Do Cultural Ecological Policies Deliver Ecological Co-Benefits? A Quasi-Natural Experiment for CEPZs in China

Abstract

China’s Cultural Ecological Protection Zones (CEPZs) are a distinctive policy instrument intended to safeguard intangible cultural heritage through the “integrated” protection of cultural practices and their supporting socioecological environments. However, there remains limited robust causal evidence on whether CEPZs generate measurable ecological co-benefits and whether such benefits come with landscape-structure trade-offs. Using a county-level panel covering 454 counties from 2006 to 2023, we evaluate CEPZs’ impacts on ecosystem quality and landscape patterns through a multi-period DID design with a rich set of socio-environmental controls. Ecosystem quality is proxied by a satellite-derived NDVI, NDWI, and NPP, while landscape pattern outcomes are captured by a composite landscape pattern connectivity index (LPCI) derived from multi-metric landscape configuration indicators. We further test mechanisms using mediators constructed from Morphological Spatial Pattern Analysis (MSPA) (core habitat proportion) and built-up land proportion, and examine heterogeneity by local governance capacity and region. The results show that CEPZ designation significantly increases the NDVI (β = 0.002, p < 0.01) but significantly reduces the LPCI (β = −0.004, p < 0.01). The average effects on the NDWI and NPP are statistically insignificant. Mechanism tests reveal two countervailing pathways: CEPZs increase the share of core habitat (β = 0.009, p < 0.01), which is positively associated with the NDVI, while simultaneously expanding built-up land (β = 0.012, p < 0.01), which offsets greening and drives fragmentation. Heterogeneity analyses suggest that ecological gains are amplified where independent CEPZ management agencies exist and are stronger in western/central China. These findings provide causal evidence that biocultural governance can yield “greening” co-benefits but may undermine landscape integrity unless development pressures are spatially regulated and local institutional capacity is strengthened.

1. Introduction

Across the globe, ecological degradation is accelerating due to the combined impacts of land-use change, pollution, climate change, and resource overexploitation, which collectively erode biodiversity and undermine the capacity of ecosystems to support human well-being [1]. The 2019 IPBES Global Assessment synthesized evidence that nature is declining at unprecedented rates in human history and emphasized that reversing these trends requires “transformative change” in the social, economic, and institutional systems that shape human–environment interactions [2]. At the same time, international conservation organizations have increasingly recognized that ecological outcomes cannot be understood or improved without accounting for the cultural, economic, and governance contexts in which ecosystems exist [3].

Protected areas remain a cornerstone, yet their effectiveness varies widely due to differences in resources, management capacity, enforcement, and local legitimacy [4]. Evidence has repeatedly highlighted that designating protected areas alone does not guarantee outcomes, and some protected lands still experience high human pressures or suffer from weak governance, undermining ecological integrity [5]. Even when protected areas provide measurable benefits, the magnitude of benefits depends on where areas are located and how they are managed [6]. Meanwhile, these traditional protection models frequently overlook the endogenous role of cultural genes, the traditional knowledge systems, belief structures, and land-use practices, in maintaining ecological equilibrium [7,8]. The disconnect between nature and culture has led to a failure in addressing the root causes of environmental degradation in inhabited landscapes.

Therefore, the protection of culture and nature cannot be entirely separated. In fact, the concept of cultural ecology proposed by Julian Steward in 1955 offers valuable insights in this regard. He argued that culture is an adaptive system through which human societies organize production, institutions, and knowledge to navigate environmental constraints. Culture is inherently intertwined with the natural environment, which is shaped by it, yet also exerts influence upon it [9]. This lineage resonates strongly with contemporary social–ecological system (SES) research, which similarly treats human behaviors, institutions, and ecological dynamics as co-evolving components of complex adaptive system stems. The cultural ecology approach emphasizes that long-term ecological stewardship often relies on culturally embedded practices, such as low-intensity agriculture, rotational grazing, protecting sacred sites, and landscape management knowledge [8]. This direction can sustain habitat heterogeneity and ecosystem functions. More importantly, the key point is not that all traditional practices are inherently sustainable, but rather that cultural knowledge systems are central mediators of resource use and therefore transmit to ecological outcomes. This also aligns with the IPBES “nature’s contributions to people” framing, which argues that cultural values and diverse knowledge systems can substantially improve environmental assessments and policy design [10].

The coupling of cultural practices and ecological functions is mediated through Ecosystem Services (ESs) [11]. Beyond provisioning and regulating services, Cultural Ecosystem Services (CESs), which encompass spiritual, esthetic, and educational values, provide the bedrock for community identity and social resilience [11,12]. This matters directly for intangible cultural heritage (ICH). Under UNESCO’s widely used framing, ICH includes not only performing arts and rituals but also “knowledge and practices concerning nature and the universe”, along with traditional craftsmanship and social practices [13]. Many ICH systems are therefore materially dependent on ecosystems; for example, specific species, habitats, seasonal cycles, water regimes, and agricultural landscapes provide the ecological substrates needed for cultural practices to persist [14]. Meanwhile, the traditional ecological knowledge (TEK) embedded in heritage practices often acts as an informal governance mechanism that regulates resource use and preserves landscape heterogeneity [15]. For instance, the maintenance of traditional agroecosystems or sacred groves directly influences soil stability, pollination, and biodiversity [16]. This socioecological feedback loop suggests that methods of traditional cultural protection can facilitate the long-term sustainability of ecosystem services [17].

Systematic promotion of in situ conservation of heritage is a crucial approach to achieving the synergy between cultural and ecological objectives. However, given the current malpractice in intangible cultural heritage (ICH) protection, where excessive focus on the conservation of heritage itself leads to its disconnection from the native environment, constructing regional and holistic conservation pathways has become a key practical direction, as well as a pathway advocated by UNESCO. Cultural Ecological Protection Zones (CEPZs), a localized conservation paradigm pioneered in China, represent a strategic shift from isolated heritage preservation to a holistic reconstruction of the “Human–Nature–Culture” composite system [18]. By design, they aim to protect not only discrete cultural individual ICH items but also the broader cultural–ecological context in which intangible heritage is practiced and reproduced. This policy framing implicitly treats ecological quality as part of the safeguarding infrastructure for cultural continuity, as well as treating cultural practices and governance as part of the institutional infrastructure for ecological stewardship. China’s national-level CEPZs have developed over more than a decade as a distinctive approach to the “holistic” safeguarding of regional cultural systems, and institutionalized a governance model that is explicitly cross-sectoral. In 2007, the Minnan Cultural Ecological Protection (Experimental) Zone was established as the first national-level cultural ecological protection (experimental) zone in China. By 2024, China had established 23 national-level CEPZs and 255 provincial-level zones, indicating substantial governmental commitment to integrating cultural safeguarding and ecological considerations in territorial governance [18]. These developments imply that CEPZs are no longer marginal pilots but have become a significant component of China’s place-based governance landscape.

