Introduction

After the Industrial Revolution, the global economy experienced rapid development. However, this progress inevitably accelerated environmental pollution, resulting in a situation where overall environmental conditions have deteriorated, even though some localized improvements have been made. The capacity for ecological restoration has lagged far behind the rate of destruction, leading to an expanding ecological deficit. As the largest developing country, China faces the same challenge. Therefore, The Third Plenary Session of the 20th Central Committee of the Communist Party of China explicitly emphasized the need to “improve the value realization mechanism of ecological products”. In ecological economics, this aligns with classic work distinguishing the potential benefits nature provides—ecosystem service value (ESV)—from the institutional and market processes that realize that value in the economy. In policy terms, this means moving from recognizing ESV to ensuring that institutional and market arrangements can convert it into tangible outcomes. Establishing and enhancing this mechanism is a pivotal task for advancing the “Two Mountains” theory and promoting the comprehensive green transformation of economic and social systems. Because forest ecosystems concentrate China’s carbon sequestration and multiple ecological functions, they naturally become the core carrier of ecological product supply; hence, the effectiveness of the “Two Mountains” transformation is ultimately revealed by how efficiently forest-based value is converted into market and fiscal outcomes. In recent years, China has made significant progress in exploring pathways to realize the value of ecological products. However, challenges such as the insufficient supply of ecological products and the urgent need for product upgrading remain prominent1. Forest ecosystems, which possess the highest biomass and the most extensive coverage in China, are also the richest in natural resources. Due to their high carbon sequestration capacity and multifunctionality, forest ecosystems become the core carrier of ecological product supply. They provide the “potential value” side (ESV), while policy and market arrangements determine how much of that potential is actually realized. The realization of their ecological value is critical for addressing the shortage of ecological products and achieving high-quality development2. Accelerating the value conversion of forest ecological products is an effective strategy to meet the growing demand for ecological products and environmental services while alleviating issues of “golden pollution” and “green poverty” in certain regions3. The efficiency of value conversion for forest ecological products, which integrates economic, social, and ecological benefits, is central to balancing economic growth and environmental protection. It directly determines the actual effectiveness of the transformation from “lucid waters and lush mountains” to “golden mountains and silver mountains” and serves as a key indicator for measuring the implementation effect of the “Two Mountains” concept. Understanding its temporal and spatial evolution and identifying its influencing factors are crucial for achieving Chinese-style modernization, which envisions harmonious coexistence between humanity and nature.

Transformative efficiency of forest ecological product values refers to the incorporation of the value of the ecological products contained in forests into the production function along with other input factors, such as capital, labour and land, in order to assess the efficiency of the output of these factors4. This concept is distinct from the traditional notion of ecosystem service value (ESV) in that it emphasizes the efficiency of the transformation process—how effectively jurisdictions convert forest-based ecosystem values into realized economic and social outcomes—rather than the magnitude of ESV itself5. By contrast, ESV analyses typically appraise the monetary value of services (e.g., carbon sinks, water purification) and their spatial–temporal evolution—sometimes also reporting “conversion rates”—but they do not explicitly evaluate process efficiency or governance performance in turning potential value into outcomes6. In addition, while eco-compensation mechanisms aim to compensate for the loss of ecosystem services caused by human activities, they do not directly address the efficiency of transforming the value of ecological goods. Ecological compensation focuses more on ensuring that the beneficiaries of ecosystem services (e.g., local communities, governments) compensate for the economic costs borne by the areas that provide these services, without reflecting the effectiveness of transforming these services into sustainable economic benefits. Thus, the efficiency of transformation of forest ecological product values represents a unique, dynamic measure of how efficiently forest ecological products are transformed into economic and social values over time, providing complementary perspectives on the value of ecosystem services and compensation mechanisms, which tend to be more static in nature. This usage also connects to established realization channels—such as payments for ecosystem services, eco-compensation, ecological fiscal transfers, and carbon markets—highlighting that conversion outcomes depend on policy design, market depth, and administrative capacity.

The concept of ecological products was first introduced in 2010 through the National Main Functional Area Plan, which categorized ecological products alongside agricultural, industrial, and service products as essential for human development. Ecological products are defined as “natural elements that maintain ecological security, ensure ecological regulatory functions, and provide a high-quality living environment”. Derived from natural ecosystems, they offer services such as provisioning, regulation, support, and culture. These products can enter production systems as inputs, undergo transformation, and eventually become marketable commodities, thus enabling the conversion of “green mountains and green hills “into” golden mountains and silver mountains”7. Building on this foundation, scholars define forest ecological products as the transformation of forest resources into goods that satisfy human needs. This transformation is primarily achieved through the value realization mechanism of ecological products and the ecological compensation mechanism4. Consistent with contemporary environmental-economic accounting practice (e.g., SEEA-EA), this paper therefore treats transformative efficiency as a governance-performance concept—how effectively jurisdictions convert forest-based ESV into budgetary, market, and household benefits over time. Currently, domestic and international research on the conversion efficiency of forest ecological product values focuses on three main areas: (1) Construction of forest ecological product value transformation efficiency indicator system. In terms of input indicators, Zhan Liulu et al. used forest resources, land resources and major forest products of each prefecture-level city as forest ecological capital input indicators, and selected the number of forestry employees as forestry social capital input indicators8. However, this selection fails to include the ecological value of forests in the system. Kong Fanbin et al. further measured the value of forest ecological products in Zhejiang cities and incorporated it into the input indicator system, which mainly includes the value of carbon sequestration and oxygen release, water conservation value, sedimentation reduction value and climate regulation value of forests4. In terms of output indicators, scholars have used the total output value of the forestry industry as the desired output indicator, and Dong Xiaoger et al. further introduced forestry exhaust, solid waste and wastewater as the non-desired outputs for further estimation9. (2) Methods for measuring the efficiency of forest ecological product value conversion10,11. Currently, there are more common measurements, mainly Super-SBM4, Super-EBM9 and Super-NSBM8. (3) Impact factor studies. Scholars have utilized the Super-SBM model to calculate the direct conversion efficiency from gross ecological product(GEP) to gross domestic product(GDP).This process benefits rural development by increasing farmers ‘income, narrowing the urban-rural gap, and reducing disparities in county-level development, resulting in positive externalities12,13. Regarding influencing factors, existing studies have qualitatively examined how the digital economy enables the realization of ecological product value. These studies use case analyses to summarize pathways through which the digital economy facilitates rural ecological product value realization14,15,16 or empirically analyze its impact on the efficiency of forest ecological product value realization7.

In conclusion, existing studies provide valuable insights for this study, but there is still room for further expansion. First, most relevant studies have focused on analysing the conversion efficiency of forest ecological products in a single province or a specific region, ignoring the complexity and diversity of collective forest areas in southern China as a representative region. This study takes several southern provinces, including Fujian, Jiangxi, Guangxi, and Guizhou, as the study area, and explores the differences in the conversion efficiency of forest ecological product values among different regions, providing a cross-regional comparative perspective and bridging the gap in the study of regional differences in existing studies.

