Analysis of Green Development Dynamics and Influencing Factors in Daihai Basin

Abstract

Green development accounting provides the theoretical basis and data support for national or regional ecological civilization constructions. The Daihai Basin, located in Ulanqab City, Inner Mongolia Autonomous Region, is not only an important ecological barrier in the region but also one of the 179 nationally important wetlands under the “China Wetland Conservation Action Plan”. It plays a crucial role in maintaining regional ecological balance, providing ecological services such as water conservation, climate regulation, and biodiversity protection. Taking Daihai Basin as the study area, an accounting system of Green Gross Domestic Product (GGDP), Gross Ecosystem Product (GEP), and Gross Economic–Ecological Product (GEEP) was constructed to explore the temporal variation characteristics of GGDP, GEP, and GEEP in the study area from 1989 to 2022. The results were as follows: (1) During the study period, the overall GGDP in Daihai Basin showed an increasing trend, with an increase of CNY 3.812 billion in the past 30 years, of which GGDP increased from 1989 to 2011 and decreased from 2011 to 2022. In addition to GDP, the ecological damage cost was the most important factor influencing GGDP in the Daihai Basin, and the most significant one is the ecological damage to the wetland. (2) The GEP in the study area decreased, with a decrease of CNY 1.066 billion in the past 30 years. However, the conversion value of “Two Mountains” increased year by year. During the study period, the ecological regulation service value, which was dominated by climate regulation and water conservation, decreased year by year. (3) The GEEP in the study area showed a fluctuating change, with an overall upward trend, reaching a maximum of CNY 9.011 billion in 2011. (4) Except for 2011, during the study period, GEEP > GEP > GGDP in Daihai Basin, but the main driving factors of the three indicators were different, and the variation trends with time were different. The results of this research can provide a decision-making basis for the high-quality development of the Daihai Basin and provide reference cases for the green development accounting of other basins.

1. Introduction

Green development has become a global consensus due to the growing prominence of climate change and environmental issues. Countries around the world have committed to green transformation. The “green economy” and “low-carbon economy” advocated by the United Kingdom [1], the “clean production mechanism” promoted by Japan [2], and the “green new deal” and “green finance” proposed by the United States [3,4] are all typical representatives of green development, which have been recognized and respected by the United Nations. As the world’s largest developing country, China upholds the concept of a community of human destiny and actively participates in global environmental and climate governance. In 2011, the Outline of the 12th Five-Year Plan for National Economic and Social Development formally raised the issues of green development and combating climate change [5]. On 19 January 2023, the Information Office of the State Council of China released a white paper on “China’s Green Development in the New Era” [6], emphasizing the concept that green water and green mountains are golden silver mountains and unswervingly taking the path of ecological priority and green development to promote a comprehensive green transformation of economic and social development. It can be seen that green development has become an inevitable choice for China’s development.

Scholars have carried out useful explorations in this regard. Green Gross Domestic Product (GGDP) is based on the principles of sustainable development, deducting the costs of resource consumption and environmental pollution from the Gross Domestic Product (GDP) to provide a more comprehensive reflection of regional development levels [7,8,9,10]. With societal progress, more factors such as environmental governance, wealth distribution, protective expenditures, and ecological footprints have been incorporated into accounting systems. To further explore new evaluation methods that integrate resource consumption, ecological environmental protection, and improvements in residents’ welfare with economic development, the World Conservation Union and the Chinese Academy of Sciences’ Ecological Research Center jointly proposed the concept of Gross Ecosystem Product (GEP). GEP represents the total economic value of the final products and services provided by ecosystems for human well-being [11,12,13]. GEP includes ecological product supply services, regulation services, and cultural services, but it does not fully integrate the ecosystem and economic system into the accounting framework [14]. The report of the 18th National Congress of the Communist Party of China explicitly stated that resource consumption, environmental damage, and ecological benefits should be incorporated into the economic and social development evaluation system [15]. Accordingly, in 2018, the Ministry of Ecology and Environment proposed the new concept of Gross Economic–Ecological Product (GEEP). GEEP deducts the costs of ecological environmental degradation caused by human economic activities from GDP and adds the ecological benefits provided by ecosystems [16,17]. It can be said that GDP accounts for “gold and silver mountains”, GEP accounts for “green waters and green mountains”, and GEEP combines both “green waters and green mountains” with “gold and silver mountains”.