However, a key scientific and practical challenge remains: Do CEPZs generate measurable improvements in ecological outcomes at landscape scales, and can this culture-embedded hybrid conservation instrument produce ecological co-benefits (such as enhanced ecosystem quality and optimized landscape spatial patterns) over and above their primary cultural mandates?

However, the related fields have not received widespread attention. From heritage and ICH protection studies, the present studies often focus on cultural vitality, tourism development, community perception, and governance coordination, while ecological outcomes remain descriptive or implicit [19,20]. Secondly, ecological policy evaluation methods are mature for conventional protected areas, ecological redlines, or restoration programs, but less developed for hybrid policies whose goals are simultaneously cultural and ecological [21]. Therefore, China’s CEPZ policy, as a new exploration, also provides reference experience and observation objects for the coordinated protection of regional culture and ecology.

To address this, we assess the role of China’s CEPZs in promoting the joint protection and sustainable management of ecosystems. Specifically, it includes two aspects: ecosystem quality improvement and landscape pattern optimization. Conceptually, this study treats CEPZs as a policy intervention that reconfigures the coupled “human–nature–culture” system in designated territories, and explores its effectiveness using a quasi-natural experimental method. The expected contributions are threefold: (a) a policy-relevant evaluation of a hybrid governance instrument; (b) mechanism-oriented hypotheses linking governance, landscape patterns, and ecosystem quality; (c) exploring the policy path and heterogeneity of the collaborative protection of culture and ecology.

2. Methods and Materials

2.1. Hypotheses and Study Areas

A useful starting point is to treat CEPZs not as static “protected areas” in the conventional sense, but as governance interventions that seek to reorganize a region’s coupled human–nature–culture dynamics. CEPZs aim at the holistic protection of ICH within its ecological context and are supported by formal management measures and cross-sectoral coordination. Many ICH domains include knowledge of nature, production practices, and craftsmanship reliant on local ecosystem materials. On one hand, by promoting the intergenerational transmission and viability of such practices, CEPZs may reduce incentives for high-intensity land conversion and maintain land-use mosaics that support biodiversity and ecological functions. On the other hand, by reducing high-intensity land conversion, strengthening ecological restoration aligned with cultural safeguarding, and sustaining culturally embedded stewardship practices, CEPZs should improve vegetation conditions, moisture-related indicators, and overall ecological status, detectable via remote sensing composite indices. Thus, we propose two hypotheses:

H1: 

CEPZ designation has a positive effect on ecosystem quality.

H2: 

CEPZ designation has a positive effect on landscape pattern optimization.

Additionally, the construction of CEPZs is government-led. Unlike traditional nature reserve tools, CEPZs are closer to a form of landscape governance intervention: they involve ecological conservation but simultaneously introduce cultural tourism and public service facilities (e.g., exhibition areas, roads, and service facilities) [22]. Thus, their impact on ecosystems may manifest as a dual effect: while protecting the core conservation area, they also lead to the expansion of construction land and facilities. These two factors, core-area conservation and peripheral development, may indirectly affect ecosystem quality and landscape patterns. Based on this, we propose the following two hypotheses:

H3: 

CEPZs enhance the quality of protected areas, transmitting these effects to ecosystem quality and landscape patterns.

H4: 

CEPZs increase the area of construction land, which in turn influences ecosystem quality and landscape patterns.

The steps for verifying our hypothesis are shown in Figure 1.

Given the variable scales of CEPZs, encompassing county-level, prefecture-level, and cross-municipal or cross-provincial units, all analyses were standardized to the county level to ensure methodological consistency. Counties/districts designated as national-level cultural ecological protection (experimental) areas were defined as the experimental group, while adjacent counties/districts without such designation served as the control group.

The study period for variable statistics spanned 2006 to 2023. To ensure data availability and comparability, counties/districts with severe data missingness were excluded from the analysis. This process yielded a final sample of 213 counties/districts in the experimental group and 249 in the control group (Figure 2).

2.2. Research Data and Variables

2.2.1. Dependent Variables and Data Sources

This study selected four categories of dependent variables reflecting ecosystem features, including vegetation dynamics, productivity, water conditions, and landscape structure. Their definitions, data sources, and ecological implications are detailed below (

Table 1. The variables of NDVI, NPP and NDWI to show ecosystem quality.

Dependent Variables	Definition	Calculation and Data Source
NDVI	The NDVI is a widely used remote sensing index to quantify vegetation health status and coverage. It is calculated as the normalized difference between near-infrared and red reflectance, with values ranging from −1 to 1 (higher values indicating denser, healthier vegetation).	Resource and Environment Science Data Center, Chinese Academy of Sciences (https://www.resdc.cn/)
NPP	NPP represents the net amount of carbon fixed by vegetation through photosynthesis minus autotrophic respiration, reflecting vegetation growth status, ecosystem resilience, and environmental carrying capacity.	Land Processes Distributed Active Archive Center (LPDAAC), NASA/USGS (https://lpdaac.usgs.gov/)
NDWI	NDWI is an indicator of surface water extent, derived from the normalized difference between green and near-infrared bands to enhance water body detection.	NDWI = (Band3 − Band5)/(Band3 + Band5) [Landsat 8 from NASA/USGS (https://lpdaac.usgs.gov/)]

and

Table 2. The variables of Landscape Pattern.

Dependent Variables	Definition	Calculation	Ecological Implication
NP	The total count of discrete patches in a landscape.	Simple count of all patches (regardless of type or size).	NP positively correlates with landscape fragmentation, and higher NP indicates greater fragmentation (more subdivided habitats), while lower NP suggests more contiguous landscapes.
LPI	The proportion of the largest patch area relative to the total landscape area, reflecting the dominance of a single patch.	LPI = Total landscape area/Area of the largest patch × 100%	High LPI indicates the presence of large, dominant patches with good connectivity; low values suggest fragmented landscapes dominated by small patches. It emphasizes the influence of keystone patches on overall landscape structure.
AI	A measure of patch clustering, quantifying the degree to which patches of the same type are aggregated.	$AI=\left[1-{\displaystyle \frac{{\sum }_{i=1}^m{p}_i}{{\sum }_{i=1}^m{p}_i^{max}}}\right]\times 100\%$ Where $p_i$ be the perimeter of the i-th patch, and $p_i^{max}$ be the maximum possible perimeter of the i-th patch.	AI ranges from 0 (completely dispersed) to 100 (perfectly aggregated). Higher values indicate stronger patch clustering, which enhances local habitat continuity but may reduce landscape diversity.
LSI	A measure of patch shape complexity, comparing actual patch perimeters to the minimum perimeter of a compact (circular) shape of equivalent area.	$LSI={\displaystyle \frac{0.25\times {\sum }_{i=1}^m{\sum }_{j=1}^{n_i}\left(p_{ij}/\sqrt{a_{ij}}\right)}{\sqrt{A}}}$ Where $p_i$ be the perimeter of the j-th polygon in patch i, $a_{ij}$ be its area, and A be the total landscape area.	LSI is dimensionless. Higher values indicate more complex shapes (e.g., elongated, irregular patches) with increased edge effects (e.g., higher exposure to disturbances); lower values suggest simpler, more compact shapes.
COHESION	A measure of patch connectivity, considering both adjacency and shared boundaries.	$COHESION=\left[1-{\displaystyle \frac{{\sum }_{i=1}^m{\sum }_{j=1}^{n_i}{b}_{ij}/B}{{\sum }_{i=1}^m{a}_i-1}}\right]\times 100\%$ Where $b_{ij}$ be the length of the boundary adjacent to other patches for the j-th polygon in patch i, B be the total landscape boundary length, and $a_i$ be the area of patch i.	COHESION ranges from 0 (no connectivity) to 100 (fully connected). Unlike AI (which focuses on clustering), COHESION emphasizes continuous connections between patches, with higher values indicating better functional connectivity (e.g., for species movement).

