An Economic Valuation of Forest Carbon Sink in a Resource-Based City on the Loess Plateau

Abstract

Forest carbon sink (FCS) is essential for achieving carbon neutrality and supporting sustainable development in ecologically fragile, resource-based cities such as those on the Loess Plateau. Despite the success of national afforestation programs, economic valuations of FCS at the city level remain limited. This study develops an integrated framework combining carbon stock estimation, regional carbon pricing, and net present value (NPV)-based valuation. Using Shenmu City in Shaanxi Province as a case study, forest carbon stocks from 2010 to 2023 are estimated based on the 2006 IPCC Guidelines. Future stocks (2024–2060) are projected using the GM (1,1) model. A dynamic pricing mechanism with a government-guaranteed floor price is applied under three offset scenarios (5%, 10%, 15%). The results show that Shenmu’s forest carbon stock could reach 20.67 million tonnes of CO₂ by 2060, and under a 15% offset scenario, the peak NPV reaches CNY 4.02 billion. Higher offset ratios increase FCS value by 18–22%, reflecting the growing scarcity of carbon credits. The pricing model improves market stability and investor confidence. This study provides a replicable approach for carbon sink valuation in semi-arid areas and offers policy insights aligned with SDG 13 (Climate Action) and SDG 15 (Life on Land).

1. Introduction

Global climate change has emerged as an unprecedented challenge to human societies and natural systems [1]. Global average surface temperatures have risen by 1.1 °C since pre-industrial levels, driven primarily by anthropogenic greenhouse gas (GHG) emissions—of which carbon dioxide (CO₂) accounts for nearly 77%, largely from fossil fuel combustion [2]. In this context, terrestrial ecosystems—especially forests—play a critical role in mitigating climate change. Forests not only store approximately 861 petagrams (Pg) of carbon, representing 40% of the terrestrial carbon pool, but also act as dynamic carbon sinks that annually absorb around 50% of global fossil fuel emissions [3,4].

As the world’s largest GHG emitter, China has committed to reaching peak carbon emissions by 2030 and achieving carbon neutrality by 2060 [5]. In support of this, the country has implemented major renewable energy transitions alongside large-scale ecological initiatives, such as the Three-North Shelter Forest Program and the Grain-for-Green Program [6]. These efforts have significantly enhanced China’s forest carbon sink, contributing over 1.2 billion tonnes of CO₂ in annual absorption, nearly 10% of the national total. Projections suggest that, with improved forest governance, annual forest carbon sequestration could offset up to 45% of residual fossil fuel emissions by 2060 [7].

Reaching carbon neutrality requires both emission reduction and carbon sink enhancement. While emission control relies on clean energy and efficiency improvements, carbon sink enhancement depends on afforestation, ecological restoration, and carbon capture [8,9,10]. Forests are the most effective terrestrial carbon sinks, accounting for approximately 62% of total land-based CO₂ absorption [11].

Forest carbon sink is an effective way to harmonize economic development and reduce GHG emissions. In China, ecological compensation mechanisms have been strengthened to incentivize carbon sequestration through forest conservation [12]. Recent policies aim to better incorporate ecosystem service values into market-based instruments such as carbon trading [13]. However, monetizing the value of forest carbon sink remains difficult due to volatile prices, high policy dependence, and fragmented market mechanisms.

The valuation of forest carbon sink involves two main components: carbon stock estimation and carbon price forecasting [14]. Existing accounting approaches include biomass surveys, volume expansion, and remote sensing [15]. Recent models incorporate species-specific data and spatial extensions, improving precision [16,17,18]. In forecasting, machine learning, regression, and hybrid models are increasingly applied, while valuation typically uses net present value (NPV) or the Black–Scholes approach [15,19]. NPV discounts all expected carbon-credit revenues (or avoided-damage benefits) and lifecycle costs to a common base year, thereby capturing both the time value of money and market-volume dynamics; its applications range from stand-level decisions—such as optimizing rotation age under a carbon price—to national policy assessments. For example, Haight et al. reported that afforestation yielded the highest marginal benefit–cost ratio among three U.S. federal mitigation scenarios for 2015–2050 [20], whereas Guan et al. showed that Yunnan’s subtropical pine plantations will become profitable once the carbon price exceeds CNY 60 t⁻¹ CO₂ under China’s dual-carbon strategy [21]. Comparable provincial studies in Heilongjiang, Liaoning, and Beijing pair GM (1,1) stock forecasts with NPV analysis to probe discount-rate sensitivity. Although the Black–Scholes model—originally designed for financial derivatives—remains robust for valuing carbon assets under high price volatility, NPV is generally preferred by policymakers for its intuitive cost–benefit metric and capacity to incorporate endogenously determined price paths [22,23]. Nonetheless, city-scale, market-reflective pricing frameworks are still scarce, underscoring the relevance of the approach advanced in this study.

The GM (1,1) model is well-suited to forecasting forest carbon sinks when only short, noisy time series are available. The model first applies an Accumulated Generating Operation (AGO) to the raw carbon sink time series, which smooths random fluctuations and reveals the underlying exponential growth law of biomass carbon storage [24]. GM (1,1) then fits a first-order differential equation to the AGO sequence and analytically solves it to obtain closed-form forecasts that can be inverse-transformed to the original scale. This procedure requires as few as four observations and no prior assumptions about data distribution, overcoming the minimum-sample limitations of methods such as ARIMA or machine-learning algorithms that demand larger, stationary datasets. Empirical studies in forest stands and other regions show that GM (1,1) can keep its mean absolute percentage error below 10% with data lengths of 5–8 years, outperforming conventional time series models in equally small samples [21,25,26].

On the policy front, carbon sink mechanisms like ecological compensation, carbon credits, and forest-based offsets have shown promise but remain underdeveloped in much of inland China [27]. The Loess Plateau, while a key ecological zone, faces challenges in sustaining restoration gains due to limited financial inputs, uneven benefit distribution, and weak project governance [28,29,30,31]. These issues weaken long-term restoration outcomes and hinder the economic realization of ecological benefits.

Shenmu City, located in the northern Loess Plateau, is a typical resource-based city, assessing its forest carbon sink potential and economic value, which is particularly significant. Between 2000 and 2020, carbon sequestration capacity increased due to afforestation efforts. Yet, the economic value of FCS in the region remains largely unrealized. Major constraints include high costs, low and unstable carbon prices, and a lack of localized valuation systems and trading mechanisms. Thus, there is an urgent need to establish regionally adaptive, market-based carbon pricing and compensation mechanisms, such as the guaranteed government procurement of carbon sink services.

This study aims to address these challenges through three key innovations: (1) the development of an integrated evaluation framework that includes forest carbon sink quantification, regional carbon pricing, and economic valuation using the NPV method; (2) the introduction of a dynamic pricing model that incorporates a government-guaranteed minimum transaction price to enhance market stability; (3) the application of the framework to Shenmu City, using the GM (1,1) gray forecasting model to predict carbon sink trends (2024–2060) and assess economic value under three policy scenarios with different offset ratios (5%, 10%, and 15%). The findings aim to support regional policy design, facilitate low-carbon transitions in resource-based cities, and contribute to SDGs 13 and 15.

2. Materials and Methods

2.1. Study Area

Shenmu City (38°13′–39°27′ N, 109°40′–110°54′ E) lies at the Qin–Jin–Mongolian tri-provincial junction in northern Shaanxi (Figure 1), overlapping the western margin of the Loess Plateau and the southeastern fringe of the Maowusu Sandy Land, therefore straddling both the Yellow River Basin and the Great Wall geomorphic belt [32]. The administrative area covers 7635 km², making it the largest county-level administrative unit in Shaanxi Province. The city has a variety of landforms, including hills and gullies in the south (49%) and sandy grasslands in the north (51%), covering a wide range of landscapes such as plateaus, hills, sands, plains, and lakes, which represent the typical geographic pattern of arid and semi-arid regions in northern China. The average annual temperature is 9.2 °C, the average annual precipitation is 441.9 mm, and the evaporation is as high as 1338.4 mm. The spatial distribution of rainfall and water resources is uneven, and the overall regional water resource supply is tense. The soil type is dominated by sandy and loess soils, and the transition between steppe and forest–steppe characterizes the vegetation distribution. As of 2023, Shenmu had a population of approximately 579,800. Land use is split between intensive resource extraction and ecological conservation. The city holds abundant coal, gas, and quartz sand reserves, making it the largest coal-producing county-level region in China. In 2023, its coal output reached 331 million tonnes, generating CNY 365.45 billion in industrial output. Its strong energy sector ranks it among the most competitive counties in western China.

Shenmu has promoted forest and grassland restoration through afforestation, forest closure, and erosion control. By 2023, its forested area reached 3301.65 km², with 43.2% forest coverage and 63.35% grassland vegetation cover. Afforestation and erosion control covered 473.17 km² and 442.22 km², respectively. As it modernizes its coal-based industries and expands clean energy such as solar and wind, Shenmu’s green transformation coexists with a legacy of heavy industry. This structural tension makes Shenmu a representative case for studying carbon-neutral strategies and the economic valuation of forest carbon sink.

2.2. Carbon-Stock Accounting Framework

2.2.1. Data Sources

Carbon sink accounting in this study followed the 2006 IPCC Guidelines for National Greenhouse Gas Inventories, which classify land types based on the Land Use and Land Use Change (LULUCF) framework [33]. The estimation of carbon sinks covered six types of carbon pools: above- and below-ground biomass, dead wood, litter, soil organic carbon, and harvested wood products. The relevant primary data mainly came from the Shenmu City Statistical Yearbook (2010–2023), Annual Statistical Bulletins (2010–2023), local GHG inventories, forestry department archives, afforestation and forest management plans, and the relevant scientific literature. Key indicators included forest area, dominant species, standing volume, and forest stock. To improve estimation accuracy, measured growth parameters of local tree species (e.g., camphor pine, oil pine) were combined with biomass conversion factors from published studies. Carbon density and biomass parameters followed technical specifications issued by the State Forestry and Grassland Administration and provincial GHG inventory guidelines. As this study focused on vegetation-based sequestration, the biomass estimation method under the IPCC framework was adopted to support the quantitative evaluation of forest carbon sink and the design of regionally adapted carbon pricing mechanisms.

