Forest cover serves as a vital measure of ecological sustainability and has long been a focus of research in environmental economics, geography, and ecology. Forests deliver key ecosystem services that underpin both ecological integrity and human well-being, including soil and water conservation, carbon sequestration, and biodiversity preservation. Simultaneously, they support economic activities through the supply of timber and non-timber forest products. Identifying the drivers of forest cover change is thus essential for designing effective conservation strategies and land-use policies. Existing studies have established that forest dynamics are influenced by a complex set of factors, including natural environmental conditions, demographic trends, institutional arrangements, economic development, agricultural productivity, and infrastructure expansion.

Natural conditions constitute the ecological basis for forest distribution, with topographic and climatic factors systematically influencing land-use returns and, consequently, forest cover outcomes. Areas characterized by steep slopes or high elevation tend to exhibit higher forest persistence, largely due to the elevated costs of agricultural conversion and construction in such settings. In contrast, sun-facing slopes experience greater human pressure, reflecting rational land-use responses to differential productivity and accessibility (Zhao et al., 2020). Climatic variables, particularly precipitation and temperature, further shape forest patterns through their effects on ecological productivity. While increased rainfall generally supports forest growth and economic returns to forest retention, warming in arid regions can induce water stress, lowering regeneration potential and reducing the viability of forest-based land uses (Gong et al., 2020).

Demographic and socioeconomic factors shape forest cover through multiple mechanisms rooted in resource demand, land-use decisions, and technological change. Increased population density raises demand for fuelwood, agricultural land, and built-up areas, often leading to forest clearance and land conversion (Carr et al., 2005). Conversely, urbanization concentrates populations in cities, raising the opportunity cost of rural labor and inducing agricultural land abandonment, which can facilitate forest regeneration in peripheral regions. Technological progress and improved agricultural efficiency may further alleviate pressure on forests by raising crop yields and reducing the need for land expansion. These dynamics resonate with the Environmental Kuznets Curve (EKC) hypothesis, which suggests that environmental degradation tends to increase during early development stages but may decline as economies grow and preferences shift toward environmental quality.

Policy and institutional settings play a decisive role in mediating forest outcomes by altering incentives for conservation and exploitation. China’s Grain for Green Program, initiated in 1999, has become a benchmark large-scale reforestation policy, significantly increasing forest cover in participating regions, especially the rural West (Deng et al., 2011). Other major initiatives, such as the Three-North Shelterbelt Program and the Natural Forest Protection Project, have expanded institutional and financial support for afforestation and sustainable management (Gao and Huang, 2020). Accompanying tenure reforms and ecological redlining policies have strengthened property rights over forest resources and improved local governance. Meanwhile, forest certification, payments for ecosystem services, and public welfare forest programs have helped align economic incentives with ecological objectives (Durst et al., 2006; Li et al., 2018).

Agricultural productivity exerts an ambiguous influence on forest dynamics through competing economic channels. On one hand, total factor productivity (TFP) growth in agriculture can generate land-sparing effects by enabling output growth without extensive land conversion, thereby mitigating deforestation pressure. As demonstrated by Li and Zhu (2023), efficiency gains in agricultural production reduce dependence on chemical inputs and associated ecological damage. On the other hand, enhanced agricultural profitability may create expansionary incentives, particularly where property rights are ill-defined or environmental regulation is weakly enforced, leading to encroachment into forest margins (Heß et al., 2021). Zhang et al. (2025) quantify this trade-off, estimating that a 1% increase in agricultural TFP in China correlates with a 0.012% reduction in forest cover, suggesting that under certain institutional conditions, technological progress may induce frontier expansion rather than conservation.

Economic development and land-use transitions further mediate forest outcomes through structural transformation. During early industrialization phases, rising demand for timber and urban land typically accelerates forest resource depletion (Zhang et al., 2017). However, as economies undergo structural change, increased environmental investment and shifting social preferences often facilitate forest recovery, which is consistent with the Environmental Kuznets Curve framework. Transportation infrastructure plays a particularly significant role in this process: Hu et al. (2016) identified a strong correlation between highway density and forest loss in Fujian, whereas lower-grade roads exhibited minimal impact. Complementing this, Zhao et al. (2020) documented sharply elevated forest loss within 20 km of major roads, revealing spatially delimited ecological externalities from transport networks.

In summary, forest cover dynamics emerge from complex interactions among environmental constraints, demographic pressures, institutional arrangements, and eco-nomic incentives, with their relative importance exhibiting substantial heterogeneity across temporal and spatial contexts.