).

The NDVI, NPP, and NDWI are core quantitative indicators in ecology and remote sensing, jointly capturing ecosystem quality across three dimensions: vegetation coverage and health (NDVI), ecosystem productivity (NPP), and water availability (NDWI). Their strong complementarity enables a comprehensive characterization of ecosystem structural integrity, functional stability, and environmental support capacity when applied synergistically [22,23,24].

Landscape pattern analysis was applied to quantify the spatial structure changes affected by the CEPZ policy. Following the classical landscape ecology framework [25,26,27], we constructed a Landscape Pattern Composite Index (LPCI) to reflect the overall fragmentation, connectivity, and stability of the landscape integrating five dimensions: patch quantity, size distribution, aggregation, shape complexity, and connectivity. Five metrics were selected to form the LPCI: the Number of Patches (NP), Largest Patch Index (LPI), Aggregation Index (AI), Landscape Shape Index (LSI), and Patch Cohesion Index (COHESION). Different from the single landscape index used in previous studies (e.g., only using fragmentation index or connectivity index), the LPCI integrates multiple dimensions such as landscape fragmentation, connectivity, and stability, which can more comprehensively reflect the spatial structure changes in the study area affected by the CEPZ policy.

The original data of the five-landscape metrics (NP, LPI, AI, LSI, and COHESION) were integrated via the entropy weight method to calculate the Landscape Pattern Composite Index (LPCI). This index synthesizes the comprehensive spatial structure and functional status of landscape patterns.

$$LPCI=\sum _{i=1}^n\left(w_{ij}{p}_{ij}\right)$$

(1)

Firstly, based on the original data, dimensionless processing is performed to obtain negative indicators, where n is the total number of indicators; $w_{ij}$ is the indicator weight, obtained by calculating the indicator’s entropy value; and $p_{ij}$ denotes the score of the i-th indicator in the j-th year. High LPCI values indicate strong inter-patch connectivity, prominent dominant patch types, robust ecosystem disturbance resistance, and a dominance of natural or semi-natural landscapes. Low LPCI values typically correspond to high patch counts, complex patch shapes, severe landscape fragmentation, and reduced habitat integrity. Such patterns often reflect patch fragmentation driven by anthropogenic activities (e.g., road construction, urbanization).

Our analysis used land-use data from 2006 to 2023, covering the period since these national-level cultural ecological protection areas were established. Data processing and analysis were conducted with ArcGIS Pro 3.3—for spatial data management—and Fragstats 4.2—for landscape pattern metric calculation and index synthesis.

2.2.2. Explanatory Variables

The core explanatory variable measures the impact of establishing national-level CEPZs on local ecosystems. This is operationalized by focusing on two dimensions: (1) whether a county/district was designated as a protected area, and (2) whether the observation year postdates the approval year. These dimensions are captured by two dummy variables including the following: (a) Treat: Equals 1 if the county/district is a national-level CEPZ, and 0 otherwise. (b) Post: Equals 1 if the year is after the approval year of the CEPZ, and 0 otherwise. The interaction term $\mathrm{did}=Treat\times Post$ serves as the dummy variable representing the policy shock.

2.2.3. Mediating Variables

To measure regional construction land expansion, we use the built-up land ratio (shown as Built), calculated by extracting county-level construction land area from annual impervious surface products, dividing it by the total county area, and denoting the result as Built_it. The impermeable surface data comes from the publicly available dataset published by Gong et al. [28]. The higher the index, the greater the proportion of construction land.

To further identify the internal spatial structure of ecological land, Morphological Spatial Pattern Analysis (MSPA) was used, which can accurately distinguish core, bridge, edge, and islet areas based on mathematical morphology [29,30]. Compared with the traditional landscape classification method, MSPA has obvious advantages in revealing the internal spatial structure of ecological land, which is also consistent with the application practice of domestic and foreign scholars in ecological protection research [31,32]. Specifically, we extract the area of core-type areas (the most interior, ecologically intact patches) and calculate their proportion to derive the index Core_it. In the MSPA process based on Guidos Toolbox 3.0 software, ecologically functional land types (including forests, grasslands, water bodies, and wetlands drawing on the classification methods of Zhu [18,30]) are selected as foreground elements and assigned a value of 2. Other land use types are designated as background elements with a value of 1. These values are converted into 30 m × 30 m binary raster maps for identification and analysis. Using the Guidos Toolbox software and applying the eight-neighborhood analysis principle (with the edge width set to 1), we identify seven landscape structure types, then select “core” type patches larger than 20 hectares as primary ecological habitats, extract their areas, and calculate their proportion within the whole foreground region. The higher the data, the better it reflects the protection of the core ecological habitat.

2.2.4. Control Variables

To mitigate confounding effects and enhance the causal effect identification power of the policy analysis, external environmental variables were controlled. Considering the data availability at the county level, we incorporated socioeconomic and natural factors as controls. All non-negative variables were log-transformed to reduce scale differences. The specific variables include the following: (a) Economic development level (ln GDP): Measured by the gross domestic product (GDP) of the county/district in the current year (unit: 10,000 yuan). (b) Industrial structure (ln structure): Ratio of the added value of the tertiary industry to that of the secondary industry in the county/district (yearly). (c) Population size (ln population): Number of registered residents in the county/district (unit is 10,000 persons). (d) Temperature condition (temp): Annual mean temperature of the county/district (unit is °C). (e) Air quality (ln PM2.5): Proxy variable measured by the annual mean PM2.5 concentration within the administrative boundary of the county/district (μg/m³, implied unit). (f) Precipitation (ln precip): Annual mean precipitation of the county/district (unit is mm).