Our carbon emissions accounting made comprehensive use of Shenmu City’s socio-economic and energy statistics for the period 2010–2023, including population, urbanization rate, GDP, energy mix and consumption, electricity use, and industrial structure. The data were mainly taken from the Shenmu City Statistical Yearbook, annual statistical bulletins, and information released by the National Bureau of Statistics, Shaanxi Provincial Bureau of Statistics, and other authoritative organizations. To improve the accuracy and representativeness of the emission data, this study also collected annual average energy consumption and energy intensity data for industry, construction, transportation, services, agriculture, forestry, animal husbandry, fishery, and residential life, which were mainly derived from local surveys and departmental data submissions. The carbon emission data for 2024–2060, derived from the authors’ unpublished modeling framework, were incorporated as an anticipatory extension of the empirical dataset, enabling forward-looking analysis under a unified accounting paradigm.

The cost of forest carbon sink mainly includes the afforestation cost, maintenance cost, and development costs of forest carbon sink projects. The afforestation cost data came from the research statistics of Shenmu municipal government’s investment in forestry projects, and the initial input per mu of afforestation was estimated by combining the local seedling and labor costs. The cost of forestry care and maintenance was based on the forest ecological benefit compensation standard of Shaanxi Province and the empirical values presented in related studies [34]. Based on operational data from afforestation projects in Shenmu City, the annual cost of forestry care and maintenance is approximately CNY 299,850.28/km² during the young forest stage, CNY 89,955.02/km² during the growth stage, and CNY 29,985.01/km² during the maturity stage. The carbon sink development cost is the processing cost of forest carbon increment to form tradable carbon credits. Referring to the relevant research data [35], the development cost of forest carbon sink projects in the national carbon market under the CCER methodology accounts for about 3% to 5% of the carbon sink trading amount. Considering that the regional carbon market process in Shenmu City is more straightforward, we assumed that the development cost was about 1–3% of the afforestation cost. For carbon price data, the China carbon quota market pilot price and the EU carbon market price provided reference prices for the model. It was assumed that the initial value of the regional carbon price in Shenmu City was slightly lower than the average price in the national carbon market and would increase over time. In the scenario analysis, different clearing caps and offset ratio conditions, as well as guaranteed price mechanisms, affected the effective carbon price, which is detailed in the methodology section below.

2.2.2. Measurement of Carbon Stocks

The measurement of forest carbon sink in this study mainly followed the IPCC 1996 Guidelines for National Greenhouse Gas Inventories and Guidelines for the Preparation of Provincial Greenhouse Gas Inventories in China. Additionally, it incorporated the parameters and technical pathways for terrestrial carbon sequestration estimation outlined in the Second National Communication [33,36,37,38]. In the carbon accounting process, three major carbon pools were considered the primary targets for estimation: above-ground biomass, below-ground biomass, and soil organic carbon. Because SOC gains are not creditable under prevailing forest-offset protocols, the valuation ultimately focused on the two biomass pools (UNFCCC. 2006). For above-ground biomass, stand area, dominant species, age class, and mean growing stock were extracted from Shenmu’s 2010–2023 continuous-inventory plots and converted to biomass with species-specific expansion factors. For below-ground biomass, species-level root-to-shoot ratios (e.g., Pinus tabulaeformis, Betula platyphylla) were applied to the calibrated above-ground totals to obtain below-ground estimates.

Emission factors were selected based on the principle of conservatism to avoid overestimation. Region-specific parameter adjustments were made according to the local forest characteristics in Shenmu City, including forest area, dominant tree species composition, age structure, and standing volume. These regional calibrations enhanced the representativeness and accuracy of the carbon sink estimates. The change in biomass carbon stock was calculated using the IPCC (1996) methodology:

$$\Delta C_{biomass}=\Delta C_{arboreal}+\Delta C_{bamboo/economic/shrub}-\Delta C_{loss}$$

(1)

where

ΔC_biomass refers to the net change in biomass carbon stocks in forests and other woody biomass (measured in tonnes of carbon);

ΔC_arboreal refers to carbon sink in the biomass growth of arboreal forests (tonnes of carbon);

ΔC_{bamboo/economic/shrub} refers to a change in biomass carbon stocks of bamboo forests (or economic forests, or special shrub forests) (tonnes of carbon);

ΔC_loss refers to biomass carbon loss from disturbances, harvesting, or consumption (tonnes of carbon).

Due to the confidentiality policies of the relevant authorities, disaggregated data on the carbon stocks of arboreal forests, economic forests, bamboo forests, special shrublands, and biomass losses are not publicly available. However, aggregated results—namely the total forest area and corresponding carbon stock estimates—are provided in

Table A3. Forest area, Standing volume, carbon sink (2010–2023), and forecast (2024–2060) based on GM (1,1) model in Shenmu City.

Year (a)	Forestry Area (km2)	Standing Volume (10 km3)	Actual (10 k tCO2)	Predicted (10 k tCO2)
2010	1782.97	1976.53	237.94	237.94
2011	1879.17	2112.69	237.96	240.84
2012	1975.36	2255.33	254.26	251.64
2013	2071.56	2404.76	262.84	262.93
2014	2167.76	2561.30	271.71	274.72
2015	2309.47	2745.21	290.33	287.04
2016	2360.15	2898.05	295.58	299.91
2017	2340.92	3027.58	309.28	313.36
2018	2552.55	3264.26	315.88	327.42
2019	2648.74	3461.71	359.51	342.10
2020	2788.51	3687.62	368.75	357.44
2021	2841.14	3886.18	377.99	373.47
2022	2935.17	4112.29	385.72	390.22
2023	3031.37	4350.11	399.14	407.72
2024				426.01
2025				445.11
2026				465.07
2027				485.93
2028				507.72
2029				530.49
2030				554.28
2031				579.14
2032				605.12
2033				632.25
2034				660.61
2035				690.23
2036				721.19
2037				753.53
2038				787.33
2039				822.64
2040				859.53
2041				898.08
2042				938.35
2043				980.44
2044				1024.41
2045				1070.35
2046				1118.35
2047				1168.50
2048				1220.91
2049				1275.66
2050				1332.87
2051				1392.65
2052				1455.11
2053				1520.36
2054				1588.55
2055				1659.79
2056				1734.23
2057				1812.00
2058				1893.26
2059				1978.17
2060				2066.89

.

2.2.3. Carbon Sink Forecast

In this study, the GM (1,1) gray model was employed to forecast forest carbon sink trends in Shenmu City due to its strong adaptability to small-sample, poor-information systems [39]. Unlike traditional models such as ARIMA, which require long, stationary time series, GM (1,1) is well-suited to the short, structurally trending data typical of regional carbon sink assessments [40,41]. Comparative analysis shows that GM (1,1) achieved a lower average relative error (5.45%) than exponential smoothing (9.87%), confirming its superior predictive accuracy. The model constructs a first-order differential equation based on the Accumulated Generating Operation (AGO), which smooths random fluctuations and reveals the system’s inherent dynamics. Parameter estimation was performed using the least squares method, and predictions were derived through a time response function. Accuracy testing followed standard thresholds: a relative error < 5% indicated high precision, 5–10% was acceptable, and >10% was unreliable. Overall, GM (1,1) proved to be an effective tool for forecasting forest carbon sink trajectories in data-limited, ecologically complex regions. The model’s basic formulation involves the following first-order differential equation:

$${\displaystyle \frac{d{\chi }^{(1)}}{dt}}+a\chi =u$$

(2)

where χ⁽¹⁾ is the accumulated series of the original data χ⁽⁰⁾, t is the time variable, and a, u are the developmental and endogenous control gray numbers.

2.3. Regional Carbon-Pricing Model

2.3.1. Market Context and Cost Structure

The economic value of forest carbon sink is primarily realized through monetization and value redistribution, with regional carbon markets and supportive policy frameworks serving as essential channels. Using Shenmu City as a case study, this research developed a localized valuation system centered on a regional trading mechanism. While international voluntary markets (e.g., VCS), national schemes (e.g., CCER), and ecological compensation channels exist, their relevance in Shenmu remains limited due to policy, quantification, and market constraints [42,43]. Therefore, this study focused on market-based valuation driven by local carbon pricing.

In the regional forest carbon market of Shenmu City, the government serves as buyer of last resort. When market prices fall below marginal sequestration costs, the government ensures viability by purchasing at a guaranteed minimum price. This supports afforestation, expands carbon sink capacity, and allows regulated enterprises to use forest carbon sink as offset credits. Based on labor value and equilibrium price theory, the forest carbon sink price (P_i) includes four components: the cost of afforestation (C_F), the cost of maintenance (C_M), the cost of development (C_D) and the premium coefficient (r):

$$P_i=P_{g,i}+P_{g,i}\times r_i$$

(3)

where the guaranteed base price P_{g, i} is the sum of cost components:

$$P_{g,i}=C_{F,i}+C_{M,i}+C_{D,i}$$

(4)

where

P_i: Market price in year i (CNY/tCO₂);

P_{g, i}: Guaranteed price in year i (CNY/tCO₂);

C_{F, i}: Cost of afforestation in year i (CNY/tCO₂);

C_{M, i}: Cost of conservation and management of forestry in year i (CNY/tCO₂);

C_{D, i}: Cost of development tradable carbon sink credits in year i (CNY/tCO₂);

r_i: Premium reflecting supply–demand dynamics in year i (dimensionless).