The global proliferation of road infrastructure in recent decades has raised significant ecological concerns among economists and environmental scientists. Projections indicate that by 2050, the world’s road network will expand by approximately 25 million kilometres (an increase of nearly 60% compared to 2010 levels), with over 90% of this growth occurring in developing nations (Laurance et al., 2014). Much of this development overlaps biologically critical zones, including tropical forests that serve as vital global reservoirs of biodiversity and carbon (Barrett et al., 2016). This spatial convergence underscores a fundamental tension in development policy: balancing the economic benefits of improved transport access against the preservation of ecologically significant forest systems.

From an economic standpoint, road development enhances factor mobility, reduces transaction costs, and improves market integration, contributing positively to regional growth and productivity. Yet these gains are often accompanied by considerable environmental externalities. Empirical studies reflect a dualistic impact: transportation infrastructure can either intensify or mitigate forest loss depending on mediating factors such as institutional quality, market structure, and the stringency of land-use regulations (Deng et al., 2011; Asher et al., 2020; Kaczan, 2020).

On the negative side, road construction is widely identified as a major proximate driver of deforestation. By improving market access and raising agricultural profitability, roads encourage the expansion of farmland into forested areas (Chomitz and Gray, 1996; Busch and Ferretti-Gallon, 2017). This effect is especially pronounced in remote, resource-rich regions where improved accessibility triggers agricultural frontier expansion (Kaczan, 2020). Roads also promote in-migration and settlement in forest regions, amplifying deforestation pressure. Cross-country studies across the Amazon, Southeast Asia, and Central Africa confirm that road development accelerates land conversion and forest loss (Cropper et al., 2001; Deininger and Minten, 2002; Rudel et al., 2009; Pfaff et al., 2018).

Roads also contribute to indirect ecological degradation through land-use conversion and forest fragmentation. Increased accessibility raises land values and investment intensity, encouraging conversion of forest land to construction land (Chomitz and Gray, 1996). Moreover, the physical presence of roads fragments contiguous forest areas, disrupting habitat connectivity and biodiversity (Laurance et al., 2014). In China, Zhao et al. (2020) found that forest loss is most pronounced within 20 km of highways, largely due to improved access for illegal logging and the disruption of ecological corridors.

On the positive side, under strong governance and well-functioning markets, road development can facilitate forest protection through the “Forest Transition” pathway. Improved transportation enhances the profitability of sustainable forestry and plantation management (Foster and Rosenzweig, 2003). Crucially, by lowering migration costs, roads enable rural labor to shift toward non-agricultural sectors in urban centers. This structural transformation reduces the labor supply for agricultural expansion and alleviates pressure on forest margins (Singh et al., 2017). Furthermore, better road connectivity can reduce the cost of alternative energy sources, decreasing household dependence on fuelwood and alleviating pressure on forest ecosystems (DeFries and Pandey, 2010; Aggarwal, 2018).

The impact of road construction on forest cover exhibits substantial regional heterogeneity. In economically advanced regions with well-developed agricultural sectors, road improvements may paradoxically contribute to forest recovery through various economic mechanisms. In contrast, in remote, economically marginalized areas characterized by weaker governance capacity, road infrastructure often accelerates forest degradation and resource over-exploitation. Kaczan (2020) empirical study in India demonstrates these pronounced regional disparities in road impacts, while.

Moreover, the ecological consequences vary significantly across different road hierarchies. National highways and local rural roads differ fundamentally in their transport functions, economic spillover effects, and induced land-use changes. Hu et al. (2016), analyzing data from Fujian province, China (2000–2012), found that while overall road density showed no significant relationship with forest benefits, highways density specifically correlated positively with forest loss. Similarly, Asher et al. (2020), using microdata from India, demonstrated that new rural roads had negligible impacts on forest cover, whereas highway expansion significantly exacerbated forest loss, possibly due to increased timber demand during construction. This heterogeneity underscores the necessity of adopting a differentiated analytical framework that distinguishes between road types in environmental impact assessments.

In summary, road infrastructure manifests a dual character in forest cover dynamics, with outcomes determined by an interplay of regional development levels, institutional arrangements, resource management regimes, and complementary socioeconomic policies. In lagging regions, roads typically exacerbate forest degradation by accelerating agricultural frontier expansion, promoting resource extraction, and inducing migration into forested areas. Conversely, in regions with stronger governance and developed market institutions, road improvements can enhance forestry returns, facilitate labor transition to non-farm sectors, and promote energy substitution, which collectively alleviating pressure on forests and potentially enabling their sustainable management and restoration.