The greening of watersheds is an important path to promote green development in China. Although China’s watershed water environment protection has made historic progress, and the physical and chemical indicators of the water environment are close to the level of developed countries, there are still deep-rooted problems such as water–ecological imbalance, insufficient ecological water use, and aggravation of surface source pollution [18]. Since the 18th CPC National Congress, the CPC Central Committee and the State Council have attached great importance to the management of the watershed ecological environment. Various regions have also actively explored and practiced the construction of green watersheds. In 2019, China’s Ministry of Ecology and Environment issued the Technical Outline for the Preparation of the “14th Five-Year Plan” for water–ecological environmental protection in key basins, which, for the first time, adjusted “water pollution prevention and control” to “water–ecological environment protection”. It clearly puts forward the need to highlight the integration of water resources, water ecology, and water environment, synergistically promote the development and utilization of water resources, water environment quality improvement, and water ecology protection, and promote the green development of the watershed. Current research on green development accounting mainly focuses on provincial [19,20], municipal [21], and county-level scales [22]. There is relatively little research on green development accounting in watershed areas, and comparative studies on the similarities and differences in the accounting of GGDP, GEP, and GEEP indicators are scarce.

The Daihai Basin, as an important ecological barrier in the Inner Mongolia Autonomous Region, is one of the 179 nationally important wetlands under the “China Wetland Conservation Action Plan”. During China’s two sessions in 2018, General Secretary Xi Jinping instructed, while attending the deliberations of the Inner Mongolia delegation, to “accelerate the comprehensive treatment of water ecology in Lake Hulun, the Wuliangsu Lake, and the Daihai Lake”. In 2019, General Secretary Xi Jinping, while attending the deliberations of the Inner Mongolia delegation, once again emphasized the need to grasp the comprehensive ecological management of Lake Hulun, the Wuliangsu Lake, and the Daihai Lake and to prescribe the right medicine for the problem and effectively implement it [23]. In 2020, the government of Inner Mongolia opened the “Yellow River Diversion Project to Daihai Lake” and other construction projects in order to increase the amount of water resources in Daihai Lake, improve water quality, and curb the trend of ecological degradation to protect the health of the Daihai ecosystem [24]. The comprehensive estimation of the GGDP, GEP, and GEEP accounting systems for the Daihai Basin can provide a decision-making basis for the high-quality development of the Daihai Basin and serve as a reference case for green development accounting in other basins. Given this, this study draws on existing accounting systems and research results, taking the Daihai Basin as the research object, constructs a framework and methodology for GGDP, GEP, and GEEP accounting, and calculates the GGDP, GEP, and GEEP for the study area from 1989 to 2022, aiming to provide a theoretical basis for the assessment of green development levels and the formulation of green development policies in the Daihai Basin.

2. Materials and Methods

2.1. Study Area Overview

The Daihai Basin (40°11′–40°48′ N, 112°16′–112°59′ E) is located in Ulanqab City, Inner Mongolia Autonomous Region, which administers Liangcheng County, Fengzhen City, and Zhuozi County, covering parts of these counties with an area of 2341.7 km² (Figure 1). This region has a typical temperate continental climate, with an annual precipitation of 410.1 mm, and the terrain is mainly mountainous and hilly. The vegetation types in the Daihai Basin are diverse, primarily consisting of Poaceae, Chenopodiaceae, and Asteraceae, with dominant species including Suaeda glauca, Phragmites communis, Artemisia scoparia, Stipa krylovii, Leymus chinensis, Leymus secalinus, Tamarix chinensis, Carex duriuscula, and Artemisia frigida.

2.2. Construction of GGDP, GEP, and GEEP Accounting Systems for the Daihai Basin

Referring to the “Technical Guidelines for Green GDP (GGDP) Accounting (Trial)” [25], “Technical Guidelines for Gross Ecosystem Product (GEP) Accounting” [26], and “Technical Guidelines for Gross Economic–Ecological Product (GEEP) Accounting (Trial)” [27] issued by the Chinese Academy of Environmental Planning and based on the GEP and GEEP accounting indicators of the Inner Mongolia Autonomous Region, the GGDP, GEP, and GEEP accounting systems for the study area were constructed in combination with watershed characteristics.

