end{array}$$ (Equation 8)

$$\begin{array}{c}{V}_{wr}=Q_{wr}\times P_{we}\end{array}$$ (Equation 9)

Where: V_wr is the total value of water conservation services; Q_wr is the total physical quantity of water conservation (m³);A_i is the area of the i-th land use type (m²); P_i, R_i and ET_i represent the annual precipitation, annual surface runoff, and annual actual evapotranspiration of the i-th land use type (mm), respectively; P_we is the construction cost per unit of reservoir capacity (CNY/m³).

Water purification value

This study primarily assessed the purification capacity of water bodies for Total Nitrogen (TN), Total Phosphorus (TP), and Chemical Oxygen Demand (COD). The replacement cost method (specifically, the treatment cost method) was employed to calculate the economic value. The specific formulas are as follows:

$$\begin{array}{c}{V}_{WP}=M_P\times P_P+M_N\times P_N+M_{COD}\times P_{COD}\end{array}$$ (Equation 10)

$$\begin{array}{c}{M}_i=A\times Q_i\end{array}$$ (Equation 11)

Where: V_WP is the value of water purification services; M_P、M_N and M_COD represent the functional purification amounts of the aquatic ecosystem for Total Phosphorus, Total Nitrogen, and COD, respectively; A is the wetland area; Q_i is the purification capacity of the wetland for the i-th water pollutant; P_P、P_N and P_COD represent the treatment costs per unit volume for Phosphorus, Nitrogen, and COD, respectively.

Climate regulation value

Climate regulation value refers to the function of ecosystems in improving local climate conditions by absorbing heat through vegetation transpiration and water body evaporation.³⁶ This study adopted the replacement cost method for assessment. The core concept equates the natural cooling effect provided by ecosystems to the electrical energy value consumed by artificial climate regulation measures (i.e., air conditioning). The specific formulas are as follows:

$$\begin{array}{c}{V}_{tt}=E_{tt}\times P_e\end{array}$$ (Equation 12)

$$\begin{array}{c}{E}_{tt}=E_{pt}+E_{we}\end{array}$$ (Equation 13)

$$\begin{array}{c}{E}_{pt}=\sum _i^{3}EP{P}_i\times S_i\times D\times \frac{{10}^6}{3600\times r}\end{array}$$ (Equation 14)

$$\begin{array}{c}{E}_{we}=E_w\times q\times \frac{{10}^3}{3600\times r}\end{array}$$ (Equation 15)

Where: V_tt is the value of ecosystem climate regulation services; E_tt represents the total energy consumed by transpiration and evaporation of the terrestrial ecosystem (kW·h); E_pt is the energy consumed by vegetation transpiration (kW·h); E_we is the energy consumed by water body evaporation (kW·h); EPP_i is the heat consumed by transpiration per unit area of the i-th ecosystem type(kJ⋅m⁻²⋅d⁻¹); S_i is the area of the i-th ecosystem type(km²); r is the energy efficiency ratio (EER) of air conditioners; D is the number of days air conditioning is required, calculated as the average number of days in the study area with a daily mean temperature above 26°C; E_w is the annual evaporation volume of water bodies (m³); q is the latent heat of vaporization of water, i.e., the heat required to evaporate 1g of water (J/g); i represents the ecosystem type, including forest, shrub, and grassland; P_e is the electricity price in Fujian Province.

Carbon sequestration value

Ecological carbon sequestration service refers to the function of ecosystems in absorbing and storing carbon dioxide through photosynthesis to mitigate climate change. This study employed the carbon sequestration mechanism model to estimate the physical quantity of carbon sequestration based on Net Primary Productivity (NPP) data. The market value method was then used to calculate its economic value based on the average trading price in the Fujian carbon market. The specific formulas are as follows:

$$\begin{array}{c}{Q}_{tc}=Q_{fwc}+Q_{sc}\end{array}$$ (Equation 16)

$$\begin{array}{c}{Q}_{fwc}=\frac{M_{C{O}_2}}{M_C}\times NEP\end{array}$$ (Equation 17)

$$\begin{array}{c}{Q}_{sc}=A\times K\end{array}$$ (Equation 18)

$$\begin{array}{c}NEP=\alpha \times NPP\times \frac{M_{C_6}}{M_{C_6{H}_{10}{O}_5}}\end{array}$$ (Equation 19)

$$\begin{array}{c}{V}_{Cf}=Q_{tc}\times C_C\end{array}$$ (Equation 20)