Our study draws on multiple data sources to examine the ecological impacts and underlying mechanisms of highway construction in China’s underdeveloped regions from 2009 to 2019. The start date of 2009 corresponds to the initiation of China’s aggressive infrastructure investment strategy in less developed regions. The endpoint is set at 2019 to exclude the exogenous systemic shocks caused by the COVID-19 pandemic. The subsequent nationwide lockdowns and supply chain disruptions in 2020 would severely confound the identification of market-driven mechanisms, particularly the mobility of goods and personnel, which are central to our analysis.

The empirical design adopts a “buffer-zone” DID design to address the endogeneity of road placement by separating the direct ecological disturbances of highway construction from the economic effects of improved market accessibility. Regions directly traversed by highways often experience significant ecological disturbances during construction, such as soil erosion, land degradation, and vegetation loss resulting from large-scale works (Han et al., 2018), making it difficult to accurately identify the pure effects of market accessibility.

Therefore, we adopt a spatial difference-in-differences design to disentangle the economic mechanism from construction disturbances. We define the treatment group as the immediate neighbors (first-order adjacency) of highway-hosting towns, while explicitly excluding the hosting towns themselves. This exclusion is vital because the deforestation in directly traversed zones likely reflects a mix of land conversion and resource extraction, whereas our focus is solely on the latter. Correspondingly, the control group consists of second-order neighboring townships. This design ensures comparability in geographic characteristics while strictly isolating the spillover effects of improved market access.

More specifically, we specify the baseline model (Equation 1) as follows:

The dependent variable, Forest Cove r i , t represents forest cover for township i at year t. The key explanatory variable, Nbr _ hw { i , t } is a dummy variable that takes the value of 1 if a new highway has opened in a first-order neighboring township of township i by year t , and 0 otherwise.

X i , t represents township-level control variables relevant to forest cover, including connectivity to non-highway roads, railways, and high-speed rail, distance to the nearest highway, the number of township enterprises, forestry land area, and meteorological factors such as annual mean temperature, sunshine duration, precipitation, and humidity (Zhang et al., 2017). We include county-level economic characteristics ( D i , j , t ) such as population, night-time lights, and carbon emissions, along with city-level controls for built-up green space ( C i , c , t ). To account for the influence of non-time-varying economic and geographical factors on the estimation, we include interactions ( Sf ( t ) ) between township-level characteristics (elevation, night-time lights, and distance to the nearest urban center) and a linear time trend t. Here, μ i and υ p , t denotes the township-level fixed effects and the province-by-year fixed effects, respectively. ε i , t the error term, and standard errors are clustered at the city level.

Table 1 presents the summary statistics for the key variables used in this study, covering a total of 84,508 observations. The dependent variable, Forest Cover, has a mean value of 32.28% with a standard deviation of 27.07%. The values range widely from 0.35 to 84.04%, indicating significant variation in forest resources across the sampled underdeveloped regions. Regarding the core independent variables, the mean of highway presence (Nbr_hw) is 0.426, suggesting that approximately 42.6% of the observations in our sample have highway access. In contrast, High-Speed Rail (HSR) is relatively scarce in these regions, with a mean of only 0.031. The control variables encompass a comprehensive set of town, city, and county-level characteristics. Notably, climatic factors (temperature, humidity, precipitation) and socio-economic indicators (population, night time light, number of firms) also exhibit varying degrees of heterogeneity, which are controlled for in our empirical model to isolate the net effect of infrastructure construction.

Table 2 presents the baseline estimates of the impact of road construction on forest cover in underdeveloped regions. Columns (1) through (3) progressively introduce county- and city-level controls, as well as interactions between township characteristics and time trends, to absorb potential confounding factors. Across all specifications, the coefficient for highway construction remains consistently negative and statistically significant, confirming that highway expansion in neighbor townships significantly exacerbates forest loss in these local rural townships. To further validate measurement accuracy, column (4) replicates the analysis using the 250-m resolution global forest cover dataset from Asher et al. (2020), yielding results consistent with our primary findings.

Crucially, column (5) serves as a falsification test by examining the impact of non-highway roads. Unlike highways, non-highway road construction shows no statistically discernible negative effect on forest cover. This comparison is motivated by the need to distinguish the ecological externalities of high-speed market integration from general rural connectivity. The null result for non-highway roads suggests that the observed deforestation is not merely a consequence of physical road paving, but rather driven by the specific economic mechanism of highways. Namely, the reduction in transportation costs that facilitates large-scale resource extraction from rural peripheries. To further verify this, we decomposed the non-highway measure into National Roads and Provincial Roads. As reported in Appendix Table A1, neither category shows a significant negative impact on forest cover, reinforcing the conclusion that the environmental shock is unique to the high-speed expressway network.