2.2.1. GGDP, GEP, and GEEP Accounting Systems for the Daihai Basin

The GGDP of the study area is obtained by subtracting environmental degradation and ecological damage costs from GDP. GEP is obtained by summing the values of ecological product supply, ecological regulation, and ecological cultural services. GEEP is obtained by adding GGDP and ecological regulation value (Figure 2). Environmental degradation mainly includes air pollution, water pollution, and soil pollution, while ecological damage mainly includes damage to forests, grasslands, wetlands, and farmland caused by human activities. The sum of ecological product supply and ecological, cultural service values is used to measure the value of converting “green waters and green mountains” into “gold and silver mountains”, referred to as the “Two Mountains” conversion value. Discount rates are used to modify the calculations to eliminate the effects of economic factors such as time, inflation, and prices. The GDP and prices in this paper are the values when discounted to 2022. On the basis of fully considering the socio-economic and environmental characteristics of the Daihai Basin, the GEP accounting framework was constructed with 11 first-level indicators and 15 s-level indicators, and the GEP accounting framework was constructed with 11 first-level indicators and 15 s-level indicators, such as aquatic product supply, water conservation, climate regulation, and so on, with full consideration of the social and economic environment characteristics of the Daihai Basin [28] (

Table 1. Calculation methods of indicators in GGDP, GEP, and GEEP accounting systems in Daihai Basin.

Functional Category	Characterization of the Object of Accounting		Accounting Formulas	Description of Formulas
	Level 1 Indicators	Tier 2 Indicators
Environmental degradation costs	Costs of air pollution control	SO2	APC = Pi × Ci	APC is the cost of air pollution control; Pi is the pollution emission of the type ith pollutant (SO2, NOx, dust); Ci is the treatment cost of the type ith pollutant (SO2, NOx, dust);
		NOx
		fine dust
		life cleaning	Ch = H × GDPpc × (α − α0)/100	Ch is the cost of domestic cleaning; H is the total number of households in the city; GDPpc is the city’s GDP per capita; α is the coefficient of cleaning costs (α0 = 0.876)
	Costs of water pollution control	wastewater from agricultural cultivation	WPCa = QR × Ci QR = W × λ × f	WPCa is the cost of agricultural cultivation wastewater treatment; Ci is the cost of pollutant treatment; QR is the pollution load of agricultural cultivation water pollution; W is the number of population in the watershed; λ is the pollution production coefficient; f is the inlet coefficient of the lake
		livestock farming wastewater	WPCl = QA × Ci QA = A × λ × f	WPCl is the treatment cost of livestock and poultry wastewater; Ci is the cost of pollutant treatment; QA is the pollution load of livestock and poultry water pollution; A is the number of livestock and poultry in the watershed; λ is the pollution production coefficient; f is the inlet coefficient of the lake
	Costs of soil pollution control	Soil remediation of agricultural land	APS = ${\sum }_i^3{APS}_i\times {AC}_i$	APS is the cost of soil remediation on agricultural land; ${APS}_i$ is the contaminated area of agricultural land with different levels of contamination, and ${AC}_i$ is the cost of remediation of contaminated agricultural land per unit area with different levels of contamination
Cost of ecological damage	Value of losses from forest degradation		EcDC = ERSf × HRf + ERSg × HRg + ERSw × HRw + ERSl × HRl HRf = ${\displaystyle \frac{FO}{FR}}={\displaystyle \frac{FC-FCQ}{FR}}$ HRg = ${\displaystyle \frac{1.0}{1.0+29.875\times {0.143}^x}}$ HRl = ${\displaystyle \frac{S_d}{S_l}}$	EcDC is the ecological damage loss; ERSf, ERSg, ERSw, and ERSl are the ecological regulation services provided by forest, grassland, wetland, and farmland ecosystems, respectively HRf is the anthropogenic damage rate of forests; FO is the forest overexploitation rate (China’s forest overexploitation rate is 0.82% [29]); FR is the forest stock; FC is the amount of forest harvest, and $FCQ$ is the limit of forest harvesting HRw is the rate of anthropogenic damage to wetlands; HRg is the rate of anthropogenic damage to grasslands; x is the rate of livestock overloading in grasslands; HRl is the rate of anthropogenic damage to cropland area; Sd is the area of cropland converted to other uses such as construction land, and Sl is the total area of cropland.