The estimated values of C_F, C_M, and C_D were derived from forestry fiscal investments and carbon stock data in Shenmu City between 2011 and 2023. Based on these cost components, the P_g is calculated as the value of the labor invested by forest farmers and other forest carbon sink producers in producing FCS. The r is the proportion of the premium of the FCS transaction price relative to the current guaranteed transaction price caused by changes in the demand and supply of FCS. Historical fiscal data on afforestation investment and forest establishment area were processed using a three-year moving average to ensure robustness.

2.3.2. Forest Carbon Sink Price Model in Regional Carbon Market

This study assumed that the FCS price in the regional forest carbon sink trading market in Shenmu City is the result of free and full quotes from buyers and sellers, and that its transaction price follows the theory of price equilibrium between supply and demand. The market price in year i combines the floor price with a demand-driven premium:

$$P_i=\left(C_{F,i}+C_{M,i}+C_{D,i}\right)\times \left(1+r_i\right)$$

(5)

Therefore, we calculated the relationship between FCS price and its supply and demand in a certain trading period (Figure A1,

Table A1. Relationship between forest carbon sink price, supply, and demand.

Price	Demand	Supply	Critical Point
8	10,000,000	3,065,536
10	10,000,000	3,200,000
12	10,000,000	3,497,664
14	10,000,000	4,075,648
16	10,000,000	5,097,152	16
18	10,000,000	6,779,136
20	10,000,000	9,400,000	20.4
22	10,000,000	13,307,264
24	10,000,000	18,925,248	24
26	10,000,000	26,762,752

).

(1)

FCS Demand Curve

Under the carbon neutrality framework, FCS demand at any point in time is assumed to be inelastic and defined as a fixed proportion of total carbon emissions, i.e., the carbon neutrality rate. Since the maximum FCS clearance volume is pre-set, demand remains constant regardless of price fluctuations, represented as a vertical demand curve:

$$Q_{r,i}=c_i$$

(6)

where Q_{r, i} is the FCS demand in period i, and c_i is the upper limit (constant) of the FCS offset caps in period i.

(2)

FCS Supply Curve

Supply is price-elastic. As prices rise, afforestation becomes more profitable, encouraging greater participation and increasing FCS supply. The supply function is expressed as

$$Q_{s,i}=f\left(P_i\right)$$

(7)

where Q_{r, i} is the supply of FCS in period i, P_i is the price of FCS in period i, and f is a transformation function reflecting the effect of price on supply. This function is influenced by the average social return on investment, the direct and opportunity costs of carbon sink production, and other factors. Based on the incentive effect of the guaranteed transaction mechanism, the study constructed the FCS price–supply curve (Figure A2,

Table A2. Fitted curve of forest carbon sink price and supply.

Year (a)	FCS Price (CNY)	FCS Supply (tCO2)
2024	362.60	1,129,113.23
2025	385.23	1,196,723.60
2026	396.51	1,268,105.73
2027	407.84	1,343,461.25
2028	419.20	1,423,002.27
2029	428.52	1,506,951.92
2030	439.91	1,595,544.89
2031	451.34	1,689,028.07
2032	462.81	1,787,661.08
2033	474.31	1,891,717.02
2034	483.48	2,001,483.08
2035	495.00	2,117,261.27
2036	506.56	2,239,369.22
2037	518.15	2,368,140.90
2038	529.77	2,503,927.48
2039	538.77	2,647,098.24
2040	550.40	2,798,041.41
2041	562.07	2,957,165.18
2042	575.38	3,100,571.78
2043	588.79	3,249,987.42
2044	599.30	3,405,974.35
2045	612.77	3,568,794.53
2046	626.33	3,738,719.01
2047	639.95	3,916,028.08
2048	653.65	4,101,011.56
2049	667.42	4,293,968.99
2050	681.28	4,495,209.88
2051	695.21	4,705,053.92
2052	709.23	4,923,831.18
2053	723.33	5,151,882.37
2054	737.52	5,389,559.01
2055	751.80	5,637,223.66
2056	766.17	5,895,250.10
2057	780.63	6,164,023.51
2058	795.19	6,443,940.70
2059	809.84	6,735,410.19
2060	824.60	7,038,852.46

) by numerically fitting the guaranteed price to the historical data for carbon sink, which is used to predict the future trend of supply changes.

It follows that, at the current measure, the $Q_{s,i}=f\left(P_i\right)=1.0954\times P_i^{2.3373}$. It follows that $P_i=f^{\prime }\left(Q_{s,i}\right)=0.9618\times Q_{s,i}^{0.4278}$ (f′ is the inverse function of f).

(3)

Supply–Demand Equilibrium and Premium Coefficient

On the FCS price–supply–demand volume curve, the intersection point of the supply curve and the demand curve represents the equilibrium state of supply and demand in the market at a certain point in time. The corresponding price is the market-transacted price of FCS for the current period, and the quantity is the transacted volume (Figure 2, Table A1). The intersection point is denoted as (P, Q), where P denotes the price and Q denotes the quantity traded, which is equal to both supply and demand.

If $Pg<P$, the market is oversupplied, the guaranteed price is lower than the equilibrium price, and the FCS is sold at a price higher than the guaranteed price. At this point, the FCS premium coefficient r is positive, indicating the percentage premium of the market price compared to the guaranteed price. Since P₀, Q₀, and c are known quantities at a certain defined point in time, the premium coefficient can be calculated as follows:

$$r={\displaystyle \frac{P-P_0}{P_0}}={\displaystyle \frac{f\prime \left(Q\right)-P_0}{P_0}}={\displaystyle \frac{0.9618c^{0.4278}-P_0}{P_0}}$$

(8)

If $P_g\ge P$, supply exceeds demand in the market, the guaranteed price is higher than or equal to the equilibrium price, and the seller chooses to sell at the guaranteed price, the premium coefficient r = 0. Therefore, the expression for the FCS premium coefficient r is

$$r=\max \left({\displaystyle \frac{0.9618c^{0.4278}-P_0}{P_0}},0\right)$$

(9)

2.3.3. Scenario Design and Price Trajectories

To simulate FCS market evolution under the dual-carbon goal, we assumed that Shenmu City peaks in terms of carbon emissions by 2035 and reaches neutrality by 2060. The carbon neutrality rate increases linearly from 2031 to 2060. Based on a stable technological baseline and the CCER framework, three scenarios of clearing ratio caps were considered: 5%, 10%, and 15%. These scenarios allow the prediction of future FCS price trajectories under different policy and market conditions.

2.4. Economic Valuation of Forest Carbon Sink

Forest carbon sink value is calculated as the product of carbon stocks and unit price. To reflect local ecological conditions, a pricing model tailored to Shenmu was developed. Each year’s newly established forest land was treated as an independent carbon sink project and evaluated using the NPV method. Future cash inflows from carbon sales and establishment costs were discounted to obtain annual NPV, which was then divided by the afforested area to calculate the per-unit value. The value series from 2024 to 2060 reflects the evolution of forest carbon sink under carbon neutrality.

Assumptions: (1) Revenue derives solely from carbon credit sales (no timber income); (2) unit sequestration cost stabilizes over time; (3) the main species are camphor and oil pine with 50-year cycles; (4) a 2.57% discount rate, reflecting China’s 30-year Treasury bond (Code: 019742, 2024).

NPV formula:

$${NPV}_i=\sum _{t=i}^j{\displaystyle \frac{P_t\times Q_t}{{(1+r)}^{(j-t)}}}-C_i,\left(i=\mathrm{2024,2025},\dots ,2060\right)$$

(10)

$${npv}_i={\displaystyle \frac{{NPV}_i}{S_i}},\left(i=\mathrm{2024,2025},\dots ,2060\right)$$

(11)

where

NPV_i: The current net present value of the forest carbon sink project in year i (CNY);

J: The final number of years that the forest carbon sink projects will generate carbon sink;

P_t: The price per unit of regional carbon sink in year t (CNY/tCO₂);

Q_t: The quantity of carbon sink generated in year t by the forest projects initiated in year i (tonnes CO₂);

C_i: The total cost of establishing and maintaining forest carbon sink projects in year i (CNY);

npv_i: The current net present value per unit area of forest carbon sink projects in year i (CNY/km²);

S_i: The afforestation area established in year i (km²).

These scenarios were designed to align with Shenmu City’s implementation of China’s “dual-carbon” strategy and support regional goals for energy structure optimization and efficiency improvement.

All data processing and calculations were performed using Microsoft Excel 2019 (Microsoft Corporation, Redmond, WA, USA).

3. Results

3.1. Temporal Variation in Forest Carbon Sink in Shenmu City

From 2010 to 2060, the forest carbon sink in Shenmu City exhibits a distinct staged growth pattern (Figure 3). Empirical data indicate that the total regional forest carbon sink reached 2.38 million tonnes of CO₂ equivalent in 2010 and increased to 3.57 million tonnes by 2020, representing an average annual growth rate of approximately 4.12% (Table A3). This growth is primarily driven by national ecological initiatives, including the Grain-for-Green Program, the Three-North Shelter Forest Program, and the ecological rehabilitation of mining areas, which have significantly enhanced above-ground biomass accumulation and soil carbon storage.

Over the long term, the forest carbon sink in Shenmu is expected to maintain a steady upward trajectory, assuming continuity in current forestry policies and afforestation practices. Using the GM (1,1) gray forecasting model, carbon sink trends from 2024 to 2060 were projected. The results show a sustained acceleration in carbon sink capacity, with a strong model fit and a relative prediction error controlled within 5%, indicating high reliability.