Second, existing literature mainly relies on traditional efficiency assessment methods, such as Data Envelopment Analysis (DEA) and Solow Residual Method, etc. Although these methods are effective, they are less likely to take into account the inevitable externality and resource loss in the process of transforming ecological product values. In this study, we used a combination of the super-efficient margin model and the value equivalence method, which allowed us to measure the multidimensional efficiency of forest ecological products more precisely, especially for the valuation of different ecological service functions. This methodological innovation provides a more detailed measurement tool for the transformation of the value of ecological products.

Third, most of the existing literature focuses on static analyses of the conversion efficiency of forest ecological products, and less on the spatio-temporal evolution process and the dynamic changes of efficiency differences. This study, on the other hand, provides insights under the analysis of time-series data by introducing a spatio-temporal analysis framework to delve into the dynamic changes and regional differences in the efficiency of forest ecological product transformation in the southern collective forest areas during the period from 2012 to 2021. We also pay special attention to the factors behind regional differences, such as policy environment, infrastructure, and market demand, which are usually under-appreciated in the existing literature.

Fourth, although existing studies have put forward policy recommendations regarding the transformation of forest ecological products, most of them remain only at the macro policy level and lack specific operational and localised guidelines. This study not only reveals the differences in the efficiency of forest ecological product conversion, but also provides targeted policy recommendations for different regions, especially in terms of policy support, market incentives, and ecological compensation, and proposes specific implementation paths.

Research methods, indicators, and data sources

Overview of the study area

The southern collective forest area(18°10′–34°38′N,103°36′–123°10′E) is situated in southeastern China and constitutes one of the three major forest regions (Northeast Forest Area, Southwest Forest Area, and Southern Forest Area). This region encompasses five coastal provinces—Zhejiang, Fujian, Guangdong, Guangxi, and Hainan—and five inland provinces—Anhui, Hunan, Hubei, Jiangxi, and Guizhou. The southern collective forest area plays a pivotal role in China’s collective forest tenure system reform due to its abundant forest resources and diverse ecosystems. Spanning 87.32 million hectares, this area includes 26.32 million hectares of public welfare forests and 37.53 million hectares of natural forests, accounting for 56.2% of the region’s land area and 30.1% of the total national forest area.

Under the guidance of the “Two Mountains” theory, this region has taken the lead in exploring the marketization of forest ecological products, carrying significant policy demonstration value. For example, Fujian Province was the first in China to initiate pilot projects for forest carbon trading, utilizing market mechanisms to convert the economic value of forest carbon sinks. In contrast, Guangxi has actively promoted eco-tourism, leveraging forest landscapes and cultural resources to attract tourists and realize the market value of ecological products. Lishui City in Zhejiang Province has developed a mechanism for realizing the value of ecological products by establishing a transaction platform, becoming a pilot city for the realization of ecological product values nationwide. Additionally, southern collective forest areas have been at the forefront of innovation in ecological compensation mechanisms. Fujian has implemented an ecological public welfare forest compensation system, providing economic compensation for forests with critical ecological functions to protect the interests of ecological conservationists. Guangxi has integrated ecological compensation with poverty alleviation, developing an “eco-poverty alleviation” model to help impoverished areas gain economic benefits through forest protection. These mechanisms not only improve the efficiency of forest ecological product value conversion but also offer important references for the formulation of national ecological compensation policies.

Furthermore, the regional cooperation and coordinated development model in southern collective forest areas holds substantial potential for broader application. For instance, Fujian and Jiangxi have established an inter-regional ecological compensation mechanism to promote the sharing and complementarity of ecological resources, while Guangdong and Guangxi have jointly developed eco-tourism projects to foster coordinated regional economic growth. These cross-regional cooperation mechanisms not only reduce development disparities between regions but also enhance the overall efficiency of ecological resource utilization, providing valuable policy insights for broader application.

Therefore, studying the value conversion efficiency of forest ecological products in this region offers valuable insights for constructing mechanisms to realize the value of forest ecological products.

Research methods

Super-EBM model with unexpected output

In the study of the value conversion efficiency of forest ecological products, the Super-EBM model proves to be more applicable than the SBM model and the Malmquist index. First, Super-EBM can handle decision-making units with super-efficiency, accurately identifying high-efficiency regions or units that surpass the frontier. This capability is crucial for evaluating outstanding forest ecological conservation and resource utilization models17. Second, through more refined efficiency assessments, Super-EBM helps decision-makers identify the optimal balance between ecological conservation and economic development, whereas the SBM model focuses more on underutilized resources, and the Malmquist index emphasizes changes in efficiency over time18. Finally, Super-EBM is well-suited to the diverse transformation models of forest ecological products, providing precise measurement of super-efficiency performance across different units19. This avoids the over-reliance on dynamic factors found in the Malmquist index, offering a more accurate assessment of the current moment. Therefore, Super-EBM holds distinct advantages in evaluating the current efficiency of forest ecological product value conversion.

The non-oriented model with VRS was chosen because it does not assume proportional changes in inputs and outputs, which is realistic for forestry ecological product conversion where adjustments can be asymmetric. The inclusion of the constraint ∑γj = 1 ensures VRS, accounting for the fact that DMUs might not be operating at optimal scale due to size or policy constraints. The super-EBM model of unexpected output represents an advanced analytical approach for efficiency measurement, extending the applicability of traditional Data Envelopment Analysis (DEA) models. This model accounts for undesirable outputs generated during production processes and incorporates boundary limits for efficiency values to prevent overestimation or misjudgment. By addressing the limitations of radial and non-radial models, the super-EBM model enhances the accuracy of efficiency calculations.

To tackle the inherent issue of unexpected outputs in decision-making unit ranking and lifecycle analysis, this study builds upon the methodology established by Shi Pengfei et al.20,21,22, developing a customized super-EBM model with unexpected outputs as follows:

$${r}^{*}=\text{m}in\frac{\:{\uptheta\:}-{\epsilon\:}_{x}{\sum\:}_{r=1}^{m}\frac{{{w}_{r}^{-}s}_{r}^{-}}{{x}_{i0}}}{{\phi\:+{\epsilon\:}_{y}{\sum\:}_{r=1}^{s}\frac{{{w}_{r}^{+}s}_{r}^{+}}{{y}_{r0}}+\epsilon\:}_{z}{\sum\:}_{p=1}^{q}\frac{{{w}_{p}^{z-}s}_{p}^{z-}}{{z}_{i0}}}$$
(1)
$$s.t.\left\{ {\begin{array}{*{20}l} {\sum\limits_{{j = 1}}^{n} {x_{{ij}} \gamma _{j} + s_{i}^{ - } = \theta x_{{i0}} } \left( {i = 1,2, \ldots ,m} \right)} \\ {\sum\limits_{{j = 1}}^{n} {y_{{rj}} \gamma _{j} - s_{r}^{ + } = \theta y_{{r0}} } \left( {r = 1,2, \ldots ,s} \right)} \\ {\sum\limits_{{j = 1}}^{n} {z_{{pj}} \gamma _{j} - s_{p}^{ - } = \varphi z_{{p0}} } \left( {p = 1,2, \ldots ,q} \right)} \\ {\gamma _{j} \ge 0,\;s_{i}^{ - } ,\;s_{r}^{ + } ,\;s_{p}^{ - } \ge 0} \\ \end{array} } \right.$$
(2)