	Value of losses from grassland degradation
	Lost value of wetland degradation
	Value of loss from farmland degradation
Ecological Product Supply Services	Agricultural products	Agricultural products	Vp = $\sum _{i=1}^n{E}_i\times V_i$	Vp is the total value of ecological product supply services; $E_i$ is the output of the type ith ecological product (agricultural product, livestock product, aquatic product); $V_i$ is the price of the type ith product (agricultural product, livestock product, aquatic product)
	Livestock Product	Livestock Product
	fishery product	fish production
Ecological regulation services	water conservation	water conservation	Vwr = Qwr × Cwr	Vwr is the value of the water source in the study area (CNY·a−1); Qwr is the amount of water source in the study area calculated using the InVEST model (m3·a−1); Cwr is the engineering cost of reservoir construction (taken as CNY·8.26·m−3)
	soil conservation	Reduce sedimentation	Vsr = Vsd + Vdpd Vsd = μ × (Qsr/ρ) × c Vdpd = $\sum _{i=1}^n{Q}_{sr}\times D_i\times G_i$	Vsr is the ecosystem soil retention value (CNY·a−1); Vsd is the value of reducing sediment siltation (CNY·a−1); Vdpd is the value of reducing surface source pollution (CNY·a−1); Qsr is the amount of soil retention (t·a−1); c is the cost of desilting project for a unit of reservoir (taken as CNY·m−3); ρ is the soil capacity weight (t·m−3); μ is the coefficient of sediment siltation; Gi is the pure content of nitrogen, phosphorus and other nutrients in the soil; Di is the treatment cost
	sequester carbon and release oxygen	carbon sequestration and oxygen release	Vg = Qc × Pc + Qo × Po	Vg is the value of oxygen release from carbon sequestration (CNY·a−1); Qc is the amount of carbon sequestered in the study area calculated using the InVEST model (t·a−1); Pc is the cost of carbon sequestration (CNY·t−1); Qo is the amount of oxygen release based on carbon sequestration (t·a−1); Po is the cost of oxygen production (CNY·t−1)
	Atmospheric clarification	To purify sulfur dioxide	Va = Qa × Ca Qa = $\sum _{i=1}^m{\Sigma }_{j=1}^n{Q}_{ij}\times S_i$	Va is the cost of atmospheric purification (CNY); Qa is the total amount of atmospheric pollutants purified (t); Ca is the cost of atmospheric pollutants purified (CNY·t−1); Qij is the amount of atmospheric pollutants of category j (SO2, NOx, industrial dust) absorbed per unit area of ecosystems of category i (forests, grasslands, wetlands, farmlands) (t·km−2·a−1); Si is the area (km2) of ecosystems of category i (forests, grassland, wetland, agricultural land) area (km2)
		Purification of nitrogen oxides (NOx)
		Purifying industrial dust
	Water purification	Purification COD	Vw = ${\sum }_{i=1}^n{Q}_{wpi}\times C_i$ Qwpi = $\sum _{i=1}^n{P}_i$	Vw is the value of ecosystem water quality purification (CNY·a−1); Qwpi is the amount of purification of water pollutants of category i (COD, ammonia nitrogen, total phosphorus) (t·a−1); Ci is the unit treatment cost of water pollutants of category i (COD, ammonia nitrogen, total phosphorus) unit treatment cost (CNY·t−1); Pi is the discharge (kg·a−1) of pollutant category i (COD, ammonia nitrogen, total phosphorus)
		Purification of ammonia nitrogen
		purification of total phosphorus
	climate regulation	plant evaporation	$\mathrm{Q}={\displaystyle \frac{{\sum }_i^{3}GPP\times S_i\times d}{3600\times R\times 2}}$ + (EQ × q × 103/3600 + EQ × γ) V = Q × P	Q is the energy consumed by ecosystem transpiration and evaporation (kwh); GPP is the heat consumed by transpiration per unit area of different ecosystem types (kJ·m−2·d−1); Si is the area of the type ith ecosystem type (forest, grassland, wetland, farmland) (m2); R is the energy–efficiency ratio of air conditioning: 3.0; d is the number of air conditioning open days (days); EQ is the evaporation of water surface (m3); q is the volatilized latent heat, taken as 2453.2 J·g−1; γ is the power consumption (kWh) of the humidifier to convert 1 m3 of water into steam, taken as 120. V is the value of climate regulation (CNY·a−1); P is the price of electricity (CNY·kWh−1)
		water surface evaporation
Ecocultural services	Total annual tourism revenue	Total annual tourism revenue	Vcs = Rt	Vcs is the value of cultural services; Rt is the total annual tourism revenue