Where: Q_tc is the total amount of carbon sequestration by the ecosystem (t); Q_fwc is the carbon sequestration amount of forest, wetland, and grassland ecosystems (t); Q_sc is the carbon sequestration amount of cropland (t); A is the cropland area (m²); $\frac{M_{C{O}_2}}{M_C}$ is the coefficient for converting C to CO₂ (44/12); NEP is the Net Ecosystem Productivity (t·C); K is the carbon sequestration rate of cropland, taken as 0.38 t/hm²; V_Cf is the value of terrestrial ecosystem carbon sequestration (CNY); C_C is the carbon price (CNY/t); α is the conversion coefficient between NEP and NPP; NPP is the Net Primary Productivity (t·dry matter); $\frac{M_{C_6}}{M_{C_6{H}_{10}{O}_5}}$ is the coefficient for converting dry matter to C.

Oxygen release value

The accounting of physical quantity for oxygen release services is closely related to carbon sequestration services. Based on the stoichiometric relationship of photosynthesis (the molecular weight ratio of CO₂ to O₂ is 44:32), the physical quantity of oxygen released is derived from the calculated amount of carbon dioxide fixed. Subsequently, the replacement cost method was employed to calculate the economic value, using the market price of industrial oxygen production as the replacement unit price. The specific formulas are as follows:

$$\begin{array}{c}{Q}_{O_2}=\frac{M_{O_2}}{M_{C{O}_2}}\times Q_{C{O}_2}\end{array}$$ (Equation 21)

$$\begin{array}{c}{V}_{O_2}=Q_{O_2}\times C_{O_2}\end{array}$$ (Equation 22)

Where: $V_{O_2}$ is the value of oxygen release services; $M_{O_2}/M_{C{O}_2}$ is the conversion coefficient of CO₂ to O₂, taken as 32/44; $Q_{C{O}_2}$ is the amount of carbon sequestration by the ecosystem; $C_{O_2}$ is the price of industrial oxygen.

Air purification value

Air purification service refers to the function of ecosystems (especially vegetation) in reducing atmospheric pollutants (such as sulfur dioxide, nitrogen oxides, and particulate matter) through processes like leaf adsorption, absorption, and under-canopy deposition, thereby improving air quality. This study adopted the replacement cost method to calculate this value, taking the treatment cost required to remove an equivalent amount of atmospheric pollutants through artificial means (e.g., industrial desulfurization, denitrification, and dust removal) as the economic value. The specific formulas are as follows:

$$\begin{array}{c}{Q}_{ai}=\sum _{j=1}^n{q}_{ai}\times A_j\end{array}$$ (Equation 23)

$$\begin{array}{c}{V}_{ap}=\sum _{i=1}^n{Q}_{ai}\times C_{gi}\end{array}$$ (Equation 24)

Where: V_ap is the value of air purification services; Q_ai is the purification amount of the i-th atmospheric pollutant (kg); q_ai is the purification amount per unit area of the i-th pollutant by the j-th ecosystem type (kg/m²); A_j is the area of the j-th ecosystem type (m²);C_gi is the treatment cost of the i-th atmospheric pollutant (CNY/kg).