All the socioeconomic variables were obtained from the China County Statistical Yearbook, while natural climate variables were sourced from the National Tibetan Plateau Data Center (https://data.tpdc.ac.cn/home Date: 23 September 2024). Descriptive statistics for all the variables are presented in

Table 3. Data descriptive statistics.

Variable	Sample Size	Mean	Standard Deviation	Minimum	Median	Maximum
NDVI	8172	0.548	0.151	0.0550	0.603	0.766
NDWI	8172	−0.433	0.0720	−0.605	−0.438	−0.140
NPP	8172	0.719	0.371	0.111	0.707	3.158
LPCI	8172	0.647	0.184	0.129	0.651	0.989
did	8172	0.288	0.453	0.000	0.000	1.000
temp	8172	13.27	6.366	−6.178	14.93	22.82
Built	8172	0.004	0.001	0.001	0.004	0.007
Core	8172	0.406	0.113	0.322	0.412	0.519
ln precip	8172	6.830	0.478	3.712	6.888	7.814
ln PM2.5	8172	3.599	0.388	2.446	3.637	4.681
ln population	8172	3.386	0.828	0.818	3.517	5.176
ln GDP	8172	13.42	1.331	8.924	13.50	17.33
ln structure	8172	0.760	0.404	0.0420	0.681	4.025

.

2.3. Specification of the Multi-Period Difference-in-Differences (Multi-Period DID) Model

Traditional difference-in-differences (DID) methods typically involve only two periods (pre- and post-policy implementation). However, policies are frequently implemented in multiple phases. The multi-period DID method allows us to analyze the impacts of such staggered policy interventions. In this framework, time dummy variables are no longer binary but are coded based on the multiple implementation periods of the policy.

To investigate the impact of cultural ecological protection area construction on host counties, we treat the establishment of CEPZs as an exogenous shock and apply the DDID method framework for empirical testing. Because these areas were established in different years (i.e., the policy was implemented in a phased shock pattern), we construct a multi-period DID model for analysis. The calculation method is specified as follows:

$$\begin{array}{c}{Y}_{it}={\beta }_0+{\beta }_1{did}_{it}+{\alpha }_1{X}_{it}+{\mu }_i+{\lambda }_t{+\epsilon }_{\begin{array}{c}\begin{array}{c}it\end{array}\end{array}}\end{array}$$

(2)

where the subscripts i and t denote the county i and year t, respectively. $Y_{\mathrm{it}}$ is the dependent variable, representing the relevant indicator of county i in year t. ${\beta }_0$ is the constant term, and ${\beta }_1$ is the estimated coefficient of the core explanatory variable (the treatment effect). ${did}_{it}$ is the core explanatory variable (the interaction term of the treatment status and post-treatment period, defined as ${\mathrm{did}}_{it}={Treat}_i\times {Post}_{it}$). ${\alpha }_1$ is the vector of the estimated coefficients for control variables. $X_{it}$ is a vector of the control variables (socioeconomic and natural factors, log-transformed where applicable). ${Post}_{it}$ is a time dummy variable: it equals 1 if t year is on or after the policy implementation year $t_0$ for county i, and 0 otherwise. ${\mu }_{\mathrm{i}}$ represents county-specific fixed effects (controlling for time-invariant unobserved heterogeneity across counties). ${\lambda }_{\mathrm{t}}$ represents year fixed effects (controlling for time-varying common shocks). ${\epsilon }_{\begin{array}{c}\begin{array}{c}it\end{array}\end{array}}$ is the stochastic disturbance term (assumed to be independently and identically distributed).

To analyze the effect of the mediating variables, this study constructed a mediating effect test model based on the above equation, adopting the multi-period difference-in-differences (Multi-Period DID) approach. The specific formula is as follows:

$$M_{it}={\gamma }_0+{\gamma }_1{did}_{it}+{\alpha }_2{X}_{it}+{\mu }_i+{\lambda }_t+{\epsilon }_{\begin{array}{c}\begin{array}{c}it\end{array}\end{array}}$$

(3)

$$Y_{it}={\beta }_3+{\beta }_4{did}_{it}+{\beta }_5{M}_{it}+{\alpha }_3{X}_{it}+{\mu }_i+{\lambda }_t+{\epsilon }_{\begin{array}{c}\begin{array}{c}it\end{array}\end{array}}$$

(4)

In the equation, $M_{it}$ denotes the mediating variable, which comprises two components: Built_it (the proportion of built-up land) and Core_it (the proportion of core ecological habitats). The meanings of the other symbols are identical to those in the preceding equation.

To improve the robustness and accuracy of the tests, this study conducted conventional parallel trend tests and placebo tests. The core identification assumption of DID is the parallel trends assumption: the pre-policy trends of the dependent variable should not differ significantly between the treatment and control groups. We used the event study method to visually examine dynamic policy effects and test this assumption, constructing the following model:

$$Y_{it}=\alpha +\sum _{k=m,m\ne -1}^M{\beta }_k{D}_{it}^k+\gamma X_{it}+{\mu }_i+{\lambda }_t+{\epsilon }_{it}$$

(5)

In the model, k denotes the k-th year relative to the policy implementation year (where k > 0 indicates years after implementation, and k < 0 indicates years before implementation). $D_{it}^k$ represents a series of dummy variables: it equals 1 if county was designated as a CEPZ, and year t is the k-th year relative to the policy implementation year, and 0 otherwise. All the other variables are consistent with Equation (1) (the multi-period DID model specification). To avoid multicollinearity, the year immediately preceding policy implementation (k = 1) is set as the baseline period. ${\beta }_k$ is the core estimated coefficient, representing the average difference in the dependent variable between the treatment group and control group in the k-th year relative to policy implementation. If ${\beta }_k$ is significantly different from 0 for k < 0 (i.e., in the pre-policy period), the parallel trends assumption is violated; conversely, if ${\beta }_k$ is insignificant for all k < 0, the assumption holds.

A key concern when using the multi-period DID model is that findings might reflect a random phenomenon rather than true policy effects. To address this, the study conducted a placebo test by randomly selecting 213 counties (matching the size of the real treatment group) from the sample via computer as pseudo-treatment groups, and randomly assigning the establishment years of the protected areas. This process was repeated 500 times to generate placebo effect estimates.

Then, this study conducted three additional robustness checks. First, sample trimming was performed. Given the large time span of approvals across different batches of experimental areas, early (t = 2007) and recent (t = 2020) samples may exhibit unique characteristics. Thus, samples from the earliest and latest batches of designated experimental areas were excluded, and the regression was re-run. Second, county-level time trend control was introduced: while the baseline regression controlled for county-specific fixed effects and time effects, counties vary in their socio-cultural policy environments, resource endowments, and locational attributes. Thus, differences that may arise among counties with time-varying characteristics could affect the accuracy of the baseline results. To address this, county–year trend terms were added to the baseline model to control for inherent characteristic year trends across county groups that influence the dependent variable. Third, outlier mitigation was applied that all continuous variables were winsorized at the 1% level in both tails to mitigate the influence of extreme values.