Note: For livestock pollution, a discounting method was used where 3 sheep = 1 pig equivalent and 1 cow = 5 pig equivalents [30].

).

2.2.2. Calculation Methods for GGDP, GEP, and GEEP in the Daihai Basin

The specific calculation methods for each indicator in the GGDP, GEP, and GEEP accounting systems of the study area are shown in Table 1. The water conservation and soil retention amounts were calculated using the InVEST model and the Revised Universal Soil and Water Loss Equation, respectively. The net primary productivity (NPP) was calculated using the CASA model, and the carbon sequestration amount was further calculated.

• Water Conservation

  $$Y\left(x\right)=\left(1-{\displaystyle \frac{AET\left(x\right)}{P\left(x\right)}}\right)\times P\left(x\right),$$

  (1)

  where Y(x) is the water yield of grid x (mm); AET(x) is the annual actual evapotranspiration of grid x (mm); P(x) is the annual precipitation of grid x (mm).

  $${\displaystyle \frac{AET\left(x\right)}{P\left(x\right)}}=1+{\displaystyle \frac{PET\left(x\right)}{P\left(x\right)}}-{\left[1+{{\displaystyle \frac{PET\left(x\right)}{P\left(x\right)}}}^{\omega }\right]}^{{\displaystyle \frac{1}{\omega }}},$$

  (2)

  where PET(x) is the potential evapotranspiration of grid x (mm); ω is a non-physical empirical parameter representing natural climate and soil properties.

  $$\omega \left(x\right)=Z{\displaystyle \frac{AWC\left(x\right)}{P\left(x\right)}}+1.25,$$

  (3)

  where AWC(x) is the available water content of the soil (mm), determined by soil texture and effective soil depth; Z is a seasonal constant;

2.

Soil Retention

$$A_q=R·K·L·S,$$

(4)

$$A_s=R·K·L·S·C·P,$$

(5)

$$A=A_q-A_s,$$

(6)

where A is the soil retention amount (t·hm⁻²·a⁻¹); A_q is the potential soil erosion amount (t·hm⁻²·a⁻¹); A_s is the actual soil erosion amount (t·hm⁻²·a⁻¹); R is the rainfall erosivity factor (MJ·mm·hm⁻²·h⁻¹·a⁻¹); K is the soil erodibility factor (t·hm²·h·hm⁻²·MJ⁻¹·mm⁻¹·a⁻¹); L is the slope length factor (dimensionless); S is the slope factor (dimensionless); C is the cover and management factor (dimensionless); P is the support practice factor (dimensionless);

3.

Carbon Sequestration

$$C=NPP\times 1.63\times {\displaystyle \frac{12}{44}},$$

(7)

where C is the carbon sequestration amount; 1.63 is the coefficient for converting NPP to CO₂, and 12/44 is the coefficient for converting CO₂ to carbon.

2.3. Data Sources

The remote sensing image data of the study area from 1989 to 2022 were obtained from Landsat TM images provided by the United States Geological Survey (http://www.usgs.gov/, accessed on 15 January 2025). Digital elevation data (with a spatial resolution of 30 m) were obtained from the International Scientific Data Service Platform. Meteorological data were obtained from monitoring data from meteorological stations in and around the study area. National economic data, public health data, and product supply data were obtained from statistical materials such as the Inner Mongolia Statistical Yearbook, Ulanqab Statistical Yearbook, China Administrative Divisions Dictionary—Inner Mongolia Autonomous Region Volume, Liangcheng County Annals, and National Agricultural Product Cost Collection Data Compilation. Cultural service value data were obtained from survey data from the tourism management center of the study area.

Spatial calculations of ecosystem service values were performed at a 30 m × 30 m grid resolution, with results aggregated to administrative units (counties/towns) for cross-validation against statistical yearbook data, ensuring consistency between grid-scale accounting and regional statistics.

3. Results and Analysis

3.1. Spatiotemporal Dynamics of Ecosystem Services in the Daihai Basin

From 1989 to 2022, the water conservation and soil retention services in the Daihai Basin showed an increasing trend, while carbon sequestration services showed a decreasing trend. The spatial distribution characteristics of the three services were consistent across different periods. In 2022, the spatial distribution of water conservation showed a pattern of “high in the middle and low in the surrounding areas”, with the highest value being 299.2 mm (Figure 3 left). The high-value areas of soil retention were concentrated in the northwest (Figure 3 center), while the high-value areas of carbon sequestration were mainly distributed in the northwest and southeast, with low-value areas mainly distributed in the central Daihai Lake area (Figure 3 right).

3.2. Dynamics of GGDP in the Daihai Basin

From 1989 to 2011, the GGDP of the Daihai Basin increased by CNY 4.955 billion, with an increase of CNY 565 million from 1989 to 1996, a change rate of 283.79%; an increase of CNY 1.389 billion from 1996 to 2004, a change rate of 181.90%; and an increase of CNY 3.002 billion from 2004 to 2011, an increase of 139.42%. From 2011 to 2022, the GGDP of the Daihai Basin decreased by CNY 1.144 billion, with a change rate of −22.19% (Figure 4a). During the study period, the GGDP of the Daihai Basin showed the same trend as GDP, but the amplitude of GGDP changes was higher than that of GDP.