Finally, a supplemental analysis of directly traversed townships yields a significantly larger coefficient of −0.5633 Appendix Table A2, likely reflecting the combined impact of construction damage and resource extraction. This result justifies our baseline exclusion of highway-hosting towns, confirming that our design successfully filters out physical engineering noise to strictly isolate the economic spillover effects of market access.

To ensure the validity of the difference-in-differences (DID) estimation, this study first tests the parallel trends assumption. This assumption requires that the treatment and control groups exhibit similar trends in forest cover change prior to the policy intervention, namely the opening of highways. Specifically, the treatment group consists of townships adjacent to newly constructed highways, while the control group comprises second-order neighbouring townships without direct highway access. Any divergence in trends should occur only after the highways become operational, thereby reflecting the causal impact of construction. Accordingly, a parallel trends test is con-ducted to determine whether the groups followed comparable trajectories before the highway opening and whether a significant difference emerged thereafter. The assumption is considered valid if no significant pre-treatment differences are observed, yet a persistent divergence appears following the policy implementation.

Figure 1 plots event-study coefficients and their 95% confidence intervals, where the horizontal axis indicates years relative to the opening of the highways. The omitted category is the year immediately prior to opening (t = −1), which serves as the baseline. Due to limited observations at longer horizons, event-time indicators at the extremes are binned. Specifically, the leftmost coefficient (t = −5) represents an aggregated pre-treatment period covering 5 to 10 years before highway opening, while the rightmost coefficient (t = +5) corresponds to an aggregated post-treatment period covering 5 to 10 years after opening. All other coefficients represent single-year indicators.

As shown in Figure 1, prior to the opening of highways, there is no statistically significant difference in forest cover between the treatment and control groups. However, beginning in the year of highway operation, a discernible gap emerges and remains statistically significant for at least 4 years after the opening. This pattern provides strong empirical support for the validity of the parallel trends assumption and reinforces the robustness of the DID identification strategy.

To rigorously disentangle the temporal dynamics of infrastructure impacts, we follow the strategy proposed by Song et al. (2023) to distinguish between short-run (first 2 years) and long-run (third year onward) effects. The estimates reported in Table 3 corroborate the dynamic patterns observed in Figure 1. Specifically, while we observe a pronounced decline in forest cover in the short run, this negative impact attenuates and turns marginally positive in the long run.

This recovery trajectory mirrors the inverted U-shaped pattern posited by the Environmental Kuznets Curve (EKC) hypothesis (Grossman and Krueger, 1995). Specifically, improved accessibility initially precipitates a surge in timber harvesting due to immediate arbitrage opportunities. Over the longer term, however, sustained reductions in transport costs reshape the regional comparative advantage (Baum-Snow et al., 2020). This structural shift drives the reallocation of labor and capital from resource-extraction sectors toward manufacturing and services. As the local economy climbs the value chain, the reliance on timber-derived income diminishes, thereby allowing forest ecosystems to recover from the initial disturbance.

To address potential endogeneity concerns stemming from the non-random placement of transport infrastructure (e.g., planners prioritizing areas with high growth rates that correlate with forest cover changes), we introduce an instrument based on hypothetical least-cost path spanning tree networks. Following Faber (2014), we construct an instrumental variable based on the predicted highway network that connects regional economic hubs along routes minimizing construction costs determined by terrain characteristics. The key insight is that, conditional on the set of connected cities to be connected, the exact routing of highways is primarily governed by geographic construction costs, such as slope, elevation, and terrain ruggedness, rather than by local economic fundamentals. As argued in Faber (2014), once planners’ decisions regarding which cities to connect are taken as given, variation in highway alignments largely reflects engineering constraints instead of anticipated regional economic outcomes.

Building on this principle, we use a minimum spanning tree (MST) algorithm to simulate the counterfactual highway network. The MST connects major cities while minimizing total construction costs implied by geographic features, thereby closely approximating the actual routing logic of highway construction. Because terrain and topographic characteristics are predetermined and plausibly exogenous to contemporary economic and environmental outcomes, the resulting predicted highway exposure satisfies the exclusion restriction. This approach allows us to isolate exogenous variation in highway placement arising from geographic cost minimization, strengthening the causal interpretation of the estimated effects.