By 2040, the total forest carbon sink is expected to surpass 8.60 million tonnes of CO₂, and by 2060, it is projected to reach 20.67 million tonnes, with an annual average increase of 0.60 million tonnes. The curve reflects exponential growth characteristics, suggesting that forest ecosystems in Shenmu are transitioning from middle-aged to mature stands, thereby steadily enhancing their carbon sink potential.

3.2. Economic Cost and Price Characteristics of Forest Carbon Sink

Afforestation investment was combined with per-unit lifetime carbon sink to compute the unit afforestation cost per ton of CO₂ sequestered (

Table 1. Afforestation cost per unit of FCS in Shenmu City (2011–2023).

Year (a)	Afforestation Investment (104 CNY)	Afforestation Area (km2)	Cost (104 CNY/km2)	Lifetime Carbon Sink (tCO2/km2)	Cost per tCO2 Sequestered (CNY/tCO2)
2011	7136	162.0	58.4	21,270	27.43
2012	6578	169.3	85.4	21,540	39.65
2013	14,450	151.3	119.4	21,810	54.74
2014	18,730	144.8	133.2	22,065	60.35
2015	16,808	122.5	141.2	22,320	63.26
2016	11,245	84.0	151.5	22,575	67.13
2017	13,008	84.3	207.3	22,830	90.77
2018	13,775	82.7	266.9	23,070	115.64
2019	28,575	100.2	313.7	23,310	134.53
2020	37,206	115.3	330.3	23,550	140.26
2021	33,500	101.1	333.9	23,775	140.44
2022	48,231	143.7	353.7	24,000	147.35
2023	54,850	164.3	360.9	24,225	148.95

Note: Afforestation projects generally span three years; a rolling average was applied. The lifetime carbon sink is calculated over a 50-year cycle.

). The results reveal that this cost increased from CNY 27.43/tCO₂ in 2011 to CNY 148.95/tCO₂ in 2023, reflecting rising terrain development challenges, labor costs, and seedling prices.

Using a linear regression model (R² = 0.95) based on 2011–2023 data, the trend in afforestation cost was extrapolated for the 2024–2060 period (Figure 4a–c), showing that while growth will moderate, rising costs will continue to exert pressure on the economic viability of carbon sink projects (

Table A4. Forestry cost forecast for 2024–2060.

Year (a)	Forestry Area (km2)	Afforestation Cost per Unit of Carbon Sink (CNY)	Cost of Nurturing and Maintenance per Unit of Carbon Sink (CNY)	Development Cost Ratio (%)	Development Cost per Unit of Carbon Sink (CNY)	FCS Guaranteed Price (CNY/tCO2)
2010	1782.97
2011	1879.17	27.43	106.75	3	4.03	138.21
2012	1975.36	39.65	101.02	3	4.22	144.89
2013	2071.56	54.74	96.91	3	4.55	156.20
2014	2167.76	60.35	128.64	3	5.67	194.66
2015	2309.47	63.26	142.66	3	6.18	212.09
2016	2360.15	67.13	156.12	3	6.70	229.95
2017	2340.92	90.77	169.08	3	7.80	267.64
2018	2552.55	115.64	181.54	3	8.92	306.10
2019	2648.74	134.53	193.56	3	9.84	337.93
2020	2788.51	140.26	205.14	3	10.36	355.76
2021	2841.14	140.44	194.79	3	10.06	345.28
2022	2935.17	147.35	194.81	3	10.26	352.42
2023	3031.37	148.95	194.87	3	10.31	354.14
2024		157.06	194.98	3	10.56	362.60
2025		178.88	195.14	3	11.22	385.23
2026		189.63	195.34	3	11.55	396.51
2027		200.39	195.57	3	11.88	407.84
2028		211.14	195.85	3	12.21	419.20
2029		221.90	196.17	3	10.45	428.52
2030		232.65	196.53	3	10.73	439.91
2031		243.41	196.93	3	11.01	451.34
2032		254.16	197.36	3	11.29	462.81
2033		264.92	197.83	3	11.57	474.31
2034		275.67	198.33	2	9.48	483.48
2035		286.43	198.87	2	9.71	495.00
2036		297.18	199.44	2	9.93	506.56
2037		307.94	200.05	2	10.16	518.15
2038		318.69	200.69	2	10.39	529.77
2039		329.45	201.36	2	7.96	538.77
2040		340.20	202.07	2	8.13	550.40
2041		350.96	202.80	2	8.31	562.07
2042		361.71	205.17	2	8.50	575.38
2043		372.47	207.62	2	8.70	588.79
2044		383.22	210.14	1	5.93	599.30
2045		393.98	212.73	1	6.07	612.77
2046		404.73	215.39	1	6.20	626.33
2047		415.49	218.13	1	6.34	639.95
2048		426.24	220.94	1	6.47	653.65
2049		437.00	223.82	1	6.61	667.42
2050		447.75	226.78	1	6.75	681.28
2051		458.51	229.82	1	6.88	695.21
2052		469.26	232.95	1	7.02	709.23
2053		480.02	236.16	1	7.16	723.33
2054		490.77	239.45	1	7.30	737.52
2055		501.53	242.83	1	7.44	751.80
2056		512.28	246.30	1	7.59	766.17
2057		523.04	249.86	1	7.73	780.63
2058		533.79	253.52	1	7.87	795.19
2059		544.55	257.28	1	8.02	809.84
2060		555.30	261.14	1	8.16	824.60

).

C_M was estimated based on forest area structure projections and a 2% annual inflation rate.

Table 2. Projected FCS care costs in Shenmu City (2024–2060).

Year	Total Forest Area (km2)	Seedling Forest (km2)	Growing Forest (km2)	Mature Forest (km2)	Annual Nurturing Cost (106 CNY)	Annual Carbon Sink (106 tCO2)	Unit Care Cost (CNY/tCO2)
2024	3193.29	353.83	680.57	2158.89	2201.58	112.91	194.98
2025	3320.77	367.95	707.74	2245.07	2335.26	119.67	195.14
2030	4038.71	447.51	860.75	2730.45	3135.75	159.55	196.53
2035	4911.87	544.25	1046.85	3320.77	4210.62	211.73	198.87
2040	5973.81	661.92	1273.17	4038.71	5653.93	279.8	202.07
2045	7265.33	805.03	1548.43	4911.87	7591.99	356.88	212.73
2050	8836.07	979.07	1883.20	5973.81	10,194.38	449.52	226.78
2055	10,746.41	1190.74	2290.34	7265.33	13,688.81	563.72	242.83
2060	13,069.75	1448.18	2785.50	8836.07	18,381.06	703.89	261.14

Notes: Forest age classification follows the Technical Regulations for Continuous Forest Inventory in China (9th Edition, 2014): young forests (0–3 years); middle-aged forests (4–10 years); mature forests (>10 years). Initial afforestation care costs are included in project establishment and are not double-counted. Inflation adjustments are applied at an annual rate of 2%.

summarizes the forecasted forest composition and the corresponding forest maintenance costs per ton of CO₂. As forests mature, the weighted average cost per ton increases from CNY 194.98/tCO₂ in 2024 to CNY 261.14/tCO₂ by 2060, largely due to increased ecological management needs for older forest stands.

Based on the operational experience of the CCER mechanism, the development cost of forest carbon sink projects typically accounts for 3% to 5% of the total transaction value. Given the relative simplicity and lower compliance costs of Shenmu’s regional carbon trading system, this study set the development cost ratio at 3% in 2024. This proportion was assumed to decrease by 1 percentage point every five years, stabilizing at 1% by 2044. Accordingly, the estimated development cost per ton of carbon sink follows a phased downward trajectory between 2024 and 2060 (Figure 4b).

Combining the costs of afforestation, ecological management, and project development, the projected guaranteed transaction price for forest carbon sink (Figure 4c) is expected to increase from approximately CNY 362.60/tCO₂ in 2024 to around CNY 824.60/tCO₂ by 2060. This represents an average annual growth rate of roughly 2.31%. This rising trend reflects both the inherent rigidity of supply-side costs and positive market feedback under an evolving ecological product pricing system.

3.3. Scenario Assessment of Economic Value of FCS and Potential Simulation Analysis

To further assess the economic potential of regional forest carbon sink under the framework of carbon neutrality, this study constructs a development pathway in which Shenmu City is projected to peak its carbon emissions around 2035 and achieve carbon neutrality by 2060. Based on this assumption, three policy scenarios are established under a low-carbon context, corresponding to compliance offset ratios of 5%, 10%, and 15%, respectively. By integrating regional carbon emission trajectories and the FCS supply–demand equilibrium model, we simulate the dynamic evolution of FCS premium coefficients from 2024 to 2060 (Figure 5,

Table A5. Variation in forest carbon sink premium coefficients and prices by offset ratio.