Additionally, considering that the effects of the policy may exhibit heterogeneity, we conducted two separate heterogeneity analyses. First, we analyzed Cultural Ecological Protection Experimental Zones (CEPZs) with independent management agencies (IMAs) versus those without. Second, we examined the heterogeneous policy effects across the eastern, central, and western regions of China following the National Bureau of Statistics’ classification and precedents in related studies [33]. The eastern region comprised 101 counties, the central region 147 counties, and the western region 206 counties.

3. Results

3.1. Spatio-Temporal Evolution of Ecosystem Functions and Landscape Patterns

3.1.1. Ecosystem Function

The results for the NDVI exhibited a general pattern of higher values in southern regions and lower values in northern regions (Figure 3). Notably, three Wuling Mountain CEPZs and three Hakka CEPZs showed relatively high NDVI values. After policy implementation, the mean NDVI within CEPZs demonstrated a significant increase, with an overall average growth of 5.98% (Figure 4a). Among these, three areas including Shaanbei CEPZ, Wuling Mountain (Xiangxi) CEPZ, and Wuling Mountain (Southeast Chongqing) CEPZ showed the largest pre–post policy increases, reaching 15.77%, 8.84%, and 8.24%, respectively. The NDVI values in the experimental group (CEPZs) were significantly higher than those in the control group (non-CEPZs). Both groups exhibited a steady upward trend in NDVI during the 2006 to 2023 study period.

The NPP was higher in southeastern regions and lower in northwestern regions (Figure 5). Areas with elevated NPP included the Minnan CEPZ, Marine Fishery (Xiangshan) CEPZ, and Hakka (Meizhou) CEPZ. After policy implementation, the mean NPP within CEPZs increased significantly, except for Diqing CEPZ (Figure 4b). The overall average growth was 5.44%, with Shaanbei, Jinzhong, and Baofeng CEPZs exhibiting the largest increases (29.37%, 14.13%, and 11.27%, respectively). Initially, the NPP in the experimental group was slightly lower than in the control group, but both groups showed a slow upward trend over the study period. After 2012, NPP values in the experimental and control groups became comparable.

The NDWI showed minimal regional variation overall, with higher values observed in the Tibetan (Yushu) CEPZ, Qiandongnan CEPZ, and Wuling Mountain (Southeast Chongqing) CEPZ (Figure 6). After policy implementation, the mean NDWI within CEPZs generally decreased, except for the Tibetan CEPZ (Yushu) (Figure 4c). The overall average decline was 5.98%, with the Shaanbei, Jinzhong, and Baofeng CEPZs showing the largest reductions (12.66%, 12.59%, and 8.41%, respectively). From the comparison perspective, no significant difference in NDWI was observed between the experimental and control groups. Both groups showed a gradual downward trend from 2006 to 2023, except in 2012 and 2020.

3.1.2. Landscape Pattern Evolution

Among all the CEPZs, the land use changes in the Minnan CEPZ, Qilu CEPZ, Heluo CEPZ, and Huizhou CEPZ are relatively significant. Based on land use data, landscape pattern metrics were calculated for protected and non-protected areas across years. The value of the NP and LPI showed decreasing trends, while the LSI first increased then decreased. The AI and COHESION remained relatively stable. Both protected and non-protected areas exhibited a “first rise, then fall” pattern in the composite landscape pattern index, indicating an initial optimization followed by the degradation of overall landscape structure. The Gesar (Golog) CEPZ, Huizhou CEPZ, and Hakka Culture (Western Fujian) CEPZ had higher composite indices, reflecting superior landscape patterns.

After policy implementation, most areas showed a decline in the composite index (Figure 4d). Only three areas including the Wuling Mountain (Southeast Chongqing) Area CEPZ, Wuling Mountain (Xiangxi) Area CEPZ, and Shaanbei CEPZ exhibited increases (6.42%, 5.28%, and 3.55%, respectively). This suggests that cultural ecological protection policies did not consistently promote landscape pattern optimization.

3.2. Multi-Period DID Analysis

3.2.1. Baseline Regression

We employed the multi-period DID model to investigate the effectiveness of establishing a national-level CEPZ. The regression results are presented in

Table 4. The benchmark regression results.

Variables	(1) NDVI	(2) NDWI	(3) NPP	(4) LPCI
did	0.002 ***	0.000	0.002	−0.004 ***
	(3.340)	(0.487)	(1.213)	(−2.645)
temp	0.010 ***	−0.012 ***	−0.003 **	−0.008 ***
	(11.889)	(−11.330)	(−1.965)	(−6.004)
ln population	−0.029 ***	0.009 *	−0.028 ***	−0.017
	(−6.501)	(1.879)	(−4.033)	(−1.475)
ln GDP	0.004 ***	0.007 ***	−0.011 ***	0.019 ***
	(3.522)	(5.367)	(−5.173)	(7.269)
ln PM2.5	−0.010 ***	−0.034 ***	0.021 ***	0.010 **
	(−3.929)	(−11.080)	(4.441)	(2.008)
ln structure	0.002 **	0.001	−0.003	0.007 ***
	(2.005)	(0.399)	(−1.411)	(3.167)
ln precip	0.027 ***	0.033 ***	−0.006 *	−0.004
	(16.476)	(16.541)	(−1.933)	(−1.218)
_cons	0.312 ***	−0.508 ***	0.980 ***	0.538 ***
	(10.152)	(−14.244)	(18.339)	(7.874)
N	8172	8172	8172	8172
R2	0.990	0.924	0.994	0.973
Individual Fixed Effects	YES	YES	YES	YES
Time Fixed Effect	YES	YES	YES	YES

Note: *, **, ***, respectively, indicate significance at the 10%, 5%, and 1% levels, with t-values in parentheses. The same applies below.

. Columns (1)–(4) of Table 4 report results with the NDVI, NDWI, NPP, and LPCI as dependent variables, respectively. The coefficient of the core explanatory variable did was significantly positive in Column (1), significantly negative in Column (4), and insignificant in Columns (2) and (3).

3.2.2. Robustness Tests

To validate the reliability of the baseline results, we conducted three robustness checks: a parallel trend test, a placebo test, and additional sensitivity analyses.