Here, r*: The optimal efficiency score for the DMU under evaluation. A value ≥ 1 indicates efficiency. θ: The radial efficiency score for inputs. φ: The radial efficiency score for desirable outputs. xij: The amount of input i for DMU j. yrj: The amount of desirable output r for DMU j. zpj: The amount of undesirable output pp for DMU j. si−: Slack variable for input i, representing excess input. Sr+: Slack variable for desirable output r, representing shortage in output. spz−: Slack variable for undesirable output p, representing excess in undesirable output. γj: Intensity weight for DMU j, used to construct the efficient frontier. wr−, wr+, wpz−: Exogenously determined weights for input i, desirable output r, and undesirable output p, respectively. In this study, we set all weights to 1 for simplicity and fairness, implying equal importance for all variables. εx, εy, εz: Parameters combining radial and non-radial slack variables. Typically, εx = εy = εz = 0.0001 to ensure the non-radial components are adequately considered. m, s, q: Number of inputs, desirable outputs, and undesirable outputs, respectively. n: Number of DMUs.

Kernel density estimation

Kernel density estimation is a non-parametric statistical method used to estimate the probability density function of random variables. Compared to histograms, kernel density estimation produces a smooth density curve that more clearly illustrates the distribution characteristics of the data, minimizes discretization errors, and eliminates the need to assume a specific data distribution. This approach is suitable for diverse datasets with unknown distributions, making it broadly applicable. Drawing on the work of Wang et al.23, the kernel density function curve in this study is constructed as follows:

$$f\left( x \right) = \frac{1}{{Nh}}\sum\limits_{{i = 1}}^{N} {{\text{K}}\left( {\frac{{X_{i} - x}}{h}} \right)}$$
(3)

Here, K{·} represents the kernel density function, describing the weight contributed by all sample points xi in the neighborhood of x, and h denotes the bandwidth of the kernel density estimation. The kernel density function and bandwidth settings are based on the methodology outlined by Sun Chang and Wu Fen24. we selected the Gaussian kernel function for its smoothness and differentiability, which is expressed as:

$$K\left(\mu\:\right)=\frac{1}{\sqrt{2\pi\:}}\text{e}\text{x}\text{p}(-\frac{1}{2}{\mu\:}^{2})$$

The variable h denotes the bandwidth, which critically determines the smoothness of the estimated density curve. The optimal bandwidth is calculated as:

$$h=1.06\times\:\widehat{\sigma\:}\times\:{n}^{-\frac{1}{5}}$$

\(\widehat{\sigma\:}\) is the standard deviation of the sample data, and \({n}^{-\frac{1}{5}}\) is the sample size. This method remains widely advocated in recent econometric literature for its reliability and reproducibility in applied research. This data-driven approach ensures an objective and reproducible estimation of the density function, reducing subjectivity in the interpretation of distribution features such as “degree of dispersion” and “peak characteristics”.

Dagum Gini coefficient method

The Dagum Gini coefficient method does not require specific assumptions about data distribution, making it adaptable to various forms of data distributions. This method provides a more intuitive reflection of the overall inequality in the value conversion efficiency of forest ecological products. Following Zhang Zhuoqun et al.25,26, the Dagum Gini coefficient and its subgroup decomposition method are employed, using the formula:

$$G = \frac{{\sum\nolimits_{{j = 1}}^{k} {\sum\nolimits_{{h = 1}}^{k} {\sum\nolimits_{{i = 1}}^{{n_{j} }} {\sum\nolimits_{{r = 1}}^{{n_{k} }} {\left| {y_{{ji}} - y_{{hr}} } \right|} } } } }}{{2n^{2} \mu }}$$
(4)

In this formula, G represents the overall Gini coefficient; k denotes the number of regional groups (e.g., coastal and inland provinces); n is the total number of provinces; yji and yhr are the efficiency levels of forest ecological product value conversion in province I of region j and province r of region h, respectively; and µ is the mean efficiency value across all provinces. Subgroup decomposition satisfies G = Gw+Gnb+Gt, where Gw represents within-group inequality, Gnb captures the inequality between groups, and Gt accounts for transvariation.

Convergence model

σ Convergence

In this paper, the coefficient of variation is used to test the degree of dispersion in the level of efficiency in the conversion of forest ecological product values, which is calculated by the formula:

$$\sigma _{t} = \sqrt {\frac{1}{N}} \sum\limits_{{i = 1}}^{N} {\left( {lnETFP_{{it}} - \overline{{lnETFP_{t} }} } \right)^{2} }$$
(5)
$$CV_{t} = \sqrt {\frac{1}{N}\sum\limits_{{i = 1}}^{N} {\left( {ETFP_{{it}} - \overline{{ETFP_{t} }} } \right)^{2} } } /\overline{{ETFP_{t} }}$$
(6)

included among these, σt denotes the standard deviation of the logarithmic value of forest ecological product value conversion efficiency in year t; CVt denotes the coefficient of variation of the conversion efficiency of forest ecological product values in year t; N indicates the number of provinces; ETFPit indicates the efficiency of conversion of forest ecological product values in the i province of the t year; \(\overline{{{\text{lnETFP}}_{{\text{t}}} }}\) mean value of logarithmic value conversion efficiency of forest ecological products for all provinces in the year indicated; \({\text{ETFP}}_{{\text{t}}}\) denotes the mean value of the conversion efficiency of forest ecological product values in all provinces in year t.

β-convergence

β-convergence is to examine the growth of forest ecological product value conversion efficiency between regions from the perspective of growth rate, so as to judge whether the lagging region of forest ecological product value conversion efficiency has the characteristics of “catching up” and convergence. β-convergence is categorised into absolute β-convergence and conditional β-convergence based on whether control variables are included or not. Among them, absolute β-convergence refers to the convergence of the forest ecological product value conversion efficiency itself only considered; conditional β-convergence refers to the convergence of the forest ecological product value conversion efficiency between regions after controlling the influencing factors. The traditional β-convergence based on panel data is as follows:

$${\text{ln}}\left( {\frac{{ETFP_{{i,t + 1}} }}{{ETFP_{{it}} }}} \right) = \alpha + \beta {\text{ln}}\left( {ETFP_{{it}} } \right) + u_{i} + v_{t} + \varepsilon _{{it}}$$
(7)

Included among these, i denotes area, t denotes time; ETFPi, t+1、ETFPit indicates the level of development of forest ecological product value conversion efficiency in area i in periods t + 1, t; ln(ETFPi, t+1 / ETFPit) denotes the annual rate of increase in the level of efficiency of conversion of the value of forest ecological products in the region from t to t + 1.; ui indicates area fixed effects, vt indicates a time fixed effect, εit denotes a random disturbance term that is independently and identically distributed; β is the convergence factor, If β < 0 and passes the significance test, it indicates that the conversion efficiency of the value of forest ecological products in China shows a converging trend, and vice versa indicates a diverging trend.