Year (a)	Carbon Emission (10 k tCO2)	Media Frequency (%)	Total Social Carbon Sink Demand (tCO2)	Supply of Forestry Carbon Sinks (CNY)	Guaranteed Transaction Price (CNY)	5% Upper Limit of Clearance				10% Upper Limit of Clearance				15% Upper Limit of Clearance
						FCS Upper Limit of Clearance (tCO2)	Premium Coefficient	FCS Price (CNY/tCO2)	Discount 2024 (CNY)	FCS Upper Limit of Clearance (tCO2)	Premium Coefficient	FCS Price (CNY/tCO2)	Discount 2024 (CNY)	FCS Upper Limit of Clearance (tCO2)	Premium Coefficient	FCS Price (CNY/tCO2)	Discount 2024 (CNY)
2024	13,819.6343	0.0	0.00	1,129,113.23	362.6046186	0.00	0.00	363	363	0.00	0.00	363	363	0.00	0.00	363	363
2025	14,933.62	0.0	0.00	1,196,723.60	385.2334995	0.00	0.00	385	376	0.00	0.00	385	376	0.00	0.00	385	376
2026	16,137.40	0.0	0.00	1,268,105.73	396.5145976	0.00	0.00	397	377	0.00	0.00	397	377	0.00	0.00	397	377
2027	17,438.22	0.0	0.00	1,343,461.25	407.8385377	0.00	0.00	408	378	0.00	0.00	408	378	0.00	0.00	408	378
2028	18,843.90	0.0	0.00	1,423,002.27	419.2042224	0.00	0.00	419	379	0.00	0.00	419	379	0.00	0.00	419	379
2029	20,362.88	0.0	0.00	1,506,951.92	428.5202846	0.00	0.00	429	377	0.00	0.00	429	377	0.00	0.00	429	377
2030	22,004.31	0.0	0.00	1,595,544.89	439.9108896	0.00	0.00	440	378	0.00	0.00	440	378	0.00	0.00	440	378
2031	21,926.61	3.3	7,308,868.58	1,689,028.07	451.3401764	365,443.43	0.00	451	378	730,886.86	0.00	451	378	1,096,330.29	0.00	451	378
2032	21,849.17	6.7	14,566,114.93	1,787,661.08	462.8073153	728,305.75	0.00	463	378	1,456,611.49	0.00	463	378	2,184,917.24	0.07	496	405
2033	21,772.01	10.0	21,772,012.52	1,891,717.02	474.3115318	1,088,600.63	0.00	474	377	2,177,201.25	0.04	495	394	3,265,801.88	0.24	589	468
2034	21,695.13	13.3	28,926,833.52	2,001,483.08	483.4820925	1,446,341.68	0.00	483	375	2,892,683.35	0.16	559	434	4,339,025.03	0.37	665	516
2035	21,618.51	16.7	36,030,848.80	2,117,261.27	495.0018742	1,801,542.44	0.00	495	374	3,603,084.88	0.24	614	464	5,404,627.32	0.47	730	552
2036	21,542.16	20.0	43,084,327.97	2,239,369.22	506.5565364	2,154,216.40	0.00	507	374	4,308,432.80	0.31	663	489	6,462,649.20	0.56	788	581
2037	21,466.09	23.3	50,087,539.37	2,368,140.90	518.1454961	2,504,376.97	0.01	525	378	5,008,753.94	0.36	707	508	7,513,130.91	0.62	841	604
2038	21,390.28	26.7	57,040,750.08	2,503,927.48	529.7682103	2,852,037.50	0.05	555	389	5,704,075.01	0.41	747	524	8,556,112.51	0.68	889	623
2039	21,314.74	30.0	63,944,225.91	2,647,098.24	538.770134	3,197,211.30	0.08	583	399	6,394,422.59	0.46	785	536	9,591,633.89	0.73	933	638
2040	21239.47	33.3	70,798,231.42	2,798,041.41	550.4015801	3,539,911.57	0.11	609	406	7,079,823.14	0.49	819	546	10,619,734.71	0.77	975	649
2041	20750.30	36.7	76,084,433.95	2,957,165.18	562.0652121	3,804,221.70	0.12	628	408	7,608,443.40	0.50	845	549	11,412,665.09	0.79	1005	653
2042	20272.40	40.0	81,089,588.16	3,100,571.78	575.3817974	4,054,479.41	0.12	646	409	8,108,958.82	0.51	868	550	12,163,438.22	0.80	1033	654
2043	19805.50	43.3	85,823,835.79	3,249,987.42	588.7876548	4,291,191.79	0.12	661	408	8,582,383.58	0.51	890	549	12,873,575.37	0.80	1058	653
2044	19349.36	46.7	90,297,000.51	3,405,974.35	599.2958465	4,514,850.03	0.13	676	407	9,029,700.05	0.52	909	547	13,544,550.08	0.80	1082	651
2045	18903.72	50.0	94,518,597.20	3,568,794.53	612.7747213	4,725,929.86	0.12	689	405	9,451,859.72	0.51	927	544	14,177,789.58	0.80	1103	647
2046	18468.35	53.3	98,497,840.94	3,738,719.01	626.3253624	4,924,892.05	0.12	702	401	9,849,784.09	0.51	944	540	14,774,676.14	0.79	1123	642
2047	18043.00	56.7	102,243,655.78	3,916,028.08	639.9493555	5,112,182.79	0.11	713	398	10,224,365.58	0.50	959	535	15,336,548.37	0.78	1141	636
2048	17627.45	60.0	105,764,683.26	4,101,011.56	653.6483591	5,288,234.16	0.11	723	393	10,576,468.33	0.49	973	529	15,864,702.49	0.77	1157	629
2049	17221.47	63.3	109,069,290.71	4,293,968.99	667.4241067	5,453,464.54	0.10	733	389	10,906,929.07	0.48	986	523	16,360,393.61	0.76	1173	622
2050	16,824.84	66.7	112,165,579.25	4,495,209.88	681.2784093	5,608,278.96	0.09	742	383	11,216,557.92	0.46	998	516	16,824,836.89	0.74	1187	614
2051	16,114.03	70.0	112,798,187.39	4,705,053.92	695.2131579	5,639,909.37	0.07	743	375	11,279,818.74	0.44	1000	504	16,919,728.11	0.71	1190	600
2052	15,433.25	73.3	113,177,142.63	4,923,831.18	709.2303261	5,658,857.13	0.05	745	366	11,317,714.26	0.41	1002	492	16,976,571.39	0.68	1191	585
2053	14,781.23	76.7	113,322,748.33	5,151,882.37	723.3319735	5,666,137.42	0.03	745	357	11,332,274.83	0.39	1002	480	16,998,412.25	0.65	1192	571
2054	14,156.76	80.0	113,254,045.07	5,389,559.01	737.5202486	5,662,702.25	0.01	745	348	11,325,404.51	0.36	1002	468	16,988,106.76	0.62	1192	557
2055	13,558.67	83.3	112,988,881.05	5,637,223.66	751.7973921	5,649,444.05	0.00	752	342	11,298,888.11	0.33	1001	456	16,948,332.16	0.58	1190	542
2056	12,985.84	86.7	112,543,978.93	5,895,250.10	766.1657409	5,627,198.95	0.00	766	340	11,254,397.89	0.30	999	444	16,881,596.84	0.55	1188	528
2057	12,437.22	90.0	111,934,999.01	6,164,023.51	780.6277314	5,596,749.95	0.00	781	338	11,193,499.90	0.28	997	431	16,790,249.85	0.52	1186	513
2058	11,911.78	93.3	111,176,599.21	6,443,940.70	795.185904	5,558,829.96	0.00	795	336	11,117,659.92	0.25	994	419	16,676,489.88	0.49	1182	499
2059	11,408.53	96.7	110,282,491.73	6,735,410.19	809.8429073	5,514,124.59	0.00	810	333	11,028,249.17	0.22	991	408	16,542,373.76	0.45	1178	485
2060	10,926.55	100.0	109,265,496.90	7,038,852.46	824.6015027	5,463,274.85	0.00	825	331	10,926,549.69	0.20	987	396	16,389,824.54	0.42	1173	471

).

The results reveal that the FCS premium coefficient is highly sensitive to changes in the compliance offset ratio, demonstrating a typical “increase–peak–decline” pattern. Under the 5% compliance scenario, the premium coefficient peaks at approximately 0.13 in 2044. In the 10% and 15% scenarios, the peak values rise to 0.52 and 0.80, respectively, with peak years also concentrated around 2044.

On this basis, the three types of costs per unit of carbon sink are further superimposed, and the market price trend of FCS under different scenarios is measured based on the pricing model (Figure 6, Table A5). Overall, the FCS price shows continuous growth in all scenarios, the price level is positively correlated with the offset ratios, and the FCS prices under the 5%, 10%, and 15% offset ratios in 2035 reach CNY 495/tCO₂, CNY 614/tCO₂, and CNY 730/tCO₂, respectively. By 2060, the prices under the 10% and 15% offset ratios reach CNY 987/tCO₂ and CNY 1173/tCO₂, respectively, highlighting the significant role of policy intensity in driving FCS price.

The NPV method was applied to assess the economic returns of the forest carbon sink under each scenario (Figure 7a–c). In the 5% offset scenario, the economic value peaks at approximately CNY 617.24/km² (Figure 7a,

Table A6. NPV of FCS under 5% condition.