In addition to GDP, the cost of ecological damage had the greatest impact on the GGDP of the Daihai Basin, with an average of CNY 147 million per period, reaching a peak of CNY 175 million in 2001 (Figure 4b). Among the ecological damage costs, wetland ecological damage costs were the highest, averaging CNY 104 million per period and peaking at CNY 135 million in 2011. This aligns with the spatial-temporal dynamics of ecosystem services (Section 3.1), where the shrinking surface area of Daihai Lake and degradation of wetland ecosystems (evidenced by reduced water conservation and carbon sequestration services in Figure 3) directly exacerbated ecological damage losses. The economic losses from forest and grassland ecosystem damage were secondary, averaging CNY 23 million and CNY 16 million per period, respectively, reflecting milder anthropogenic impacts compared to wetlands.

In addition to GDP, the cost of ecological damage had the greatest impact on the GGDP of the Daihai Basin, with an average of CNY 147 million per period, reaching a peak of CNY 175 million in 2001 (Figure 4b). Among the ecological damage costs, wetland ecological damage costs were the highest, with an average of CNY 104 million per period, reaching a peak of CNY 135 million in 2011. The economic losses caused by forest and grassland ecosystem damage were next, with averages of CNY 23 million and CNY 16 million per period, respectively. Among the environmental degradation costs in the Daihai Basin, air pollution control costs were the highest, with an average of CNY 4.9459 million per period, increasing from 1989 to 2011 and then decreasing. Among them, SO2 control costs accounted for 48.41–90.68% of the environmental degradation costs; NOx control costs accounted for 4.36–36.54%, and dust control costs accounted for 3.76–6.43%. During the study period, water pollution control costs increased from CNY 954,600 in 1989 to CNY 1.4454 million in 2022, an increase of 51.41%. Among the water pollution control costs, agricultural planting sewage control costs accounted for the largest proportion, accounting for 53.52–58.99% of the water pollution control costs; livestock breeding sewage control costs were next, accounting for 40.77–46.28%. Agricultural land restoration costs increased over time, reaching CNY 329,100 in 2022.

3.3. GEP Accounting Results for the Daihai Basin

From 1989 to 2022, the GEP of the Daihai Basin showed an overall decreasing trend, decreasing from CNY 5.941 billion in 1989 to CNY 4.875 billion in 2022, a decrease of 17.94%. From 1989 to 2004, the average annual change rate was −1.10%, with a slight increase from 2004 to 2011, with an annual change rate of 0.13%, and from 2011 to 2022, the annual change rate was −0.33% (Figure 4a). The green gold index (GEP/GDP) of the study area decreased from 17.41 in 1989 to 0.94 in 2011 and then slightly increased to 1.17 in 2022. In terms of the composition of GEP, the value of ecological product supply accounted for 0.82–20.41%; the value of ecological, cultural services accounted for 0.02–7.53%, and the value of ecological regulation services accounted for 72.06–99.16% (Figure 5).

During the study period, the “Two Mountains” conversion value in the Daihai Basin increased over time, from CNY 50 million in 1989 to CNY 1.362 billion in 2022, with an annual change rate of 87.47%. Compared with mountainous regions dominated by forest ecosystems (e.g., Lishui City [15], where the conversion rate is 35–40%), the Daihai Basin’s lower conversion efficiency (27.93% in 2022,

Table 2. The primary conversion rate of ecological products calculated by related research.

Spot	Vintages	Primary Conversion of Ecological Products/%	The Literature
Yanqing	2016	8.60	[31]
Garze, Sichuan	2010	9.32	[32]
Alshan	2014	11.56	[13]
Yunnan	2010	17.21	[19]
Hainan Rainforest National Park	2022	17.40	[33]
Eshan	2015	22.21	[34]
Xiushui	2010	23.80	[22,35]
Sanya Wenmen Village	2017	24.93	[36]
national	2015	27.02	[11]
Foochow	2015	30.87	[37]
national	2022	34.64	[38]
Ordos	2015	41.29	[21]
Foochow	2018	42.82	[37]
Ninghai, Zhejiang	2018	46.74	[39]
Dalian	2015	61.35	[40]
Zhengzhou	2020	34.30	[41]
Hanzhong City, Shaanxi Province	2021	22.87	[42]
Weima Township, Hefeng County, Hubei Province	2021	11.79	[43]
Forest ecosystems in Chaling County, Hunan Province	2020	11.01	[44]
Wetland ecosystems of Jiangxi Province	2020	6.29	[45]
Wulong District of Chongqing	2022	67.59	[46]
Daihai of Ulanqab	2022	27.94	-