Variable selection and processing

Calculation of value conversion efficiency of forest ecological products

(1) Value evaluation of forest ecological products based on unit area equivalent factor method. The method based on unit area equivalent factor is usually used to evaluate the value of ecosystem services. By uniformly converting the values of different types of ecosystem services into value equivalents per unit area, comparison and evaluation can make the values of different types of ecosystem services compared on the same scale. Taking the southern collective forest area as a sample, this paper assigns various services of forest ecosystem through classification, including food production, raw material production, water supply and other supply services. Gas regulation, climate regulation, environmental purification, hydrological regulation and other regulation services; Support services such as soil conservation, maintenance of nutrient cycle, biodiversity and cultural services such as aesthetic landscape, so as to calculate various values of forest ecological products27. As the value equivalent factor table calculated by Xie Highland is based on the national sample and represents the national average level of ecosystem services, this paper adopts the grain correction method to revise it based on the actual production capacity of the southern collective forest area28. See formula(8) and formula(9) for the specific correction formulas. After the correction, three staple foods, namely rice, wheat and corn, were selected to calculate the economic value of 1hm2 forest ecological product equivalent factor (see Eq. 10)29. Finally, based on the remote sensing image data, the forest cover information is obtained Information, multiplied by the revised equivalent factor table and the economic value of the equivalent factor of 1 hm2 forest ecological products, so as to estimate the total value of forest ecological products (see formula (11)).

$${\uptheta\:}=\frac{{S}_{r}}{S}$$
(8)
$${\text{E}}_{kj}={{\uptheta\:}\times\:\text{e}}_{kj}$$
(9)
$${\text{F}} = \frac{1}{7}\sum\limits_{{i = 1}}^{n} {\frac{{M_{i} P_{i} Q_{i} }}{T}}$$
(10)
$${\text{EPV}} = \sum\limits_{{k = 1}}^{n} {\sum\limits_{{j = 1}}^{n} {A_{k} \times \:E_{{kj}} \times \:F} }$$
(11)

In formula (1), θ is the revision coefficient of equivalent factor, and Sr and S respectively represent the average grain yield of each province and the whole country in the southern collective forest area; k = 1,2,3,4,indicating the classification of forest ecosystem corresponding to the equivalent factor table, j = 1,2…11, indicating the service functions in the equivalent factor table, ekj indicating the revised equivalent factor table, ekj being the original equivalent factor table; F is the economic value of ecological product equivalent factor per unit area(yuan/hm2), i is the type of food crops, Mi, Pi and Qi represent the sown area, average market price and yield per unit area of the i-th crop respectively, and T is the total area of all food crops; EPV represents the total value of forest ecological products, and A k represents the cover area of the k-th forest ecosystem.

Estimation of Value Conversion Efficiency Using the EBM Model. Referring to existing studies10,11,30,31, the efficiency of value conversion was calculated for the period 2012–2021, with 2011 as the base year. As shown in Table 1, input indicators include labor force, land, capital, and forest ecological product values (supply, regulation, support, and cultural services), represented by forestry employment, fixed-asset investment, forest land area, and calculated ecosystem service values. The expected output is the total forestry output value, while the unexpected output accounts for pollution during forestry production. Following existing research32,33, forestry secondary industry output, total industrial output, and industrial waste indices are used as proxies for forestry pollution.

The selection of input and output indicators in this study integrates the economic, social and ecological dimensions of forest ecological product value transformation. The input indicators include labour, land, capital and forest ecological product values (supply services, regulating services, supporting services and cultural services), which reflect the core resources required in the production process. Labour (number of forestry employees) and capital (investment in forestry fixed assets) are traditional factors of production, directly affecting production efficiency; land (area of forested land) and the value of forest ecological products reflect inputs of ecological resources and the service function of the ecosystem.

Output indicators include gross forestry output value (desired output) and industrial pollution (non-desired output). Gross forestry output value is an important indicator of the economic value of forest ecological products, reflecting the efficiency of converting ecological products into economic output. Industrial pollution, as a non-desired output, reflects the negative impacts of the production process on the environment, and is a factor that must be considered when assessing the efficiency of transforming the value of ecological products. By incorporating industrial pollution into the model, this study is able to assess the comprehensive efficiency of forest ecological product value transformation more comprehensively, avoiding the bias of focusing only on economic outputs and ignoring environmental costs.

Industrial pollution indicators (including industrial SO₂ emissions, industrial solid waste generation and industrial wastewater discharges) were used as unintended outputs to reflect the negative environmental impacts of forestry production processes. These indicators were chosen because they provide a direct measure of the level of environmental pollution caused by industrial production activities and are currently commonly used in academia9.

Table 1 Details the input-output index system for calculating the value realization efficiency of forest ecological products.
Full size table

Variables affecting the value conversion efficiency of forest ecological products

Understanding the factors influencing the value conversion efficiency of ecological products is essential for improving this efficiency in southern collective forest areas. This study draws on existing research and contextualizes findings with the actual conditions34,35. The value conversion efficiency of ecological products serves as the dependent variable, while six explanatory variables are selected as follows:

  1. (1)

    Economic Development Level: The level of economic development directly influences the supply and demand for ecological products. A robust economic environment increases demand, thereby promoting the marketization and value enhancement of ecological products. This study uses per capita GDP as a proxy for measuring economic development levels.

  2. (2)

    Digital Inclusive Finance: Digital inclusive finance mitigates the limitations of traditional financial services by reducing information asymmetry in the value conversion process of ecological products. Additionally, it broadens financing channels for producers lacking resources. The Peking University Digital Inclusive Finance Index is employed as the measurement indicator.

  3. (3)

    Degree of Government Intervention: Government policies play a dual role: guiding effective resource allocation and influencing the dynamics of ecological product value conversion. By incorporating government intervention into the analysis, this study provides a more nuanced understanding of value conversion processes. The proportion of fiscal expenditure to GDP is selected as an indicator to measure the degree of government intervention.