Year (a)	Newly Added Forest Area (km2)	Q—Annual Carbon Sink Capacity (tCO2/km2)	∑P—Total Price Discounting (CNY/tCO2)	P—Revenue Discounting 2024 (10 k CNY)	C—Cost (10 k CNY)	Cost Discounting 2024 (10 k CNY)	NPV 2024 (10 k CNY)	NPV—Economic Value of FCS (10 k CNY)	NPV—Value per Unit Area (CNY/km2)
2024	122.59	353.59	17,984.32	77,958.97	40,942.17	40,942.17	37,016.80	37,016.80	301.94
2025	127.49	360.38	17,915.17	82,309.39	46,101.80	44,946.67	37,362.71	38,322.93	300.60
2026	132.58	367.21	17,830.25	86,805.51	50,282.24	47,794.06	39,011.45	41,042.41	309.57
2027	137.87	374.10	17,741.20	91,504.52	54,791.53	50,775.28	40,729.24	43,950.86	318.78
2028	143.38	381.03	17,648.28	96,414.53	59,652.86	53,895.17	42,519.36	47,061.76	328.24
2029	149.10	388.02	17,551.74	101,543.95	64,575.95	56,881.23	44,662.72	50,704.55	340.07
2030	155.05	395.06	17,453.66	106,912.86	70,189.76	60,277.00	46,635.86	54,305.29	350.24
2031	161.24	402.15	17,352.42	112,520.42	76,232.62	63,826.11	48,694.31	58,159.51	360.70
2032	167.68	409.30	17,248.25	118,376.26	82,734.26	67,534.01	50,842.25	62,285.59	371.46
2033	174.37	416.50	17,141.35	124,490.37	89,726.32	71,406.32	53,084.05	66,703.29	382.53
2034	181.33	423.75	17,031.94	130,873.17	96,768.12	75,080.78	55,792.39	71,908.22	396.55
2035	188.57	431.05	16,922.05	137,550.45	104,804.83	79,278.86	58,271.59	77,033.70	408.51
2036	196.10	438.41	16,810.03	144,520.26	113,436.71	83,658.36	60,861.90	82,525.80	420.83
2037	203.93	445.82	16,696.06	151,794.50	122,704.15	88,225.60	63,568.90	88,411.61	433.54
2038	212.07	453.29	16,575.14	159,335.78	132,650.12	92,987.09	66,348.69	94,649.28	446.31
2039	220.54	460.81	16,439.82	167,071.22	142,617.75	97,469.39	69,601.83	101,841.78	461.79
2040	229.34	468.39	16,292.47	175,015.09	154,004.64	102,614.36	72,400.74	108,659.74	473.79
2041	238.50	476.02	16,135.03	183,180.74	166,211.97	107,973.27	75,207.48	115,772.94	485.42
2042	248.02	479.94	15,972.62	190,130.79	178,401.26	112,987.79	77,143.00	121,804.39	491.11
2043	257.92	483.76	15,806.70	197,222.90	191,355.25	118,155.41	79,067.49	128,051.51	496.47
2044	268.22	487.51	15,638.50	204,489.41	204,118.63	122,878.40	81,611.02	135,567.60	505.44
2045	278.93	491.21	15,469.06	211,943.42	218,686.71	128,349.72	83,593.70	142,429.85	510.64
2046	290.06	494.84	15,299.29	219,598.06	234,165.45	133,990.81	85,607.26	149,609.23	515.78
2047	301.64	498.41	15,129.95	227,466.63	250,605.96	139,805.17	87,661.46	157,136.43	520.94
2048	313.68	501.92	14,961.70	235,562.63	268,061.95	145,796.34	89,766.28	165,044.77	526.15
2049	326.21	505.36	14,795.11	243,899.86	286,589.84	151,967.91	91,931.94	173,370.55	531.47
2050	339.23	508.73	14,630.66	252,492.47	306,248.94	158,323.49	94,168.98	182,153.33	536.96
2051	352.77	512.04	14,468.78	261,355.02	327,101.54	164,866.72	96,488.31	191,436.30	542.66
2052	366.86	515.28	14,313.00	270,562.97	349,213.04	171,601.27	98,961.70	201,389.62	548.96
2053	381.50	518.45	14,163.52	280,137.74	372,652.12	178,530.86	101,606.89	212,086.71	555.93
2054	396.73	521.54	14,020.46	290,101.50	397,490.89	185,659.22	104,442.28	223,607.84	563.62
2055	412.57	524.57	13,883.88	300,476.54	423,805.00	192,990.11	107,486.43	236,039.48	572.12
2056	429.04	527.52	13,750.26	311,205.79	451,673.87	200,527.34	110,678.46	249,295.52	581.05
2057	446.17	530.39	13,616.27	322,223.39	481,180.77	208,274.71	113,948.69	263,257.69	590.04
2058	463.98	533.19	13,481.97	333,533.36	512,413.08	216,236.06	117,297.30	277,958.60	599.07
2059	482.50	535.92	13,347.38	345,139.42	545,462.42	224,415.26	120,724.15	293,431.42	608.14
2060	501.77	538.56	13,212.55	357,044.91	580,424.83	232,816.19	124,228.72	309,709.70	617.24

); under the 10% offset scenario, the economic value of forest carbon sink peaks at CNY 689.78/km² in 2044, gradually declining to CNY 664.29 per km² by 2060 (Figure 7b,

Table A7. NPV of FCS under 10% condition.

Year (a)	Newly Added Forest Area (km2)	Q—Annual Carbon Sink Capacity (tCO2/km2)	∑P—Total Price Discounting (CNY/tCO2)	P—Revenue Discounting 2024 (10 k CNY)	C—Cost (10 k CNY)	Cost Discounting 2024 (10 k CNY)	NPV 2024 (10 k CNY)	NPV—Economic Value of FCS (10 k CNY)	NPV—Value per Unit Area (CNY/km2)
2024	122.59	353.59	21,505.77	93,223.85	40,942.17	40,942.17	52,281.68	52,281.68	426.46
2025	127.49	360.38	21,436.62	98,488.31	46,101.80	44,946.67	53,541.64	54,917.66	430.76
2026	132.58	367.21	21,351.70	103,949.48	50,282.24	47,794.06	56,155.42	59,078.90	445.61
2027	137.87	374.10	21,262.65	109,667.25	54,791.53	50,775.28	58,891.97	63,550.23	460.94
2028	143.38	381.03	21,169.73	115,652.59	59,652.86	53,895.17	61,757.43	68,355.05	476.75
2029	149.10	388.02	21,073.19	121,916.97	64,575.95	56,881.23	65,035.73	73,833.56	495.20
2030	155.05	395.06	20,975.11	128,483.59	70,189.76	60,277.00	68,206.59	79,423.40	512.24
2031	161.24	402.15	20,873.87	135,354.99	76,232.62	63,826.11	71,528.88	85,432.66	529.84
2032	167.68	409.30	20,769.70	142,544.28	82,734.26	67,534.01	75,010.27	91,893.24	548.03
2033	174.37	416.50	20,662.80	150,065.16	89,726.32	71,406.32	78,658.84	98,839.54	566.83
2034	181.33	423.75	20,537.08	157,806.58	96,768.12	75,080.78	82,725.80	106,621.43	587.98
2035	188.57	431.05	20,368.78	165,567.06	104,804.83	79,278.86	86,288.20	114,071.02	604.91
2036	196.10	438.41	20,166.89	173,380.05	113,436.71	83,658.36	89,721.69	121,658.29	620.38
2037	203.93	445.82	19,937.84	181,267.61	122,704.15	88,225.60	93,042.01	129,402.81	634.54
2038	212.07	453.29	19,686.53	189,245.46	132,650.12	92,987.09	96,258.37	137,316.73	647.50
2039	220.54	460.81	19,416.83	197,325.35	142,617.75	97,469.39	99,855.96	146,109.80	662.51
2040	229.34	468.39	19,131.89	205,516.44	154,004.64	102,614.36	102,902.09	154,436.47	673.39
2041	238.50	476.02	18,834.35	213,826.05	166,211.97	107,973.27	105,852.78	162,947.73	683.22
2042	248.02	479.94	18,531.07	220,585.36	178,401.26	112,987.79	107,597.57	169,890.41	684.99
2043	257.92	483.76	18,224.01	227,384.08	191,355.25	118,155.41	109,228.67	176,898.19	685.86
2044	268.22	487.51	17,914.82	234,254.73	204,118.63	122,878.40	111,376.34	185,012.06	689.78
2045	278.93	491.21	17,604.92	241,207.07	218,686.71	128,349.72	112,857.35	192,290.27	689.39
2046	290.06	494.84	17,295.49	248,250.57	234,165.45	133,990.81	114,259.76	199,683.02	688.42
2047	301.64	498.41	16,987.58	255,394.59	250,605.96	139,805.17	115,589.42	207,198.34	686.90
2048	313.68	501.92	16,682.05	262,648.46	268,061.95	145,796.34	116,852.11	214,844.93	684.91
2049	326.21	505.36	16,379.67	270,021.55	286,589.84	151,967.91	118,053.63	222,632.34	682.49
2050	339.23	508.73	16,081.08	277,523.36	306,248.94	158,323.49	119,199.87	230,571.18	679.69
2051	352.77	512.04	15,786.83	285,163.56	327,101.54	164,866.72	120,296.84	238,673.29	676.56
2052	366.86	515.28	15,501.70	293,033.36	349,213.04	171,601.27	121,432.09	247,117.46	673.61
2053	381.50	518.45	15,225.93	301,150.95	372,652.12	178,530.86	122,620.09	255,948.12	670.90
2054	396.73	521.54	14,959.67	309,535.04	397,490.89	185,659.22	123,875.82	265,214.47	668.50
2055	412.57	524.57	14,703.01	318,204.36	423,805.00	192,990.11	125,214.24	274,969.65	666.48
2056	429.04	527.52	14,455.98	327,178.11	451,673.87	200,527.34	126,650.77	285,272.05	664.91
2057	446.17	530.39	14,218.55	336,475.89	481,180.77	208,274.71	128,201.18	296,185.48	663.84
2058	463.98	533.19	13,990.65	346,117.68	512,413.08	216,236.06	129,881.62	307,779.58	663.35
2059	482.50	535.92	13,772.18	356,123.90	545,462.42	224,415.26	131,708.64	320,130.24	663.48
2060	501.77	538.56	13,563.00	366,515.37	580,424.83	232,816.19	133,699.17	333,320.11	664.29

). In the 15% offset scenario, the economic value of forest carbon sink peaks at approximately CNY 892.90/km² (CNY 4.02 billion in total) before declining to CNY 801.76/km² by 2060 (Figure 7c,

Table A8. NPV of FCS under 15% condition.