Table 4 reports the two-stage regression results using the instrumental variable. The results indicate that highway construction has a significant negative impact on forest cover in underdeveloped regions, and the coefficients from the two-stage regression are similar to the baseline estimates, confirming the robustness of our main findings. The F-statistics show that the regressions pass the weak instrument test, suggesting that our estimates are reliable.

We begin our robustness checks by verifying the stability of our baseline results against alternative control group definitions. As reported in columns (1) to (3) of Table 5, we assess the robustness of our results by successively employing alternative control groups: (i) third-order neighbors of towns traversed by highways; (ii) non-treated towns within the same county; and (iii) non-treated towns within the same city where new highways were constructed. Across all alternative control group definitions, the negative effect of highways openings on forest cover remains statistically significant. Moreover, the results in Table 5 indicate that the adverse impact on forest cover is more pronounced when the control group is restricted to towns within the same county.

To further verify the sensitivity of our findings to the definition of transport accessibility, we employed three alternative continuous and nuanced indicators: (1) Euclidean distance to the nearest highway ramp; (2) road density, defined as the total length of highways per unit of land area; and (3) population-weighted accessibility, an indicator capturing the intensity of transport infrastructure relative to human activity. To ensure comparability with our baseline binary identification strategy, we categorized townships into “High Accessibility” (Treat = 1) and “Low Accessibility” (Control = 0) groups based on the median values of these metrics within each city-year cell. The results, reported in Appendix Table A3, indicate that the coefficients remain statistically significant and negative across all three specifications. The magnitudes are highly consistent with our baseline estimates. This confirms that our core finding, which shows that highway access precipitates forest cover loss, is robust to the specific measurement of accessibility.

To rigorously address the statistical significance observed at t = − 5 and mitigate potential pre-testing biases as cautioned by Roth (2022), we employ the sensitivity analysis framework proposed by Rambachan and Roth (2023) to test the robustness of our results against potential violations of the parallel trends assumption. Specifically, we construct 95% robust confidence intervals under the “Relative Magnitude” assumption, and the results (Figure 2) demonstrate that our estimated treatment effect remains statistically significant (excluding zero) even when allowing for post-treatment biases to be as large as the maximum observed pre-treatment deviation ( M ¯ = 1 ), thereby confirming that the specific pre-trend fluctuation is insufficient to invalidate our main conclusions (Figure 2).

Furthermore, we re-estimate our dynamic specifications using the interaction-weighted estimator proposed by Sun and Abraham (2021) to mitigate potential biases associated with staggered treatment timing. This approach constructs an unbiased counterfactual by interacting cohort indicators with relative event-time dummies, employing only never-treated (or last-treated) units as the effective control group to avoid contamination from already-treated cohorts. As shown in Figure 3, the estimates and their 95% confidence intervals derived from the interaction-weighted estimator closely mirror the magnitude and temporal pattern of our baseline results: a significant treatment effect emerges immediately upon highway opening, persists for several periods, and gradually attenuates over time. This consistency confirms that our findings are not driven by TWFE weighting failures.

Agricultural dynamics represent another critical source of potential confounding factors. To isolate the causal effect of infrastructure, we introduce multiple sets of control variables in Table 6. Existing literature presents contrasting pathways through which agriculture may influence forest cover. Some studies suggest that rising agricultural productivity can reduce rural households’ reliance on forest resources, thus supporting short-term forest conservation (Borlaug, 2007; Abman and Carney, 2020). Structural shifts within the agricultural sector, such as the growing share of livestock and fisheries, improved cropland use efficiency, and policies like the Grain for Green Program, may also alleviate pressure on forests. Conversely, other research highlights that agricultural expansion can drive land-use conversion and intensify deforestation (Asher et al., 2020).

Specifically, columns (1) and (2) of Table 6 control for agricultural output at the county level and primary industry output at the city level, respectively, to capture regional agricultural productivity changes. Columns (3) and (4) further incorporate provincial timber production and the implementation intensity of the “Grain for Green Program” to control for macro-level influences on forest resource use. The results indicate that even after accounting for these agricultural factors, the opening of highways in less developed regions continues to exhibit a statistically significant negative effect on forest cover.