  4. (4)

    Forestry Industrial Structure: The rationality of the forestry industrial structure determines the effective integration of forest ecological resources. An optimized structure leverages forest resource advantages to facilitate the value conversion of ecological products. Following Tang Zhan and Li Hongmei36, this study employs the industrial structure hierarchy coefficient method to calculate the optimization degree of forestry industrial structure. The specific calculation formula is as follows:

    $$FIS = \sum\limits_{{i = 1}}^{n} {W_{i} Q_{i} ~} \quad ~\left( {n = 1,2,3} \right)$$
    (12)

    Among the variables, FIS represents the optimization degree of the forestry industrial structure. Wirefers to the weights assigned to the primary, secondary, and tertiary forestry industries, with weights of 1,2, and 3 respectively, following the methodology of Li Wei and Wan Zhifang and the evolutionary law of industrial structure37. Qidenotes the proportion of the output value of each forestry industry (primary, secondary, and tertiary) to the total forestry output value.

  5. (5)

    Urbanization Rate: Urbanization influences ecological awareness and shifts consumer preferences toward green consumption, thereby increasing demand for ecological products. Additionally, urbanization provides technical support for enhancing the value conversion of ecological products. The proportion of the urban population to the total population is used as a measure of the urbanization rate.

  6. (6)

    Transportation Infrastructure: Transportation infrastructure plays a dual role: it directly affects the circulation and utilization efficiency of ecological products, and its quality and coverage are key to promoting their value transformation. Furthermore, improved transportation infrastructure supports regional economic development, which in turn drives demand for ecological products. The logarithm of highway mileage is selected to measure the level of transportation infrastructure.

  7. (7)

    Level of Human Capital: Human capital has a direct impact on the efficiency of resource allocation and the ability of labour to apply technology. In the transformation of forestry eco-products, areas with higher human capital are more likely to adopt scientific planting techniques, precise management and eco-friendly processing techniques, thus enhancing resource utilisation efficiency and product added value. Therefore, this paper measures the level of human capital by the number of years of education per capita in rural areas.

  8. (8)

    Technical Level: On the one hand, a high level of technology directly affects the production and processing efficiency of ecological products. Advanced technology can improve resource utilisation and reduce production costs, thereby increasing the transformation value of ecological products. On the other hand, a higher level of technology is often accompanied by innovation ability, which allows collective forest areas to develop new products or improve existing products, thus enhancing added value. Therefore, in this paper, the number of agricultural technicians in public economic enterprises and institutions is selected to represent and logarithmically processed.

Data sources

This study uses data from 10 provinces in the southern collective forest area for the period 2012–2021.The data sources include information on agricultural production inputs, socio-economic statistics, and land use.

Agricultural production inputs

Data on sown area, average market price, and unit-area output of major grain crops were obtained from the National Compilation of Cost and Benefit Data of Agricultural Products and provincial grain and material reserve bureaus.

Input-output data for forest ecological products

Forestry employee data, fixed-asset investment, forest land area, and related statistics were sourced from provincial statistical yearbooks, the China Forestry and Grassland Statistical Yearbook, and the China Environmental Statistical Yearbook.

Land use data

Remote sensing image data were retrieved from NASA’s MODIS land cover data products (2012–2021). This dataset is consistent with the classification of forest ecosystems by Xie Gaogao et al. (2015), minimizing bias from land use type reclassification. The temporal resolution is annual, and the spatial resolution is 500 m.

Influencing factor variable data

This part of the data mainly comes from the statistical yearbooks of each province, the “China Rural Statistical Yearbook”, and the annual “Digital Inclusive Finance Development Index Report”.

It is important to note that this study cross-validated data from different sources. Specifically, for data obtained from local government reports and academic studies, we conducted comparisons and adjustments to ensure consistency and accuracy.

Temporal and Spatial evolution and regional differences in value conversion efficiency of forest ecological products

Time series evolution characteristics

The value conversion efficiency of forest ecological products in the southern collective forest area from 2012 to 2021 is shown in Table 2 (ETFP stands for efficiency of value conversion of forest production products, EC stands for efficiency of technological change, and TC stands for technological progress). During this period, the efficiency values were consistently above 1, indicating that production units improved output relative to input compared to the base year (2011). This improvement reflects enhanced resource utilization efficiency.

Table 2 Provides a detailed breakdown of the value conversion efficiency index and its components for the Southern collective forest area.
Full size table

The evolution of value conversion efficiency can be divided into three stages:

Growth stage (2012–2013)

Efficiency increased from 1.1398 in 2012 to 1.1809 in 2013, with technical efficiency rising from 0.9161 to 0.9360 and technological progress advancing from 1.2820 to 1.3624. Despite the improvement, technical efficiency values remained below 1, highlighting the need for further resource optimization. This stage was characterized by “technology-driven progress”, fueled by advancements in ecological spatial planning, restoration projects, and supportive policies for ecological construction.

Fluctuation stage (2014–2019)

Efficiency values fluctuated, declining from 1.1163 in 2014 to 1.0941 in 2019. This trend was influenced by economic pressures and policy adjustments. Technical efficiency increased modestly from 0.7966 in 2014 to 1.0479 in 2020, but its low baseline and slow growth indicate challenges in technology adoption, particularly in remote regions. Meanwhile, technological progress values dropped from 1.7480 in 2014 to 1.0384 in 2019 due to diminishing marginal returns as technology matured.

New development stage (2020–2021)

Efficiency rebounded from 1.0238 in 2020 to 1.2339 in 2021. Although technical efficiency decreased slightly (from 1.0023 to 0.9401), technological progress surged from 1.0906 to 1.4588, driven by innovations such as intelligent forestry management systems, drone technology, remote sensing, and big data analytics. These advancements significantly enhanced resource utilization.

Analysis of spatial characteristics of value conversion efficiency of forest ecological products in southern collective forest areas

The natural breakpoint method is applied to classify the value conversion efficiency of forest ecological products in southern collective forest areas into four levels. This method aims to minimize the variance within classes and maximize the variance between classes, identifying inherent groupings in the data distribution. The classification into four levels was determined based on the data’s distribution characteristics for the entire study period (2012–2021) to ensure comparability across years. The breakpoints are calculated algorithmically to identify the natural thresholds in the dataset, which are as follows:

  1. 1.

    Low conversion efficiency area (ETFP < 1.0026)

  2. 2.

    Moderate-low conversion efficiency area (1.0026 ≤ ETFP < 1.1011)

  3. 3.

    Moderate-high conversion efficiency area (1.1011 ≤ ETFP < 1.1740)

  4. 4.

    High conversion efficiency area (1.1740 ≤ ETFP < 1.3130)

The distribution of value conversion efficiency for 2012, 2015, 2018, and 2021 is shown in Figs. 1, 2, 3 and 4. The average conversion efficiency across the study period remained predominantly in the high-conversion efficiency category. Only Jiangxi Province and Guangxi Zhuang Autonomous Region consistently demonstrated high efficiency, while Guangdong Province and Hainan Province frequently fell into the low conversion efficiency category.