) is attributed to the dominant role of wetland regulation services (which are difficult to monetize directly) and the shrinking lake surface area, which reduced tradable ecological products like aquatic resources. The proportion of the “Two Mountains” conversion value in the GEP of the study area also increased over time, from 8.43% in 1989 to 27.93% in 2022. Among them, the value of ecological product supply increased from CNY 49 million in 1989 to CNY 995 million in 2022, with an annual change rate of 64.35%, accounting for an average of 88.86% of the “Two Mountains” conversion value over the five periods, with the proportion decreasing over time. The value of agricultural product supply increased from CNY 38 million in 1989 to CNY 351 million in 2022, an increase of CNY 313 million, but its proportion in the value of ecological product supply decreased from 77.55% in 1989 to 38.01% in 2022. The value of livestock product supply increased over time from CNY 8 million in 1989 to CNY 532 million in 2022, but its proportion in the value of ecological product supply increased from 16.32% in 1989 to 51.32% in 2022. The value of aquatic product supply showed a trend of first increasing and then decreasing, with 2004 as the turning point. During the study period, the value of ecological cultural services in the Daihai Basin increased by CNY 366 million, and its proportion in the “Two Mountains” conversion value increased from 2.00% in 1989 to 26.95% in 2022.

The value of ecological regulation services in the Daihai Basin decreased from CNY 5891 million in 1989 to CNY 3513 million in 2022, with a rate of change of −40.37%, and the proportion of GEP also decreased from 99.16% in 1989 to 72.06% in 2022. The value of climate regulation services in the study area accounted for the highest proportion of ecological regulation services, with an average proportion of 75.28% over the five periods, with the highest proportion in 1989 (82.75%) and the lowest in 2022 (67.89%), and the amount of the value declined period by period from CNY 4.875 billion in 1989 to CNY 2.385 billion in 2022. The value of water conservation services in the study area has the second highest share in the value of ecological regulation services, with an average share of 9.03% over the five periods, and its value increased from CNY 352 million in 1989 to CNY 436 million in 2022. The value of atmospheric purification services has a five-period average of 6.84% of the value of ecological regulation services in the study area, and its value volume is from CNY 252 (2011) to CNY 350 (2004) million during the study period. The value of carbon sequestration and oxygen release services averaged 6.34% of the value of ecological regulation services in the study area, and both the share and the amount of value increased slightly over the period, from 4.23% (CNY 249 million) in 1989 to 8.85% (CNY 311 million) in 2022. The value of soil conservation services and water purification services accounted for an average of 2.42% and 0.09% of the value of ecological regulating services in the study area, respectively, and the change in the amount of value over the study period was much smaller than that of other regulating services.

3.4. GEEP Accounting Results for the Daihai Basin

During the study period, the GEEP of the Daihai Basin showed a fluctuating trend, with the lowest value of CNY 5.859 billion in 1996 and the highest value of CNY 9.011 billion in 2011 (Figure 4a). From 1989 to 1996, the GEEP of the study area decreased by CNY 231 million, with a change rate of −3.80%; from 1996 to 2011, it increased by CNY 3.153 billion, showing a trend opposite to the change in ecological regulation service value during the same period; from 2011 to 2022, the GEEP of the study area decreased by CNY 1.488 billion, with a change rate of −16.51%, consistent with the change in ecological regulation service value during the same period. During the study period, the composition of GEEP in the study area changed significantly, with the proportion of GGDP in GEEP increasing from 3.27% in 1989 to 57.20% in 2011 and then decreasing to 53.31% in 2022.

3.5. Correlation Analysis of Various Indicators with Social, Economic, and Environmental Factors

To further explore the factors influencing the changes in GGDP, GEP, and GEEP, a correlation analysis was conducted between climate, social, and economic factors and the areas of various ecosystem types and these three indicators (Figure 6). As can be seen from Figure 6, there are different degrees of correlations between various factors and different indicators. Among them, the surface area of Daihai Lake is significantly positively correlated with GEP, indicating that changes in the area of Daihai Lake have an important impact on GEP. The area of wetland ecosystems in the basin is significantly positively correlated with GGDP and GEEP, highlighting the importance of wetland ecosystems in regional green development. Moreover, economic factors such as the development scales of the primary, secondary, and tertiary industries, and GDP per capita, as well as climate factors such as annual mean ground temperature, annual precipitation, annual mean temperature, and annual relative humidity, also have complex relationships with each indicator. These relationships provide a multi-dimensional perspective for a deeper understanding of regional green development.