Year (a)	Newly Added Forest Area (km2)	Q—Annual Carbon Sink Capacity (tCO2/km2)	∑P—Total Price Discounting (CNY/tCO2)	P—Revenue Discounting 2024 (10 k CNY)	C—Cost (10 k CNY)	Cost Discounting 2024 (10 k CNY)	NPV 2024 (10 k CNY)	NPV—Economic Value of FCS (10 k CNY)	NPV—Value per Unit Area (CNY/km2)
2024	122.59	353.59	24,939.92	108,110.29	40,942.17	40,942.17	67,168.12	67,168.12	547.89
2025	127.49	360.38	24,907.28	114,433.86	46,101.80	44,946.67	69,487.19	71,273.01	559.05
2026	132.58	367.21	24,853.39	120,997.26	50,282.24	47,794.06	73,203.19	77,014.19	580.89
2027	137.87	374.10	24,790.13	127,861.08	54,791.53	50,775.28	77,085.79	83,183.16	603.34
2028	143.38	381.03	24,717.96	135,036.95	59,652.86	53,895.17	81,141.79	89,810.27	626.40
2029	149.10	388.02	24,637.33	142,536.95	64,575.95	56,881.23	85,655.71	97,242.95	652.20
2030	155.05	395.06	24,550.51	150,384.83	70,189.76	60,277.00	90,107.84	104,926.38	676.72
2031	161.24	402.15	24,456.09	158,583.62	76,232.62	63,826.11	94,757.52	113,176.47	701.90
2032	167.68	409.30	24,354.46	167,146.86	82,734.26	67,534.01	99,612.84	122,033.25	727.78
2033	174.37	416.50	24,220.84	175,905.64	89,726.32	71,406.32	104,499.32	131,309.65	753.04
2034	181.33	423.75	24,020.53	184,573.32	96,768.12	75,080.78	109,492.54	141,119.85	778.23
2035	188.57	431.05	23,770.11	193,214.68	104,804.83	79,278.86	113,935.82	150,620.54	798.73
2036	196.10	438.41	23,480.28	201,866.09	113,436.71	83,658.36	118,207.73	160,283.99	817.35
2037	203.93	445.82	23,158.67	210,550.21	122,704.15	88,225.60	122,324.61	170,129.04	834.25
2038	212.07	453.29	22,811.12	219,281.91	132,650.12	92,987.09	126,294.82	180,165.04	849.55
2039	220.54	460.81	22,442.22	228,071.14	142,617.75	97,469.39	130,601.75	191,097.21	866.50
2040	229.34	468.39	22,055.73	236,924.50	154,004.64	102,614.36	134,310.14	201,573.99	878.92
2041	238.50	476.02	21,654.76	245,846.15	166,211.97	107,973.27	137,872.88	212,238.85	889.89
2042	248.02	479.94	21,247.50	252,920.50	178,401.26	112,987.79	139,932.71	220,945.74	890.84
2043	257.92	483.76	20,836.26	259,977.59	191,355.25	118,155.41	141,822.17	229,684.08	890.52
2044	268.22	487.51	20,423.01	267,051.86	204,118.63	122,878.40	144,173.47	239,492.80	892.90
2045	278.93	491.21	20,009.42	274,151.43	218,686.71	128,349.72	145,801.71	248,422.02	890.64
2046	290.06	494.84	19,596.91	281,283.94	234,165.45	133,990.81	147,293.13	257,412.90	887.44
2047	301.64	498.41	19,186.71	288,456.74	250,605.96	139,805.17	148,651.57	266,463.46	883.38
2048	313.68	501.92	18,779.85	295,676.98	268,061.95	145,796.34	149,880.63	275,571.35	878.50
2049	326.21	505.36	18,377.23	302,951.73	286,589.84	151,967.91	150,983.82	284,733.98	872.86
2050	339.23	508.73	17,979.62	310,288.04	306,248.94	158,323.49	151,964.55	293,948.68	866.52
2051	352.77	512.04	17,587.68	317,692.97	327,101.54	164,866.72	152,826.25	303,212.83	859.51
2052	366.86	515.28	17,207.07	325,270.44	349,213.04	171,601.27	153,669.17	312,720.76	852.44
2053	381.50	518.45	16,838.08	333,037.30	372,652.12	178,530.86	154,506.45	322,505.34	845.36
2054	396.73	521.54	16,480.88	341,010.85	397,490.89	185,659.22	155,351.63	332,603.25	838.36
2055	412.57	524.57	16,135.59	349,208.33	423,805.00	192,990.11	156,218.22	343,054.16	831.50
2056	429.04	527.52	15,802.23	357,647.38	451,673.87	200,527.34	157,120.04	353,901.95	824.87
2057	446.17	530.39	15,480.77	366,345.95	481,180.77	208,274.71	158,071.24	365,194.81	818.51
2058	463.98	533.19	15,171.15	375,322.33	512,413.08	216,236.06	159,086.26	376,985.61	812.50
2059	482.50	535.92	14,873.23	384,595.14	545,462.42	224,415.26	160,179.87	389,332.26	806.90
2060	501.77	538.56	14,586.86	394,183.36	580,424.83	232,816.19	161,367.17	402,298.09	801.76

).

These findings suggest that increasing clearing caps and offset ratios can significantly enhance the marginal economic benefits of forestry carbon sink projects and advance the timing of peak value realization. This demonstrates the catalytic effect of regional carbon trading mechanisms in unlocking the economic potential of forest carbon sink, especially when aligned with stronger emission reduction mandates and carbon neutrality goals. Therefore, we recommend that national and regional policymakers consider raising the upper limit for the FCS offset ratio and establishing stable, forward-looking pricing mechanisms to increase the attractiveness of the forest carbon sink. This would support the synergistic enhancement of both ecological and economic values in carbon markets.

4. Discussion

4.1. Mechanisms of Forest Carbon Sink Variation and Regional Strategies for Sink Enhancement

This study shows that the forest resources and carbon sink capacity of Shenmu City will increase significantly under the continuation of the established afforestation and management policies. By leveraging historical data from 2010 to 2023, we validated the predictive accuracy of the GM (1,1) gray forecasting model, which achieved a mean relative error of only 5.45%. This high level of accuracy highlights the model’s applicability for forecasting forest carbon sink dynamics under data-scarce conditions. Forest carbon sink in Shenmu City shows a steady growth trend between 2010 and 2023, and is expected to reach 20.67 million tonnes of carbon sinks by 2060. This steady growth trend aligns with the nationwide trajectory identified in China’s Ninth National Forest Resource Inventory, reflecting the cumulative effects of sustained afforestation efforts and a younger forest stand age structure [7,44,45]. If current forestry policies remain unchanged, the GM (1,1) model forecasts that forest coverage in Shenmu will approach 50% around 2035 (Figure 3). However, substantial regional heterogeneity affects the carbon sink potential, particularly due to differences in water availability and vegetation types. In arid and semi-arid areas such as the Loess Plateau, where annual precipitation often falls below 400 mm, limited hydrological conditions and high evapotranspiration suppress vegetation growth and slow the rate of carbon accumulation. As a result, forest carbon sink growth in these regions is relatively constrained compared to more humid zones [46,47]. Empirical evidence confirms that regional climate change exerts differentiated impacts on forest succession and carbon storage capacity, underscoring the importance of incorporating localized ecological baselines into carbon sink assessments [48,49,50].

It should be acknowledged that the current forest carbon sink in Shenmu and the broader Loess Plateau remains insufficient to offset regional fossil fuel emissions. A considerable gap persists between the scale of existing forestry development and the demands of China’s “dual-carbon” strategy. Achieving carbon peaking by 2035 and net-zero emissions by 2060 will require scaling up both forest coverage and carbon density per unit area. Given land constraints, Shenmu City must optimize its land use structure and prioritize areas most suitable for afforestation and ecological restoration. Forest succession—from middle-aged to mature stands—will naturally enhance sink efficiency, as is reflected in the exponential growth pattern forecasted in this study.

Furthermore, measures such as promoting mixed forest cultivation and forest closure should maximize the use of natural means of sink enhancement, while attention should also be paid to the role of artificial interventions (e.g., rainwater harvesting and utilization, soil improvement) in enhancing the survival rate of afforestation and the efficiency of carbon sink. Studies have shown that stand-structure complexity (e.g., multi-layered canopies and tree-size diversity) is often a stronger predictor of long-term carbon storage than species richness alone [51]. A global meta-analysis covering 84 experimental sites confirmed that mixed-species plantations of conifers and broadleaves exhibit, on average, 70% higher above-ground biomass carbon density compared to monocultures in the same region [52].

Our findings align with national trends in China, confirming a positive trajectory in FCS growth due to persistent afforestation efforts. Compared to studies from Europe [53] and Latin America [54], which emphasize climatic gradients and biodiversity effects, Shenmu’s relatively constrained growth is predominantly attributed to its semi-arid climate and limited water resources. Similar climatic constraints were observed in China’s Inner Mongolia region [55], indicating a regional consistency in challenges faced. Conversely, European studies noted higher carbon sink potentials driven by more favorable hydrological conditions, emphasizing the climatic dependency of carbon sequestration efficacy.

Notably, forest carbon sink development is inherently long-term and requires sustained policy support and investment continuity. Shenmu’s experience illustrates that large-scale ecological restoration yields substantial carbon sink benefits only after decades of accumulation. In assessing sink potential, cost-effectiveness must be a critical consideration: as marginal afforestation costs rise, the per-unit cost of new carbon sink will inevitably increase. Policymakers must therefore determine how to strategically allocate limited public and private capital to optimize sink enhancement pathways.

Despite the large growth potential of forest carbon sink, it is still difficult to offset the emissions pressure brought by high-carbon industries through the growth rate of forest carbon sink. The structural tension between industrial growth and ecological conservation heightens the scarcity and value of carbon sink. Scenario simulations in this study reveal that policy-driven increases in the offset ratio can elevate carbon sink economic value by 18–22%. However, suppose industrial emission reduction, energy transition, and technological pathways (e.g., CCUS, soil carbon sink) are not taken in tandem. In that case, there is still a high degree of uncertainty in achieving carbon neutrality by 2060. This issue has been emphasized in several studies, suggesting that constructing a composite carbon sink system is a key guarantee for realizing regional carbon neutrality targets [56,57].