Coastal protection value

Coastal protection function refers to the ability of wetland ecosystems (such as mangroves and salt marshes) to protect the coastline from erosion by attenuating waves and resisting storm surges. This study selected the area of offshore and coastal wetlands in Fujian Province as the physical quantity for coastal protection. The replacement cost method was used to calculate its economic value, using the construction and maintenance cost per unit area of seawall protection engineering as the replacement price. The specific formula is as follows:

$$\begin{array}{c}{V}_{cl}=M_{cl}\times P_{cl}\end{array}$$ (Equation 25)

Where: V_cl is the value of coastal protection services; M_cl is the total coastal area protected by the ecosystem (hm²), i.e., the wetland area; P_cl is the construction and maintenance cost per unit area of seawall protection engineering (CNY/hm²).

Biodiversity conservation value

Nature reserves play a crucial role in protecting biodiversity and promoting the sustainable development of ecosystems. For marine nature reserves, the replacement cost method (i.e., seawall protection engineering construction cost) was applied to assess the value of ecosystems such as coastal salt marshes, mangroves, and coral reefs in wind protection and dike consolidation. For forest nature reserves, the Shannon-Wiener index was adopted for assessment.³⁷ The specific formula is as follows:

$$\begin{array}{c}{V}_{bi}=M_{biop}\times P_{biop}+M_{bio}\times P_{bio}\end{array}$$ (Equation 26)

Where: V_bi is the value of biodiversity conservation; M_biop is the physical quantity of marine species conservation, i.e., the area of marine nature reserves (hm²); P_biop is the seawall protection engineering construction cost for marine nature reserves (CNY/hm²); M_bio is the physical quantity of forest species conservation, i.e., the area of forest nature reserves (hm²); P_bio is the biodiversity conservation value per unit area of forest.

Cultural service value

Cultural service function refers to the process by which ecosystems provide humans with leisure, recreation, eco-tourism, and spiritual enjoyment. This function plays a vital role in eco-tourism and cultural industries and is a source of regional economic vitality. This study adopted the market value method to calculate this value. Referring to existing studies, 70% of the total tourism revenue of each district and county was taken as the proxy for its cultural service value.³⁸

Quantification and statistical analysis

Time series trend analysis

In order to quantitatively characterize the temporal evolution of GEP in the coastal region of Fujian from 2003 to 2023, this study adopts the Mann-Kendall (M-K) trend test.³⁹The M-K test, as a robust nonparametric statistical method, does not require the data to satisfy specific distributional assumptions, and is also effective in counteracting outlier disturbances. The method is effective in identifying trends in time series data. The direction of the trend is determined by the Z statistic: Z > 0 indicates an upward trend and Z < 0 indicates a downward trend. The trend significance test uses the α = 0.05 significance level, and the trend is considered statistically significant when p < 0.05. The specific mathematical expressions are as follows:

$$\begin{array}{c}S=\sum _{i=1}^{n-1}\sum _{j=i+1}^n\text{sgn}\left(x_j-x_i\right)\end{array}$$ (Equation 27)

$$\begin{array}{c}\text{sgn}\left(\theta \right)=\{\begin{array}{cc}+1& \text{if}\theta >0\\ 0& \text{if}\theta =0\\ -1& \text{if}\theta <0\end{array}\end{array}$$ (Equation 28)

$$\begin{array}{c}\text{Var}\left(S\right)=\frac{n\left(n-1\right)\left(2n+5\right)-\sum _{p=1}^g{t}_p\left(t_p-1\right)\left(2t_p+5\right)}{18}\end{array}$$ (Equation 29)

$$\begin{array}{c}Z=\{\begin{array}{cc}\frac{S-1}{\sqrt{\text{Var}\left(S\right)}}& \text{if}S>0\\ 0& \text{if}S=0\\ \frac{S+1}{\sqrt{\text{Var}\left(S\right)}}& \text{if}S<0\end{array}\end{array}$$ (Equation 30)

Multi-scale Spatial analysis

The spatial distribution of GEP is often accompanied by significant spatial heterogeneity; however, the characteristics of spatial patterns may vary according to the scale of analysis, i.e., there is a certain scale effect. The traditional analysis method based on administrative districts, such as county-level administrative districts, although easy to integrate with socio-economic statistics, often hides the real spatial distribution characteristics due to the artificial demarcation of administrative boundaries and irregularities in the shape and area of districts.