(1)

Parallel Trend Test

Figure 7 presents the results of the parallel trend test, which is used to verify whether the parallel trend assumption of the multi-period DID model holds. The dashed vertical lines extending from each point estimate in the figure represent the 95% confidence intervals. For all years prior to the implementation of the CEPZ policy (i.e., relative time k < 0), the 95% confidence intervals of the regression coefficients for both NDVI and LPCI indicators cross the horizontal zero line [34,35]. From a statistical perspective, this indicates that the estimated coefficients of the pre-treatment periods are not significantly different from zero, which confirms that there was no significant structural difference between the experimental group (CEPZ areas) and the control group (non-CEPZ areas) before the policy shock. Therefore, the parallel trend assumption of the model is valid, laying a solid foundation for the subsequent empirical analysis.

(2)

Placebo Test

The placebo effect estimate results show kernel density and corresponding p-values are plotted in Figure 8. It can be observed that, for both the NDVI and the LPCI, the placebo estimates were predominantly concentrated around 0, significantly differing from the real estimates under the policy shock (from baseline regression). Moreover, most placebo estimates were statistically insignificant. These results further indicate that the observed shock effect is not driven by unobservable factors, confirming the robustness of the research conclusions.

(3)

Additional Robustness Checks

Building on the aforementioned robustness tests, three supplementary checks were conducted to further validate the baseline regression results.

The regression results (

Table 5. Other robustness test results.

Variables	Eliminate Some Samples		Consider the Time Trend		Eliminate the Influence of Outliers
	(1)	(2)	(3)	(4)	(5)	(6)
	NDVI	LPCI	NDVI	LPCI	NDVI	LPCI
did	0.002 ***	−0.006 ***	0.002 ***	−0.004 ***	0.002 ***	−0.004 ***
	(3.154)	(−4.062)	(3.253)	(−2.616)	(3.257)	(−2.680)
temp	0.011 ***	−0.008 ***	0.010 ***	−0.008 ***	0.010 ***	−0.009 ***
	(12.347)	(−5.502)	(11.648)	(−5.890)	(11.457)	(−6.477)
ln population	−0.028 ***	−0.025 *	−0.031 ***	−0.016	−0.026 ***	−0.013
	(−5.702)	(−1.846)	(−6.902)	(−1.389)	(−5.836)	(−1.127)
ln GDP	0.004 ***	0.020 ***	0.004 ***	0.019 ***	0.007 ***	0.019 ***
	(3.275)	(7.514)	(3.665)	(7.223)	(5.916)	(7.678)
ln PM2.5	−0.010 ***	0.005	−0.010 ***	0.010 **	−0.009 ***	0.009 *
	(−4.147)	(1.038)	(−4.179)	(2.093)	(−3.686)	(1.815)
ln structure	0.002 *	0.006 ***	0.002 **	0.007 ***	0.004 ***	0.009 ***
	(1.675)	(2.620)	(2.374)	(3.062)	(3.868)	(3.531)
ln precip	0.024 ***	−0.004	0.027 ***	−0.004	0.027 ***	−0.005
	(14.281)	(−1.376)	(16.407)	(−1.175)	(16.009)	(−1.637)
Time Trend Item			−0.000 ***	0.000 *
			(−6.933)	(1.818)
_cons	0.332 ***	0.568 ***	1.165 ***	0.019	0.267 ***	0.548 ***
	(10.354)	(7.446)	(9.130)	(0.068)	(8.765)	(8.254)
N	7555	7555	8172	8172	8172	8172
R2	0.990	0.973	0.990	0.973	0.990	0.973
Individual Fixed Effects	YES	YES	YES	YES	YES	YES
Time Fixed Effect	YES	YES	YES	YES	YES	YES

Note: *, **, ***, respectively, indicate significance at the 10%, 5%, and 1% levels, with t-values in parentheses. The same applies below.

) show that the coefficient of did remains statistically significant with a consistent direction across all tests, confirming the robustness and reliability of the baseline regression results.

3.3. Mechanism of the Effects

Table 6. Mediating effect test result.

Variables	(1) Core	(2) Core-NDVI	(3) Core-LPCI	(4) Built	(5) Built-NDVI	(6) Built-LPCI
did	0.009 ***	0.001 **	0.003	0.012 ***	−0.001 **	−0.009 ***
	(2.891)	(2.037)	(1.678)	(3.126)	(−2.153)	(−3.233)
Core		0.004 ***	0.006 **
		(2.673)	(1.998)
Built					0.005 ***	0.008 ***
					(2.554)	(3.237)
Control Variable	YES	YES	YES	YES	YES	YES
Sobel test		0.000 (7.298)			0.000 (8.486)	0.000 (6.633)
Proportion of mediating effect		0.329			0.277	0.315
Individual Fixed Effects	YES	YES	YES	YES	YES	YES
Time Fixed Effect	YES	YES	YES	YES	YES	YES
N	8172	8172	8172	8172	8172	8172
R2	0.979	0.954	0.963	0.982	0.943	0.956

Note: **, ***, respectively, indicate significance at the 5%, and 1% levels, with t-values in parentheses. The same applies below. The results shown in the Sobel test are p values, with Z values in parentheses.

presents the results of the mediating effect analysis. Columns (1) and (4) report the impacts of the CEPZ construction on the proportion of core ecological habitats (Core) and the proportion of built-up land (Built), respectively. Both coefficients are significantly positive (0.009 and 0.012), indicating that the policy has promoted conservation efforts in core protected areas while simultaneously driving the expansion of built-up land through industrial development and facility introduction.

Further exploration of the mediating mechanisms reveals the following: Columns (2) and (3) present the mediating effects of increased Core on the NDVI and the LPCI. Here, the coefficient for the NDVI is significantly positive (coefficient = 0.001, p < 0.05), and the p value of the Sobel test result is less than 0.01, indicating a significant mediating effect, with an effect size accounting for 32.9%, whereas the effect on the LPCI is not statistically discernible (p > 0.1). This suggests that the establishment of CEPZs has enhanced vegetation coverage to some extent by protecting core ecological habitats, but has not substantially altered the overall landscape pattern. Thus, Hypothesis H3 is partially validated.

Analysis of Columns (5) and (6) shows that, through the expansion of built-up land, the CEPZ policy exerts a significantly negative impact on both the NDVI (coefficient = −0.001, p < 0.05) and the LPCI (coefficient = −0.009, p < 0.01), and it has a greater impact on the LPCI. The Sobel test results for both columns indicate a highly significant mediation effect (p < 0.01), with effect sizes accounting for 27.7% and 31.5%, respectively. This also partially validates Hypothesis H4.

3.4. Heterogeneity Analysis

3.4.1. Local Government Management

In accordance with the management measures for CEPZs, among the 213 treatment group counties, 124 (58.2%) established independent management agencies (IMAs) responsible for integrated conservation, planning, and community coordination. These agencies, typically staffed with dedicated personnel and allocated specific funding, directly reflect local governments’ resource commitment and implementation resolve for cultural ecological protection. The remaining 89 counties (41.8%) were co-managed by cultural and tourism bureaus or intangible cultural heritage centers, lacking independent staffing and dedicated funding, which may limit policy execution capacity.