Fig. 1
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Spatial aggregation map of value conversion efficiency of forest ecological products in southern collective forest areas (2012). (Map sourced from the Ministry of Natural Resources, GS (2023)2767; base map unmodified)

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Fig. 2
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Spatial aggregation map of value conversion efficiency of forest ecological products in southern collective forest areas (2015). (Map source same as Fig. 1)

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Fig. 3
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Spatial aggregation map of value conversion efficiency of forest ecological products in southern collective forest areas (2018). (Map source same as Fig. 1)

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Fig. 4
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Spatial aggregation map of value conversion efficiency of forest ecological products in southern collective forest areas (2021). (Map source same as Fig. 1)

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Temporal trends

2012: Conversion efficiency exhibited regional concentration, with Fujian Province and Guangxi Zhuang Autonomous Region exceeding 1.1. In contrast, Hainan Province recorded the lowest efficiency (< 1.0), due in part to the implementation of the National Land Planning Outline, which emphasized ecological restoration and protection.

2015: Efficiency displayed regional shifts. Fujian and Guangxi maintained high efficiency, while Guangdong and Hainan experienced declines, nearing 1.0. Severe flooding during this year damaged forest ecosystems in coastal areas, negatively impacting conversion efficiency.

2018: Fujian Province’s efficiency increased significantly (> 1.2), while Guangdong’s fell below 1.0. Guangxi saw a slight decline but remained in the high-efficiency category.

2021: Efficiency improved markedly in Fujian, Zhejiang, and Guangxi, with Guangxi achieving the highest value (> 1.5). Guangdong recovered to > 1.2, while Hainan dropped to just above 1.0.

By observing, we can find that coastal provinces such as Fujian and Guangxi are consistently more efficient than inland areas such as Jiangxi and Guizhou. This disparity is quantitatively supported by key indicators. For instance, from 2012 to 2021, the average annual ecological compensation in Fujian and Guangxi was approximately 45% and 38% higher, respectively, than in Jiangxi and Guizhou. Similarly, the intensity of forestry technology investment (measured by R&D expenditure as a percentage of forestry output value) in coastal provinces was, on average, 1.8 times that of inland provinces. This difference can be attributed to several factors. Coastal provinces such as Fujian and Guangxi benefit from stronger economic foundations and more robust policy support, which is reflected in higher ecological compensation amounts and forestry technology investment intensity, particularly in the marketization of ecological products and ecological compensation mechanisms. For instance, Fujian and Guangxi have implemented innovative policies like carbon trading and eco-tourism, which have significantly enhanced the economic value of forest resources and contributed to rural revitalization. In contrast, inland provinces like Jiangxi and Guizhou, despite their rich natural resources, lag behind due to less developed market mechanisms and weaker policy implementation. At the same time, Coastal regions generally possess more advanced infrastructure and higher technological levels, which facilitate the production and circulation of ecological products. For example, the widespread use of intelligent forestry management systems, drone technology, and remote sensing in Fujian has significantly improved resource utilization efficiency. Inland provinces, however, face challenges in adopting these advanced technologies due to limited infrastructure and lower levels of technological investment.

At the same time, the analysis of this paper suggests that the reasons affecting the change in efficiency are divided into the following two points: On the one hand, Technological advancements, particularly in intelligent forestry management systems, drone technology, and remote sensing, have played a crucial role in enhancing the efficiency of value conversion. For instance, Fujian Province has seen significant efficiency improvements due to the widespread adoption of these technologies. In contrast, inland provinces like Jiangxi and Guizhou have been slower in adopting such innovations, resulting in slower efficiency growth. And on the other, policy changes, such as the implementation of the Returning Farmland to Forests program and ecological compensation mechanisms, have had a significant impact on efficiency. Coastal provinces have been more effective in implementing these policies, leading to faster efficiency improvements. Inland provinces, however, have faced challenges in policy execution, which has hindered their efficiency gains.

Regional differences in value conversion efficiency of forest ecological products in Southern collective forest areas

To explore spatial differentiation in forest ecological product value conversion efficiency (ETFP), this study uses the Dagum Gini coefficient to analyze intra-and inter-regional differences in southern collective forest areas from 2012 to 2021. The results are summarized in Table 3.

Table 3 Gini coefficient of collective forest area in Southern China and its source decomposition.
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Overall differences

The Gini coefficient fluctuated over the study period, rising from 0.0564 in 2012 to a peak of 0.1559 in 2019, before declining to 0.0851 in 2020 and 0.0812 in 2021.

Between 2012 and 2019, policies such as Returning Farmland to Forests and ecological compensation supported technological progress and development. After 2019, slower economic growth and external factors (e.g., Sino-US trade friction) reduced forest product demand, dampening conversion efficiency.

Intra-regional differences

Coastal Regions: Gini coefficients remained relatively stable, increasing from 0.0654 in 2012 to 0.0809 in 2021. Coastal areas benefited from earlier economic development, diversified industries, stable economic foundations, and strategic advantages (e.g., ports and free trade zones).

Inland Regions: Gini coefficients showed greater fluctuation but also increased overall, from 0.0416 in 2012 to 0.0704 in 2021. The lag in technology adoption, policy support, and market development contributed to slower efficiency improvements in inland regions.

Inter-regional differences

The Gini coefficient for inter-regional differences increased from 0.0591 in 2012 to 0.0866 in 2021, reflecting a widening gap between coastal and inland areas. This disparity is attributed to differences in economic development, resource endowment, and policy implementation.

Hypervariable density

Hypervariable density remained relatively stable, peaking between 2018 and 2020 before decreasing to 0.0270 in 2021. This indicates that extreme efficiency values in certain regions had limited impact on overall inequality.

Contribution rates

Coastal regions’ contribution to total differences decreased from 47.59% in 2012 to 46.69% in 2021.

Inland regions’ contribution increased significantly from 9.57% in 2012 to 20.08% in 2021, while inter-regional contributions fell from 42.83% to 33.23%.

Summary of findings

The overall inequality in value conversion efficiency initially increased (2012–2019) before decreasing (2019–2021).

Intra-regional disparities, especially in inland areas, have grown, while inter-regional and coastal disparities have declined.

These findings underscore the need to address inefficiencies in inland areas through enhanced technical training, infrastructure development, and policy support.

Future policies should aim to bridge gaps between inland and coastal regions, thereby fostering balanced regional development and improving overall value conversion efficiency in southern collective forest areas.

Dynamic evolution of value conversion efficiency of forest ecological products in Southern collective forest areas

To analyze temporal agglomeration differences in the value conversion efficiency of forest ecological products, this study uses the Kernel Density Function to estimate changes over time. To clearly depict the dynamic evolutionary trajectory and to capture the potential impacts of major events, we present the kernel density estimation results for four strategically selected time slices in Figs. 2, 3 and 4. The year 2012 represents the starting point of the study. The year 2016 was chosen to observe the medium-term effects after the implementation of the new Environmental Protection Law in 2015. The year 2019 allows us to examine the potential impact of the 2018 trade frictions on the forestry economy after a one-year lag. Finally, 2021 marks the endpoint of our study period, revealing the most recent state of efficiency distribution. The evolution trends are presented in Figs. 5, 6 and 7.