In order to overcome the scale effect and reveal the spatial variation pattern of GEP in coastal Fujian more comprehensively, this study adopts a multi-scale spatial analysis framework. With the help of GeoDa software, the spatial autocorrelation analysis method in the exploratory spatial data analysis (ESDA) method⁴⁰ was applied to explore the spatial distribution of GEP at two different scales, namely, the county scale and the 1 km × 1 km grid. A spatial weight matrix was constructed based on the adjacency relationship using the Queen Contiguity rule.

Global Spatial autocorrelation

Global spatial autocorrelation reflects the overall spatial distribution characteristics of GEPs across the entire study area, encompassing clustering, dispersion, and random distribution. This study employs the global Moran’s I index for analysis. The Moran’s I index ranges from −1 to 1. A value greater than 0 indicates positive spatial correlation (clustering), less than 0 indicates negative spatial correlation (dispersion), and a value close to 0 indicates random spatial distribution. Statistical significance is tested using Z-values and p-values. The calculation formula is as follows:

$$\begin{array}{c}{\text{Moran}}^{\prime }\mathrm{s}\mathrm{I}=\frac{n\sum _{i=1}^n\sum _{j=1}^n{w}_{ij}\left(x_i-\overline{x}\right)\left(x_j-\overline{x}\right)}{\left(\sum _{i=1}^n\sum _{j=1}^n{w}_{ij}\right)\sum _{i=1}^n{\left(x_i-\overline{x}\right)}^2}\end{array}$$ (Equation 31)

Where: n is the total number of spatial units (number of counties or grid cells); x_iand x_jare the GEP values of the i-th and j-th spatial units, respectively; $\overline{x}$ is the average GEP value of all units; w_ijis the spatial weight matrix.

Local Spatial autocorrelation

Based on global spatial autocorrelation analysis, this study employs the Local Moran’s I index (LISA) to further identify local spatial clustering patterns.^41,42 LISA analysis detects association patterns between each spatial unit and its neighborhood, thereby identifying statistically significant “hot spots,” “cold spots,” and spatial outliers. Visualizing these patterns through LISA cluster maps reveals four primary types of local spatial associations: high-high clusters (hot spots), low-low clusters (cold spots), high-low outliers, and non-significant areas. This study conducted LISA analyses on GEP data at both county-level and 1-kilometer grid scales to compare local spatial pattern characteristics across different scales. The calculation formula is as follows:

$$\begin{array}{c}LocalMora{n}^{\prime }sI=\frac{\left(x_i-\overline{x}\right)}{S_i^2}\sum _{j=1,j\ne i}^n{W}_{ij}\left(x_j-x\right)\end{array}$$ (Equation 32)

$$\begin{array}{c}{S}_i^2=\frac{\sum _{j=1,j\ne i}^n{\left(x_j-\overline{x}\right)}^2}{n-1}-x^2\end{array}$$ (Equation 33)

Where: n is the total number of spatial units (number of counties or grid cells); x_i and x_j are the GEP values of the i-th and j-th spatial units, respectively; $\overline{x}$ is the average GEP value of all units; w_ij is the spatial weight matrix; S_i² is the variance of GEP values.

Coupling analysis of GEP and land use change

Land use transfer matrix

The land use transfer matrix is a classic method for revealing the spatiotemporal evolution process and direction of regional land use.^43,44 This matrix quantitatively describes the area and flow direction of mutual conversions between different land use types within the study period (beginning and end), thereby clearly reflecting the evolution characteristics of land use structure.