4. Discussion

In 2022, the GGDP, GEP, and GEEP per unit area in the study area were 1.7127 million CNY·km⁻², CNY 2.0818 million·km⁻², and CNY 3.2130 million·km⁻², respectively. Compared with existing studies, the GGDP, GEP, and GEEP per unit area in other regions ranged from CNY 1.591 to CNY 13.345 million·km⁻² [47,48,49], CNY 1.387 to CNY 56.339 million·km⁻² [11], and CNY 3.720 to CNY 890 million·km⁻² [50], respectively. The green development levels in the Daihai Basin were relatively low. This result may be due to the relatively low economic development level of the region where the basin is located compared to other study areas and the severe shrinkage of the lake surface area in the basin, which increased the cost of ecological damage. Accounting results have some credibility, and the indicator system constructed in this study can achieve long-term green development accounting at the watershed scale.

By calculating the GGDP index as 100 times the ratio of GGDP to GDP, the GGDP index of the Daihai Basin showed an increasing trend over time during the study period, consistent with the national GGDP index trend [47]. This indicates that influenced by national development policies, the Daihai Basin has increasingly emphasized resource conservation and environmental protection while developing its economy, reducing negative resource and environmental externalities while growing the economy.

The sum of ecological product supply and ecological cultural service values can reflect the effectiveness of “green waters and green mountains” conversion, and the percentage of this value in GEP is referred to as the primary conversion rate of ecological products [38]. In this study, the primary conversion rate of ecological products increased over time, from 0.84% in 1989 to 27.94% in 2022, but it was still relatively low compared to other research results (Table 2). This may be related to the low value of ecological product supply and cultural services. The property rights of ecological product supply and cultural services are clear and can be directly traded in the market, but the realization process depends on the market rules and policy support of infrastructure, public services, etc. Although the value of ecological product supply and cultural services in the study area has increased period by period, they are still at a low level, which indirectly indicates that the infrastructure, public service market, and related policies in the study area have been greatly improved during the study period, but still need to be strengthened. Regulation services have the attributes of public goods, with low exclusivity and low competitiveness, and their value realization often requires government governance models such as ecological compensation and government procurement. The value of regulation services in the study area decreased over time, which is related to the reduction in the surface area of Daihai Lake, the increase in ecological fragility, and the slight increase in pollution levels in the basin.

Comparing the three accounting indicators in the study area, only in 2011 was GEEP greater than GGDP and GGDP greater than GEP, while in other periods, GEEP was greater than GEP and GEP was greater than GGDP, but the temporal trends of the three were not consistent. This indicates that the three indicators have different emphases when measuring regional green development levels. Although GGDP considers resource consumption and environmental damage during economic development, it is still mainly influenced by GDP. GEEP adds regional ecosystem service value to GGDP, but when GDP is much higher than ecosystem service value, GEEP is still mainly influenced by GDP. After 1996, the GEEP of the Daihai Basin showed the same trend as GGDP. GEP aims to account for the ecological well-being provided by ecosystems to humans, including product supply services, ecological regulation services, and cultural services and is not directly influenced by GDP. At the same time, the surface area of Daihai Lake was significantly positively correlated with GEP, and the area of wetland ecosystems in the basin was significantly positively correlated with GGDP and GEEP, indicating that Daihai Lake and its lakeside wetland ecosystems are of great significance to regional green development.

5. Conclusions

This study uses GGDP, GEP, and GEEP to evaluate the green development level of the Daihai Basin from 1989 to 2022 and draws the following conclusions:

(1) From 1989 to 2022, the GGDP of the Daihai Basin first increased and then decreased. In addition to GDP, ecological damage had the greatest impact on the GGDP of the Daihai Basin, with the largest proportion being the loss caused by damage to wetland ecosystems;

(2) The GEP of the Daihai Basin showed an overall decreasing trend, with the “Two Mountains” conversion value increasing over time and the value of ecological regulation services decreasing. The ecological regulation services in the study area were dominated by climate regulation and water conservation;

(3) GGDP, GEP, and GEEP have different emphases when measuring regional green development levels. The GGDP and GEEP of the Daihai Basin still largely depend on GDP, while GEP is less affected by GDP and is significantly positively correlated with the surface area of Daihai Lake.

In addition, this study has certain limitations, and future research can be carried out in the following directions: First, deeply explore the driving mechanisms of the changes in the ecosystem service values in the Daihai Basin, clarify the specific impact degrees of natural and human factors, and provide a basis for formulating precise ecological protection policies. Second, based on the results of this study, construct a green development prediction model applicable to the Daihai Basin, predict the trends of regional green development under different scenarios, and provide forward-looking references for sustainable development planning. Third, conduct a comparative analysis of the green development models of the Daihai Basin and other ecologically similar basins, draw on successful experiences, explore a characteristic green development path suitable for the Daihai Basin, and enhance the regional green development level.