We argue that highways construction may contribute to the decline in forest cover through two potential mechanisms, as illustrated in Figure 4. First, improved highway access may stimulate local demand-driven deforestation. By enhancing rural connectivity, highways could promote the expansion of agricultural land at the expense of forest cover and increase local consumption of forest products such as firewood, thereby intensifying timber harvesting. Second, highways may reshape interregional supply chains. Enhanced transport infrastructure strengthens the linkage between peripheral forested areas and central urban markets, facilitating the flow of timber and other forest products to economically dynamic regions. As demand for timber, pulp, and construction materials grows in urban centers, reduced transportation costs provide less-developed areas with a comparative advantage in supplying these resources, accelerating both harvesting and outbound shipment of forest products.

Finally, this study further investigates the ecological externality costs associated with highway construction. The empirical results (Table 12, Panel A) indicate that the reduction in forest cover caused by highway openings significantly increases local greenhouse gas emissions, particularly carbon dioxide (CO₂) and fluorinated gases. This suggests that forest loss may undermine the region’s carbon sequestration capacity, thereby contributing to the accumulation of greenhouse gases. Additionally, from a vehicle exhaust perspective (Panel B), although highway openings do increase vehicle traffic in the region, they do not have a significant impact on emissions of carbon monoxide (CO), ozone (O₃), nitrogen oxides (NOₓ), or sulfur dioxide (SO₂). This discrepancy suggests that while expressways primarily impact the environment through the permanent loss of ecological assets (forests), their direct contribution to localized air pollution in rural hinterlands may be secondary or mitigated by the non-congested nature of rural transit.

This study investigates the ecological consequences of transport infrastructure expansion in China’s rural hinterlands, focusing on the fundamental trade-off between accessibility and sustainable forestry. Leveraging town-level panel data and a Minimum Spanning Tree (MST) instrumental variable strategy, we provide robust causal evidence on how different types of infrastructure reshape the distribution of rural ecological capital.

Our findings reveal a significant negative spatial spill-over effect: the opening of highways in neighboring townships reduces forest cover in local underdeveloped hinterlands that lack direct highway access. However, dynamic analyses clarify that this is a transient “extraction shock” rather than a permanent structural decline, with the negative impact attenuating significantly over the long term. In contrast, non-highway rural roads show no statistically discernible negative impact, suggesting they offer a more ecologically sustainable path for connectivity.

Mechanism analysis, grounded in the core-periphery model, elucidates the economic drivers of this deforestation: highways lower transportation costs, thereby accelerating the extraction of timber and forest resources from peripheral rural areas to serve demand in central markets. This “siphon effect” is most pronounced near highway exits, in broadleaf forests with higher commercial value, and in regions with weaker economic foundations. Furthermore, we find that this infrastructure-induced deforestation contributes to increased greenhouse gas emissions, undermining regional climate resilience.

Overall, these results highlight a fundamental tension between infrastructure-led integration and rural ecological security. While high-speed connectivity facilitates market access, it risks depleting rural natural capital if not managed with strict safeguards. Consequently, achieving sustainable rural development requires a paradigm shift in infrastructure planning from distinct projects to a holistic, spatially differentiated strategy. Specifically, policymakers should prioritize the upgrade of rural and secondary roads over the excessive expansion of highways in ecologically sensitive areas, as our evidence suggests that non-highway roads offer a sustainable pathway for enhancing local livelihoods without compromising ecosystem integrity.

Furthermore, to mitigate the unintended “siphon effect” of high-speed networks, it is imperative to strengthen rural forest governance by integrating transport planning with Ecological Redlines. Drawing on our heterogeneity analysis, enforcement should be precise rather than uniform: monitoring efforts must be specifically intensified within a 10 km radius of highway exits and in zones dominated by high-value broadleaf forests, as these areas are identified as the primary hotspots of market-driven exploitation. Crucially, this targeted protection extends beyond biodiversity conservation to the preservation of rural life-support systems. Forests act as essential ecological barriers that safeguard long-term agricultural productivity through soil retention, water regulation, and local climate stabilization. Unchecked deforestation in these critical zones’ risks exacerbating soil erosion and increasing the vulnerability of farmland to extreme weather, thereby threatening regional food security. Thus, maintaining forest integrity must be viewed as a strategic imperative to ensure that short-term resource extraction does not undermine the natural foundations of grain production.

Moreover, recognizing that highways facilitate the flow of resources from rural peripheries to urban cores, fiscal mechanisms such as Payment for Ecosystem Services (PES) should be established to transfer benefits from downstream urban consumers to upstream rural conservators. By reallocating a portion of highway revenue to fund local reforestation, the environmental costs of connectivity can be internalized, ensuring that China’s rural revitalization pursues a resilient trajectory that reconciles economic progress with long-term environmental sustainability.