This study applies the matrix to the Fujian coastal region, revealing the conversion pathways and spatial extent of various land use types between 2003 and 2023. Based on this analysis, it examines the driving effects of land use change on regional ecological product value. Its mathematical expression is as follows:

$$\begin{array}{c}{S}_{ij}=\left(\begin{array}{cccc}{S}_{11}& S_{12}& \cdots & S_{1n}\\ S_{21}& S_{22}& \cdots & S_{2n}\\ ⋮& ⋮& \ddots & ⋮\\ S_{n1}& S_{n2}& \cdots & S_{nn}\end{array}\right)\end{array}$$ (Equation 34)

Analysis of GEP contribution by land use type

To quantify the contribution of different land use types to the GEP and identify the land use types that play a key supporting role, an analysis of GEP contribution by land use type was conducted. Using land use raster data from 2003 to 2023 as “Zone Data” and the corresponding GEP total value raster as “Value Raster,” the “Zonal Statistics as Table” tool in ArcGIS was utilized to calculate the GEP and its proportion for each land use type across different periods in batches. This allowed for the assessment of the contribution degree of different land use types to the GEP.

Analysis of unit area GEP change intensity

Unlike traditional indicators that merely report absolute net value changes—which are inherently skewed by the vast area differences of various land-use types—this study introduced the concept of “Unit Area GEP Change Intensity.” The theoretical necessity of this index lies in its ability to mathematically filter out area-scale interference. To more objectively compare the relative intensity of different land use transfer paths on GEP impact and achieve a standardized comparison per square kilometer, a net GEP change matrix (ΔGEPij) was constructed through overlay analysis in ArcGIS, which was then divided by the corresponding elements in the land use area transfer matrix (ΔS_ij). This yielded the unit area GEP change intensity matrix. The specific formula is as follows:

$$\begin{array}{c}{Intensity}_{ij}=\frac{\Delta GE{P}_{ij}}{\Delta S_{ij}}\end{array}$$ (Equation 35)

Where: Intensity_ij is the unit area GEP change intensity (CNY/km²); ΔGEP_ij is the net change in GEP for the transfer path from land use type i to land use type j (CNY); ΔS_ij is the total area of this transfer path (km²).

Identification of ecological-economic functional zones

The administrative areas of districts and counties in the Fujian coastal zone vary significantly; thus, directly comparing the GEP values would result in scale effects, making it difficult to reveal their respective ecological-economic functions. To overcome this scale effect, this study calculated GEP density (Unit: CNY/km²) by dividing the GEP of each district or county from 2003 to 2023 by its administrative area. The introduction of the GEP density concept allows for comparable value output intensity across different regions. Furthermore, to investigate internal structural differences in GEP, the concepts of provisioning service value density, regulating service value density, and cultural service value density were introduced. Finally, to objectively and scientifically identify the ecological-economic development models of different regions in the Fujian coastal zone, the K-Means Clustering Algorithm was employed to construct the functional zoning system.

Indicator selection and data standardization

To ensure that the functional zoning results reflect current realities and provide future guidance, this study focused on data from 2023, which represents the latest regional ecological-economic pattern. The provisioning service value density, regulating service value density, and cultural service value density of each district (county) in 2023 were selected as the three core clustering indicators. Since the magnitudes of different service values vary significantly (e.g., cultural service value is often much higher than regulating service value), the Z Score standardization method was applied to eliminate dimensional influence. The formula is as follows:

$$\begin{array}{c}{z}_{ij}=\frac{x_{ij}-{\mu }_j}{{\sigma }_j}\end{array}$$ (Equation 36)

Where: z_ij is the standardized value; x_ij is the original density value of the j-th service for the i-th district/county; μ_jis the mean value of the j-th service; σ_jis the standard deviation of the j-th service.

K-means cluster analysis

K-Means is a classic unsupervised learning algorithm that partitions samples into K clusters through iterative calculations, minimizing intra-cluster variance and maximizing inter-cluster variance.⁴⁵ This study implemented the clustering using the Scikit-learn library on the Python platform. To determine the optimal number of clusters (K), the Elbow Method, Silhouette Coefficient, and the interpretability of ecological-economic meanings were comprehensively considered. After testing, K = 5 was found to most clearly identify typical development models with significant heterogeneity. Finally, based on the numerical characteristics of the cluster centers (Centroids), the functional zones were defined and named.