Key Findings:

Distribution characteristics over time

From 2012 to 2014, the conversion efficiency exhibited a high degree of concentration, with sharp peaks indicating clustered efficiency values.

In 2016, multiple peaks emerged, reflecting a wider distribution across various efficiency ranges.

In 2019 and 2020, the distribution became more dispersed with lower peaks, indicating reduced agglomeration and broader efficiency distribution.

Over time, the distribution shifted from “high on the left and low on the right” to “low on the left and high on the right”. This indicates an increasing agglomeration effect at high efficiency levels, while low-efficiency areas weakened.

Shifting peaks

The crest of the distribution moved rightward, signifying growth in the number of regions with high efficiency and a reduction in low-efficiency regions.

The high-efficiency area expanded, and the overall efficiency level improved, driven by advancements in ecological civilization and implementation of the “Two Mountains” theory.

Regional patterns

Coastal areas demonstrated a stronger rightward shift compared to inland areas, showing higher growth in efficiency.

Kernel density analysis revealed that coastal regions exhibited a highly convergent trend with sharper, more concentrated peaks, reflecting reduced absolute differences in efficiency. However, inland areas displayed relatively flatter and more dispersed peaks due to lower economic development, imperfect forestry industry chains, and lower industrial added value.

Distribution ductility and convergence

The kernel density distribution curve showed a “left-tail” phenomenon, indicating convergence in efficiency across the region. High-efficiency provinces approached the average level, reducing extreme values and narrowing spatial gaps.

Coastal areas exhibited mild left-tailing, indicating that most provinces had high efficiency, with diminishing disparities.

Inland areas showed no significant left-tailing, indicating slower improvements and persistent regional disparities.

Polarization and peaks

A bimodal distribution was evident from 2012 to 2017, reflecting significant inter-provincial differences. From 2018 to 2021, the bimodal phenomenon weakened, transitioning toward a unimodal pattern. This suggests narrowing disparities and more balanced development across the region.

Coastal areas were predominantly unimodal, with more evenly distributed improvements, while inland areas exhibited persistent bimodal patterns due to higher regional disparities.

Fig. 5
figure 5

Nuclear density map of value conversion efficiency of forest ecological products in south collective forest area.

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Fig. 6
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Nuclear density map of value conversion efficiency of forest ecological products in coastal area of south collective forest area.

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Fig. 7
figure 7

Nuclear density map of value conversion efficiency of forest ecological products in inland areas of south collective forest area.

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Convergence analysis of value conversion efficiency of forest ecological products

σ Convergence test

The σ convergence test evaluates whether the disparities in value conversion efficiency among regions decrease over time. The specific results are shown in Table 4.

First stage (2012–2016)

The σ value in the southern collective forest area declined from 0.1006(2012) to 0.0737(2016), reflecting narrowing regional disparities.

Coastal areas saw a reduction in σ from 0.1186(2012) to 0.0750(2016), while inland areas experienced a decline from 0.0758 to 0.0693 over the same period.

Overall, this stage showed decreasing disparities, driven by policies promoting ecological restoration, technical advancements, and resource allocation improvements.

Second stage (2017–2021)

The σ value increased significantly from 0.0913(2017) to 0.3707(2018) before gradually declining to 0.1434(2021).

Coastal areas experienced fluctuations, with σ rising from 0.0572(2017) to 0.2301(2019) before stabilizing at 0.1492(2021).

Inland areas showed a pronounced increase in σ from 0.0968(2017) to 0.4331(2018), followed by a decline to 0.1277(2021). This fluctuation was likely due to natural disasters, market volatility, and uneven policy effects.

Overall observations

From 2012 to 2016, the σ convergence indicates narrowing disparities in value conversion efficiency across regions.

From 2017 to 2021, disparities fluctuated significantly, driven by external factors such as policies, natural disasters, and market dynamics. Although disparities eventually narrowed, the overall σ value in 2021 remained higher than in 2016.

Key takeaways

The dynamic evolution of value conversion efficiency reflects an overall improvement in resource utilization and agglomeration at higher efficiency levels.

Coastal areas consistently outperform inland areas, with faster convergence and more balanced development.

Inland regions show slower progress, necessitating targeted interventions such as technological training, infrastructure investment, and enhanced market integration.

While polarization is decreasing, regional disparities persist, requiring sustained efforts to achieve balanced development across southern collective forest areas.

Figures 2, 3 and 4 and Tables provide visual and numerical support for these findings. Let me know if you need assistance refining figures or creating additional visualizations.

Table 4 σ convergence of value conversion efficiency of forest ecological products in south collective forest area.
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Convergence test β

This section examines the convergence of value conversion efficiency using the OLS convergence model. Prior to the analysis, the Hausman test was conducted to determine whether random or fixed effects should be applied. The test statistic (P = 0.1342) supported the null hypothesis at a 10%level, favoring the random effects model. For robustness, a two-way fixed effects model was also utilized. Results for both absolute and relative β-convergence are summarized in Tables 5 and 6.

  1. (1)

    Absolute β-convergence (Table 5).

Findings:

Existence of convergence

There is absolute β-convergence in the overall region, coastal areas, and inland areas.

The β coefficients are significant at the 1%level across all models, indicating that the value conversion efficiency will converge to its steady-state level in the long run, even without considering economic and social factors.

Convergence rates

The overall regional convergence rate (β=−1.402) is higher than that of coastal areas (β=−1.191) but lower than inland areas (β=−1.582).

Coastal areas experience slower convergence due to intense economic activities and high resource pressures, while inland areas benefit from resource abundance and strong policy support, leading to faster convergence.

  1. (2)

    Relative β-convergence (Table 6).

Findings:

Conditional convergence

After accounting for economic and social factors (e.g., digital inclusive finance, economic development, government intervention, forestry industrial structure, urbanization, and transportation infrastructure), conditional β-convergence is observed across all regions.

The β coefficients remain significant at the 1% level, confirming that regions are converging toward steady-state levels under these conditions.

Convergence speed

Conditional β-convergence is faster than absolute β-convergence across all regions.

Coastal areas exhibit the greatest acceleration due to intensive economic activities, technological advancements, and strong policy support, with a convergence rate increase of 0.268.

Inland areas show smaller changes in convergence speed (+ 0.024) due to resource abundance and lower initial development levels, which were already well-captured in the absolute model.

Table 5 Absolute convergence test of value conversion efficiency of ecological products in South collective forest area \(\varvec{\upbeta\:}\).
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Table 6 Relative convergence test of value conversion efficiency of ecological products in South collective forest area \(\varvec{\upbeta\:}\).
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Conclusion

Conclusion

This study investigated the spatiotemporal evolution and convergence of value conversion efficiency for forest ecological products in China’s southern collective forest area from 2012 to 2021. The principal conclusions are as follows:

Temporal trends

The value conversion efficiency demonstrated an overall upward trajectory with periodic fluctuations, evolving through three distinct phases: a technology-driven growth stage (2012–2013), a fluctuation stage influenced by policy and economic factors (2014–2019), and a new development stage propelled by digitalization and ecological technology (2020–2021).