The findings of this study directly contribute to SDG 13 (Climate Action) and SDG 15.3 (Land Degradation Neutrality). By quantifying economically viable carbon sink strategies in ecologically fragile regions, the proposed forest carbon sink pricing and compensation model supports the development of low-carbon pathways tailored for arid and semi-arid zones. Specifically, the afforestation and land restoration policies modeled for Shenmu City promote sustainable land use practices, addressing both carbon mitigation and land rehabilitation, thereby offering actionable insights for the localized implementation of the UNCCD Land Degradation Neutrality target.

4.2. Applicability and Uncertainty Analysis of Regional FCS Pricing Model

This study presents an integrated economic valuation of FCS in Shenmu City, highlighting significant implications for carbon neutrality strategies in resource-based cities on the Loess Plateau. As a nature-based climate change mitigation pathway, forest carbon sink is playing an increasingly prominent role in regional carbon-neutral strategies. Shenmu City, as a resource-based city with a heavy industrial structure and high energy consumption intensity, has a forest carbon sink that provides a vital avenue for achieving negative emissions. Based on the GM (1,1) prediction model with 2010–2023 historical data, this study predicts that the carbon sink in Shenmu City will continue to grow and reach 7.04 million tonnes in 2060, with forest cover expanding to nearly 50% by 2035. Our marginal abatement cost analysis revealed that implementing dynamic carbon pricing mechanisms, particularly with guaranteed minimum prices, substantially increases the economic viability and stability of carbon market transactions. Specifically, raising policy offset ratios from 5% to 15% resulted in an increase in economic value due to heightened market scarcity. This predicted trend is in line with the development direction of the national forest carbon sink, which further proves the key role of expanding forest area, optimizing structure, and strengthening management in enhancing the potential of carbon sink [58,59].

The regionally adapted forest carbon sink pricing model and NPV-based valuation framework proposed in this study account for afforestation, maintenance, and project development costs. Coupled with the introduction of a government-guaranteed purchase mechanism, the model provides a viable pathway for realizing the economic value of forest carbon sink under market-based conditions. While the model is theoretically sound and policy-relevant, its practical applicability and inherent uncertainties warrant careful consideration.

Internationally, commonly applied valuation frameworks for FCS include the opportunity cost method, NPV method, market equilibrium modeling, and dynamic optimization techniques. Among them, the opportunity cost method is suitable for areas where the value of alternative land uses is clear, especially under the condition of land resource scarcity [60,61], whereas the NPV method used in this paper has been widely used in the economic benefit analysis of long-term ecological projects [19,62,63]. The model’s integration of market equilibrium theory adds analytical depth by capturing the dynamic interplay between supply and demand, which is pivotal for forecasting carbon credit prices [64,65,66,67].

The model adopted in this study combines the NPV and market equilibrium theories, fully considers the regional ecological and economic characteristics and the special characteristics of resource-based regions, and reflects high regional applicability. Especially in the Loess Plateau region, the fragile ecology and special industrial structure determine the necessity of government market intervention (e.g., guaranteed price mechanism), which is closely in line with the regional reality. Although the price model and the methodology for assessing the value of forest carbon sink in this study have good regional applicability, there may be uncertainties that affect their accuracy and long-term reliability. There is uncertainty in market demand and the policy environment. The cost of afforestation and maintenance per unit of carbon sink continues to rise, and this study predicts that the cost per unit of carbon aggregation will be close to 832 CNY/tCO₂ in 2060, which constitutes rigid support for the formation of a carbon sink trading price. However, in practice, the actual realization of carbon sink prices will also be influenced by changes in the intensity of future carbon emission constraint policies and the supply–demand dynamics of the carbon market.

Secondly, there is uncertainty about cost measurement and technological progress. Long-term forecasts of afforestation costs, management costs, and development costs rely on historical data and existing technology levels, ignoring future technological advances, inflation changes, and fluctuations in market conditions. Therefore, the cost assumptions in this paper may overestimate future costs, thus affecting the accuracy of carbon sink pricing. The NPV method is extremely sensitive to changes in discount rate, and the discount rate in this study adopts the risk-free interest rate, but in the real economic environment, inflation and changes in macro interest rates will significantly affect the level of the discount rate, which may bring about prediction errors. Lastly, the model assumes ideal ecological conditions. Forest carbon sink is highly susceptible to climatic shocks, pest outbreaks, and wildfires, which can substantially diminish actual sequestration outcomes. These risks from extreme climates, ecological disasters, and land use are not fully captured in the current framework.

The regional carbon pricing model presented demonstrates considerable practical applicability for Shenmu, particularly through integrating guaranteed transaction prices. Comparable applications in Europe reveal similar governmental interventions effectively stabilizing market prices [68]. However, our model faces uncertainties similar to those identified in Brazilian carbon offset initiatives [69], where policy volatility substantially influenced carbon market dynamics. The predictive uncertainties in long-term afforestation costs parallel those in Eastern European carbon projects, where inflation and technological advancements caused significant cost variances [70]. In summary, while the regional pricing model proposed here demonstrates strong contextual relevance and theoretical coherence, its limitations should be addressed in future applications through parameter sensitivity testing, multi-scenario simulation, and hybrid model refinement.

4.3. Regional Carbon Market Design and Policy Recommendations

This study quantifies the dynamic evolution of forest carbon sink prices and NPV under varying policy offset ratios by introducing a guaranteed floor price mechanism and simulating a low-carbon scenario. The results indicate that raising the offset ratio significantly enhances the marginal economic value of the forest carbon sink. Under a 15% offset scenario, the peak NPV per unit reaches CNY 7095 in 2044, substantially higher than the CNY 4115 observed under a 5% scenario (

Table 3. Maximum economic value of FCS under different offset ratios.

Offset Ratios	Economic Value of FCS (CNY/km2)	Year (a)
5%	617.24	2060
10%	689.78	2044
15%	892.90	2044

).

Our results advocate for targeted ecological restoration initiatives supported by market-based instruments in resource-based regions, ensuring sustainability in both ecological and economic domains. Nevertheless, the proposed pricing model is susceptible to uncertainties arising from ecological disturbances, fluctuating market demands, and policy volatility, indicating a need for ongoing refinement through advanced forecasting methods and broader scenario analyses. This result quantitatively verifies the incentive effect of the offset ratios on the formation of the economic value of the carbon sink. While the initial gains from a higher offset ratio are substantial, returns diminish over time due to the limited incremental capacity of the forest carbon sink. Notably, the slight decline in value in the middle and late stages of the higher offset ratio scenario does not mean that the forest carbon sink becomes unprofitable, but rather that the growth is saturated and the value is maintained at a high level. Importantly, economic value remains high across all scenarios, even when carbon emissions are kept constant, emphasizing the great potential of forest carbon sink as a “carbon asset” in the coming decades.

Our policy simulations support a moderate increase in offset ratios to maximize economic returns from FCS projects. This conclusion aligns with practices in the EU Emissions Trading System (ETS), which similarly demonstrates increased economic benefits through enhanced market flexibility [71]. Contrastingly, restrictive offset policies in Latin American carbon markets (e.g., Brazil’s early REDD+ initiatives) resulted in lower market liquidity and investment uncertainty [72].

Several policy actions are recommended to unlock this potential for building a regional carbon market. Firstly, a clear policy and supporting management mechanism should be formulated, including standardized rules for accounting, trading, and compliance auditing in order to ensure market transparency and credibility. Secondly, the FCS offset ratio cap should be raised moderately to guide more enterprises to fulfill compliance obligations and broaden their participation in the carbon market. Thirdly, the guaranteed purchase mechanism of carbon sink prices should be strengthened, with the government or a designated platform acting as a bottoming buyer to stabilize revenue streams and mitigate market risk for FCS stakeholders. These mechanisms have demonstrated efficacy in pilot regions and offer replicable pathways for broader application.

Furthermore, integration with the national carbon market should be accelerated to harmonize methodologies, certification systems, and trading platforms. A regional monitoring and evaluation system should also be established to continuously assess forest sink outputs and value realization, ensuring adaptive policy adjustment.

5. Conclusions

This study takes Shenmu City, a typical resource-based city in China, as a case to explore three key dimensions: (1) the dynamic evolution of forest carbon sinks, (2) a localized carbon pricing mechanism, (3) the economic valuation of forest-based carbon assets. Based on historical data (2010–2023), the GM (1,1) gray model was used to project carbon sink trends through 2060. A regional pricing model, incorporating labor value and cost structures, was developed to simulate economic returns under different compliance offset ratios. Although forest coverage and carbon sequestration capacity continue to increase—averaging 9.98% annually—the growth remains insufficient to fully offset industrial emissions. This highlights the strategic importance of enhancing forest carbon sinks in long-term carbon neutrality pathways. The study proposes a “guaranteed transaction price” mechanism that integrates cost accounting and labor value theory. This approach enhances price stability and market viability for forestry projects. Scenario analysis shows that higher offset ratios significantly increase the economic value of carbon sinks, even under conservative low-carbon scenarios. These findings suggest that establishing a well-regulated regional carbon market—supported by institutional frameworks, fiscal incentives, and proactive governance—can facilitate ecological product monetization and green transformation in resource-dependent areas. Shenmu’s experience provides a replicable model for similar high-emission regions such as the Loess Plateau. Looking forward, efforts should focus on real-time carbon sink monitoring through remote sensing, dynamic accounting, and market innovations. Integrating forest carbon assets into land use planning, rural revitalization, and green finance will help unlock their full ecological and economic potential.