To quantify the impact of this institutional variation, a subgroup multi-period DID model was estimated. The model retained all control variables from the baseline regression and controlled for county-level individual fixed effects and year fixed effects. The regression results for the subgroups are presented in

Table 7. Local government management heterogeneity results.

Variables	(1) Have IMA-NDVI	(2) No IMAs-NDVI	(3) Have IMA-LPCI	(4) No IMA-LPCI
did	0.004 ***	0.001	−0.002 **	−0.005 ***
	(2.821)	(1.673)	(−2.192)	(−3.945)
Control Variable	YES	YES	YES	YES
Individual Fixed Effects	YES	YES	YES	YES
Time Fixed Effect	YES	YES	YES	YES
N	4914	3258	4914	3258
R2	0.952	0.938	0.947	0.943

Note: **, ***, respectively, indicate significance at the 5%, and 1% levels, with t-values in parentheses. The same applies below.

.

For the NDVI, the policy’s positive effect was significantly amplified in counties with independent agencies (coefficient = 0.004, p < 0.01), doubling the full-sample average effect (0.002,

Table 8. Regional heterogeneity results.

Variables	(1) East-NDVI	(2) Central-NDVI	(3) West-NDVI	(10) East-NPP	(11) Central-NPP	(12) West-NPP	(4) East-LPCI	(5) Central-LPCI	(6) West-LPCI
did	0.001	0.003 **	0.005 ***	0.001	0.003 *	0.002 **	−0.002	−0.003 **	−0.006 ***
	(1.121)	(2.152)	(3.893)	(1.468)	(1.876)	(2.131)	(−1.050)	(−2.340)	(−3.560)
Control Variable	YES	YES	YES	YES	YES	YES	YES	YES	YES
Individual Fixed Effects	YES	YES	YES	YES	YES	YES	YES	YES	YES
Time Fixed Effect	YES	YES	YES	YES	YES	YES	YES	YES	YES
N	1818	2464	3708	1818	2464	3708	1818	2464	3708
R2	0.965	0.907	0.902	0.941	0.953	0.935	0.915	0.875	0.935

Note: *, **, ***, respectively, indicate significance at the 10%, 5%, and 1% levels, with t-values in parentheses.

). In contrast, non-independent agency counties showed an insignificant effect (coefficient = 0.001, p > 0.1), underscoring institutional capacity as a critical moderator of vegetation recovery. Regarding the LPCI, independent agency counties experienced a minimal 0.2% decline (coefficient = −0.002, p < 0.05), far less than the full-sample average decrease (−0.004). For the NDWI and NPP, neither subgroup showed significant effects, aligning with the full-sample conclusion that short-term cultural policies struggle to alter slow-changing hydrological and productivity dynamics.

3.4.2. Regional Heterogeneity

Considering the imbalances in regional development across China, particularly significant disparities in ecological baseline conditions and socioeconomic contexts, the multi-period DID model was applied to analyze policy effects across subgroups, with the results reported in Table 8.

The policy effect on the NDVI was strongest in the west (did = 0.005, p < 0.01), representing a 150% increase from the full-sample coefficient (0.002). Western countries are mostly ecologically fragile regions, which benefited from policy–ecology alignment. The central region showed a moderate effect (did = 0.003, 0.01 < p < 0.05), where agrarian cultural heritage integrated with farmland shelterbelt construction, driving vegetation recovery via agricultural space protection and brand enhancement. The eastern region exhibited no significant effect (did = 0.001, p > 0.1), likely due to high economic density.

Secondly, the west showed a significant promoting effect of CEPZ policy on NPP (did = 0.002, 0.01 < p < 0.05), attributed to higher precipitation stability (interannual coefficient of variation < 15%). The central region had a marginally significant effect (did = 0.003, 0.05 < p * < 0.1); however, the east showed no significant impact (did = 0.001, p > 0.1).

No significant regional heterogeneity was detected for the NDWI across subgroups (p > 0.1), consistent with the full-sample conclusion that short-term cultural policies minimally affect hydrological conditions.

The CEPZ policy’s effects on landscape structure varied spatially. The largest decline was observed in the west (did = −0.006 ***, p < 0.01), though the baseline fragmentation was low. In the central area, a moderate (did = −0.003 **, p < 0.05) and the smallest decline (did = −0.002, p > 0.1) were observed.

Regional heterogeneity analysis reveals a distinct gradient pattern in the policy effects of cultural ecological protection areas: west > central > east.

4. Discussion and Conclusions

4.1. Discussion

Our findings provide empirical evidence that the establishment of CEPZs generates a complex, bifurcated impact on the environment. Among the hypotheses proposed in this study, H1 was partially supported, H2 yielded an opposite result, and both H3 and H4 were partially supported, as shown in the table below (

Table 9. Hypotheses verified results.

Hypothesis	Results	Instructions
H1: CEPZ designation has a positive effect on ecosystem quality.	Partially Supported	Has a positive effect only on NDVI
H2: CEPZ designation has a positive effect on landscape pattern optimization.	Unsupported	Has a negative effect on landscape pattern optimization
H3: CEPZs enhance the quality of protected areas, transmitting these effects to ecosystem quality and landscape patterns.	Partially Supported	Has enhanced the quality of protected areas and transmitted to NDVI, but has not affected the landscape pattern
H4: CEPZs increase the area of construction land, which in turn influences ecosystem quality and landscape patterns.	Partially Supported	Has enhanced the area of built-up land, and transmitted negative effect on NDVI and landscape patterns.

).

Firstly, on the one hand, the policy significantly enhanced local surface vegetation coverage (evidenced by the positive NDVI effect). This aligns with the socioecological system theory, suggesting that the preservation of traditional cultures, which are often rooted in harmonious man–land relationships, can yield ecological dividends. However, a critical trade-off showed that the overall landscape structure experienced degradation and increased fragmentation, as indicated by the significant negative impact on the LPCI. This suggests that CEPZs differ from traditional nature reserves. They are “living” protection areas where cultural revitalization often triggers infrastructure expansion [36]. This finding challenges the idealistic view of cultural protection as a purely “green” endeavor and highlights a hidden cost of culture-led regional development [37], which is slightly different from the research of Chen et al. [38]. Chen et al. focused more on the cultural protection effect of CEPZs on land use and emphasized the positive synergy between cultural protection and cultural key species, but did not fully explore the negative impact of cultural revitalization on landscape structure. Internationally, similar trade-offs have been observed in cultural heritage protection policies. For example, UNESCO’s cultural heritage protection projects in Europe have also found that the development of cultural tourism accompanying cultural protection can lead to landscape fragmentation, which is consistent with the dual impact found in this study [14,39]. This cross-regional comparison further confirms that the “protection-development trade-off” is a common challenge in cultural ecological protection practice.