Spatial distribution

Significant regional disparities were identified, with coastal provinces (e.g., Fujian, Guangxi) exhibiting higher efficiency than inland provinces (e.g., Jiangxi, Guizhou). Dagum Gini coefficient decomposition confirmed that inter-regional differences between coastal and inland areas were the primary source of overall inequality.

Convergence analysis

σ-convergence results indicated that regional disparities narrowed from 2012 to 2016 but experienced fluctuations between 2017 and 2021. Both absolute and conditional β-convergence were significant, confirming a long-term convergence trend across regions, with inland areas showing faster convergence rates due to policy support and resource advantages.

Enlightenment

This study holds significant theoretical and practical implications, particularly in advancing China’s “Two Mountains” philosophy and the construction of eco-civilization. Theoretically, the research contributes to the field of ecological economics by quantifying and optimizing the value conversion efficiency of forest ecological products, thereby enriching the theory of sustainable development. Notably, by employing methods such as value equivalence and the super-efficiency frontier model, this study offers a comprehensive framework for assessing ecological product efficiency, furthering research on spatial inequality. Practically, the findings provide valuable insights for policy formulation, particularly within the context of China’s eco-civilization strategy. By revealing regional disparities in the efficiency of forest ecological product conversion, this research supports the design of differentiated policies tailored to local characteristics. Coastal regions, for example, could enhance efficiency through international cooperation and technological innovation, while inland areas may benefit from increased investments in infrastructure and technological support to improve resource management. The study also underscores the importance of market-based incentives to promote ecological conservation, facilitating the economic integration of ecological products and advancing the sustainable development of eco-civilization.

Based on these findings, the following policy recommendations are proposed:

Strengthen policy support and technological advancement in inland areas

Inland regions possess rich natural resources and ecosystems critical to sustainable development but lag in economic development and technological application. To address this:

Increase financial investments in inland provinces such as Jiangxi and Guizhou, focusing on eco-friendly initiatives through subsidies and grants.

Promote advanced forestry technologies such as drone monitoring, precision fertilization, and remote sensing to enhance resource management and utilization efficiency.

Establish regional innovation hubs in inland areas to foster the adoption of smart forestry technologies and improve the efficiency of forest ecological product value transformation.

Enhance regional coordination and cooperation

Establish cross-regional collaboration mechanisms that leverage the complementary strengths of coastal and inland areas. For example:

Coastal provinces like Fujian and Guangxi can share advanced technologies and market access with inland provinces, while inland provinces can provide raw materials and ecological services.

Promote resource sharing and technology transfer to reduce ecological and economic disparities between regions, fostering balanced development.

Develop joint projects between coastal and inland regions, such as eco-tourism initiatives or cross-regional forest product supply chains.

Accelerate the marketization of forest ecological products

Integrate ecological products into existing agricultural markets, establish dedicated counters for ecological products, and utilize online trading platforms.

Develop certification systems and eco-labeling for forest ecological products, and encourage enterprises and local governments to build brands, thereby increasing product competitiveness.

Differentiate market strategies based on the type of forest ecological product: For provisioning services (e.g., timber, non-timber forest products), focus on improving supply chain efficiency and market access. For regulating services (e.g., carbon sequestration, water purification), develop carbon credit markets and payment for ecosystem services (PES) schemes. For cultural services (e.g., eco-tourism, recreational activities), promote eco-tourism packages and cultural heritage branding.

Develop differentiated forest ecological compensation standards

Tailor compensation based on the specific ecological functions of forest services, such as water conservation, biodiversity protection, soil preservation, and climate regulation.

Provide higher compensation for regions with fragile ecosystems and significant restoration tasks, such as the mountainous areas of Jiangxi and Guizhou.

Establish an adjustment mechanism to update compensation standards based on ecological monitoring data and economic development.

Implement performance-based compensation for regions that demonstrate significant improvements in ecological product value transformation efficiency.

Promote digital transformation in forest ecological product value chains

Leverage digital inclusive finance to reduce information asymmetry and broaden financing channels for producers in inland regions.

Develop digital platforms for forest ecological product trading, enabling real-time monitoring of supply chains and enhancing transparency.

Encourage the use of blockchain technology to track the origin and sustainability of forest products, ensuring consumer trust and market competitiveness.

Key insights

This study highlights the importance of targeted interventions to improve the value conversion efficiency of forest ecological products. By addressing regional disparities, fostering technological innovation, and strengthening market mechanisms, policymakers can achieve balanced development and enhance ecological sustainability in southern collective forest areas.

Future outlook

  1. (1)

    Time span: This study analyses data based on the period from 2012 to 2021. Although this time span covers nearly a decade of data and can reveal trends in the transformation efficiency of forest ecological products in various regions in the short term, the short time period of the study fails to adequately reflect the far-reaching impacts of long-term ecological changes, policy adjustments, and market fluctuations on transformation efficiency. Over time, the transformation of the value of forest ecological products may be affected by long-term ecological changes, climate change, and national macro-policy adjustments. Therefore, future research should consider lengthening the time span and conducting longer-term spatial and temporal analyses in order to explore the changing patterns of long-term trends.

  2. (2)

    Regional disparity: The scope of this study is limited to some provinces in the southern collective forest area, such as Fujian, Jiangxi, Guangxi and Guizhou. These regions represent the typical characteristics of the southern forest areas in China, but the situation in other regions may not be exactly the same due to the great differences in ecological conditions, economic development levels and policy environments between regions in China. Especially in the western and northeastern regions, the conversion efficiency of forest ecological products may be quite different from that of the southern regions. Therefore, the results of this study are only applicable within the study area and are difficult to directly generalise to other provinces. Future research could be extended to other regions for cross-regional comparisons to verify the generalisability of the findings of this study.

  3. (3)

    Research methodology: In this study, quantitative analysis methods such as the Super-Efficient Marginal Model (Super-EBM) and the Value Equivalence Method (VEM) were used, which are effective in assessing the conversion efficiency of forest ecological products in different regions. However, there are some limitations in the application of these methods. For example, Super-EBM assesses efficiency by identifying decision-making units (DMUs) beyond the frontier, but in some cases, the model may judge some irrational or accidental high-efficiency units as ‘super-efficiency’ units, which may lead to over-optimistic assessment results. This may lead to over-optimistic results. This problem is particularly acute when the data are volatile. It also fails to explore the specific value chain and industrialisation path of eco-products. Future research can combine qualitative research methods to explore the specific mechanisms and paths of industrialisation of eco-products, so as to further enhance the depth and breadth of the study.