Secondly, the mediating effect analysis reveals the underlying logic of this dual impact. The establishment of CEPZs promoted the conservation of core ecological habitats, which directly contributed to the improvement in NDVI. Conversely, the policy simultaneously drove the expansion of built-up land through cultural-tourism industrial development and the introduction of facilities. This expansion significantly negatively impacted both vegetation and landscape connectivity, with the negative effect on LPCI being particularly pronounced. This paradox suggests that the path to the protection and dissemination of culture often inevitably introduces physical disturbances, which may actually cause disturbances to the historical ecosystems in which it exists [40]. Actually, the CEPZ policy is a governance model centered on culture-driven comprehensive regional management, characterized by its emphasis on zoned control (distinct management strategies for different areas). For ecological spaces, the policy prioritizes the holistic protection of ecosystems to foster a harmonious “nature-culture symbiosis” model, ensuring that natural habitats, biodiversity, and ecological processes are preserved as the foundation for sustainable development. In areas with intense human activity, the policy focuses on the sustainable utilization of cultural resources/heritage, including the growth of cultural and tourism industries and the development of supporting infrastructure [18]. This approach aims to balance economic vitality with cultural preservation, enabling regions to achieve integrated management that benefits both local communities and the broader ecosystem.

Thirdly, a significant theoretical contribution of this study is identifying the role of institutional capacity in ecological outcomes. Our results demonstrate that counties with independent management agencies achieved nearly double the vegetation recovery effect compared to the full sample, while experiencing significantly less landscape degradation. This underscores that ecological benefits are not an automatic byproduct of cultural policy but are contingent upon dedicated administrative resources and implementation resolve. This is consistent with Wang et al.’s interpretation of China’s policies, where the implementation of regional policies will be more effective when there is a more stable management structure [41].

4.2. Theoretical and Practical Implications

The study provides a new understanding of how culturally led policies intersect with natural ecosystems. The findings demonstrate that cultural protection is not an isolated social endeavor but can be a driver of ecological change. This validates the theory that protecting traditional cultural spaces can yield ecological dividends.

Theoretically, this research identifies a tension between ecosystem quality and landscape structure. While policy implementation enhances vegetation greenness, it concurrently promotes increased landscape fragmentation. These findings indicate that living conservation approaches—unlike strictly protected nature reserves—introduce distinct stressors through human activity and infrastructure development.

Furthermore, the study positions governance capacity as a critical theoretical moderator. The superior ecological outcomes observed under independent agencies, compared to co-managed arrangements, suggest that the effectiveness of environmental policy is strongly constrained by its administrative design. The West > Central > East gradient in policy efficacy implies that cultural and locally adapted policies exert the greatest environmental influence in regions where ecosystems are more fragile and remain closely linked to traditional land-use practices.

For policymakers and regional planners, these results offer several actionable insights. Firstly, local governments should prioritize the establishment of independent management agencies with dedicated personnel and funding. As shown in the data, these agencies are nearly twice as effective at promoting vegetation recovery while mitigating landscape degradation. Secondly, planners must be cautious of the built-up land expansion associated with CEPZs. To prevent landscape fragmentation, low-impact development standards should be applied to tourism facilities and cultural heritage sites. Thirdly, the implementation of policies needs to be tailored to local conditions, taking into account that the impact of the western region is better. In the future, it is necessary to strengthen the layout and management of CEPZ in the western region. Furthermore, because hydrological (NDWI) and productivity (NPP) metrics showed less immediate policy responses, long-term monitoring is required to understand when cultural protection begins to influence slow-changing ecological processes.

4.3. Limitations and Future Directions

Despite the robust DID framework, certain limitations provide avenues for future inquiry. Firstly, the study found minimal impact on the NDWI and NPP. This may be due to the inherent time lag required for hydrological cycles and biomass productivity to reflect surface-level policy changes. While county-level data is standard for DID, it may mask micro-level variations within a single CEPZ, particularly in large western counties. Furthermore, the baseline results might be influenced by overlapping ecological programs, although the DID model attempts to control for these through fixed effects and robustness checks

Future research could use high-resolution remote sensing to map the exact locations of cultural heritage sites against ecological hotspots to identify micro-level conflicts. Investigating whether different types of culture produce different ecological outcomes would add another layer of depth to the analysis. Limited by data availability, this study only focused on national-level CEPZs, and the policy impacts of provincial-level CEPZs warrant further investigation. Meanwhile, the varying implementation effects arising from differences in policy intensity can also be analyzed as a critical variable. Additionally, integrating economic data to evaluate whether the ecological gains outweigh the landscape fragmentation costs would be valuable for sustainable development modeling.

4.4. Conclusions

This study employs a multi-period difference-in-differences (Multi-Period DID) model to evaluate the impacts of establishing Cultural Ecological Protection Zones on ecosystem quality and landscape patterns, using panel data from 213 treatment counties and 249 control counties spanning from 2006 to 2023. By integrating vegetation (NDVI), water (NDWI), productivity (NPP), and landscape structure (LPCI) indicators and conducting rigorous robustness checks, we draw the following core conclusions:

(1)

The policy significantly enhanced vegetation coverage, reflecting improved vegetation health due to targeted conservation measures. However, the impacts on water bodies (NDWI) and productivity (NPP) were statistically insignificant, likely due to the short-term nature of the policy (most areas established post-2010) and interference from regional climate variability. It should be clarified that NDWI cannot distinguish artificial water bodies (such as reservoirs, ponds, artificial wetlands, and irrigation canals) and natural water bodies (such as natural rivers, lakes, and natural wetlands). Natural water bodies are mainly affected by natural factors such as extreme climate (e.g., extreme precipitation, drought) and topography, and are less directly affected by short-term human policy interventions and time scales; while artificial water bodies, although theoretically likely to be affected by CEPZ policies (such as ecological restoration and facility construction), did not show significant statistical differences in NDVI indicators due to the short policy implementation period and the interference of regional climate fluctuations.

(2)

While the policy aimed to preserve cultural–ecological integrity, it inadvertently reduced landscape connectivity and increased fragmentation in most regions. The relevant results were validated through parallel trend testing, placebo testing, and robustness testing.

(3)

The mediation effect analysis shows that the CEPZ policy has a significant promoting effect on ecological protection and construction land expansion in key areas, and further affects the regional ecosystem through the role of two intermediary factors. Specifically, CEPZ promotes ecological protection in key areas and significantly improves NDVI, but its effect on LPCI is not significant; CEPZ promotes the expansion of construction land through facility construction and cultural tourism development, but significantly reduces the NDVI and LPCI.

(4)

Establishing an independent management organization can help implement the CEPZ and have a positive impact on the ecosystem. From the perspective of regional differentiation, the impact of the CEPZ policy on the western region is higher than that on the central and eastern regions.