The intricate interplay between digital economy development and green, low-carbon policies has become a pivotal theme in understanding modern labor market transitions, especially in rural and semi-urban environments. New empirical evidence from China sheds light on how these forces jointly influence non-agricultural employment (NAE), revealing nuanced pathways and complex dynamics at the county level. This deep-dive study leverages robust statistical methodologies to unpack not only the magnitude but also the contextual variability of digital and green policy effects, providing fresh insights into their combined potential and inherent tensions.
At the heart of this research lies a sophisticated approach that combines fuzzy-set Qualitative Comparative Analysis (fsQCA) with multiple linear regression (MLR) models. The fsQCA illuminates multiple configurational pathways, emphasizing that different combinations of digital and green policy conditions are sufficient to drive NAE, but it stops short of quantifying their average marginal impacts. To fill this gap, the study conducts a series of regression analyses controlling for diverse dimensions of digital infrastructure, including internet penetration rates, e-commerce activity, agricultural innovation, and platform economy engagement.
The first layer of analysis incorporates Internet Penetration Rate (IPR) as a primary control variable. Model outcomes demonstrate remarkably high explanatory power, accounting for over 96% of county-level NAE variation. Among the green policy instruments, fiscal supports such as Green Subsidy Policies (GSP) and Carbon Emission Trading Policies (CETP) emerge as significant positive contributors to non-agricultural job creation. This suggests that such policies work synergistically with advanced digital infrastructure to expand service-oriented employment niches, particularly in digitally-empowered counties.
Conversely, ecological restructuring policies (GASP) and technology substitution initiatives (LTAP) show a contrasting pattern. Both register statistically significant negative coefficients, implying these strategies may, in the short term, disrupt traditional employment structures. Such disruption is consistent with transitional frictions as labor markets adjust to newer economic realities brought on by ecological reforms and technology-driven shifts. Importantly, these initial displacement effects highlight the need for complementary mechanisms that can facilitate labor reskilling and sectoral mobility to mitigate temporary job losses.
Delving deeper, the study differentiates digital infrastructure by isolating E-commerce Platform Participation Rate (EPPR) as an exclusive control. This approach effectively holds the broader digital market access constant, thereby focusing on how online commercial activity interacts with green policies to influence employment outcomes. Findings reveal that robust e-commerce engagement strongly correlates with increased NAE, underlining the rising significance of the digital marketplace in rural job absorption.
However, the disrupted pattern of green policy effects intensifies in this model. GASP and LTAP’s negative impacts become even more pronounced, which may be interpreted as evidence that unchecked digital commercialization could exacerbate displacement of traditional livelihoods if not paired with production-side innovations. Meanwhile, CETP continues to demonstrate a positive influence, reinforcing its role as a market-aligned instrument capable of facilitating job creation in the evolving green-digital landscape. Notably, traditional green subsidies lose their statistical relevance, hinting that without integration into digital platforms and ecosystems, fiscal support may have limited leverage.
Expanding further, the third regression model utilizes Agricultural Innovation and Application Level (AIAL) as focal control. This setting captures production-side digitalization — technological integration directly within agricultural processes — as a proxy for the broader digital skills and infrastructure impacting the transition out of agriculture. Here, results strongly reinforce the “technology-enabled labor release” hypothesis, with AIAL exerting a robust positive effect on NAE.
Within such technologically advanced agricultural contexts, both fiscal incentives and carbon trading policies maintain their positive impacts on non-agricultural job growth, suggesting that these green policy instruments are particularly effective when underpinned by sophisticated production technologies. The persistence of negative coefficients for ecological and technological restructuring again underscores transitional employment disruptions, emphasizing the temporal complexity of structural economic adjustments in rural counties. The policy implication points towards prioritizing CETP and GSP to maximize synergies with digital agricultural modernization, while deploying focused reskilling efforts to buffer labor market shocks associated with GASP and LTAP.
The fourth and final regression model focuses on Digital Platform Engagement (DPE) as the singular digital control variable, zeroing in on employment effects in highly platform-penetrated regions. This model continues to demonstrate that platform participation drives significant employment increases outside agriculture, attesting to the platform economy’s absorptive capacity for rural labor. CETP remains a steady positive force, reinforcing its wide-ranging applicability across digital contexts.
However, in these highly platformized environments, the efficacy of GSP diminishes, losing statistical significance. This diminishing return suggests a substitution effect where fiscal subsidies may be partially superseded or overshadowed by the more dynamic mechanisms inherent to digital platform ecosystems. Meanwhile, both GASP and LTAP retain their negative impacts, indicating that despite platform-driven growth, the disruptive nature of ecological and technological transitions still tempers employment gains. This pattern calls for complementary strategies aimed at enhancing workforce adaptability and smoothing sectoral transfers during green transitions.
Taken collectively, the multi-model analysis crystallizes three pivotal insights. First, carbon emission trading policies stand out for their consistent, positive association with non-agricultural employment, irrespective of digital infrastructure variation. This underlines the policy’s robustness in fostering green resource allocation and enabling job creation in sectors centered around carbon asset management, certification services, and related logistics. Second, ecological restructuring and technology substitution policies inherently carry transitional costs that constrain immediate employment benefits. Their structural and skill-related challenges necessitate tailored labor market interventions. Third, green subsidies’ impact appears contingent on their strategic integration within digital ecosystems—without which their potential to induce employment growth remains limited in the digital era.
The implications of these findings are far-reaching. They underscore the critical importance of synchronizing digital economy expansion with green policy frameworks to harness the full employment benefits of both domains. In particular, they caution against uncoordinated, early-stage digital commercialization that lacks sufficient accompanying production upgrades, which may exacerbate labor displacement rather than alleviate it. Market-based green instruments like carbon trading offer a promising pathway to aligning digital and green transformation agendas, supporting sustainable employment gains.
From a methodological perspective, the stepwise incorporation of varied digital controls offers a refined causal lens for interpreting green policy impacts, enabling a clearer distinction of marginal effects amid the complex digital–green interplay. The study’s robustness is further supported by its thorough checks against multicollinearity and its high explanatory power across all model specifications, lending credibility to its empirical conclusions.
For policymakers, these results suggest a differentiated approach is essential. Emphasizing carbon market mechanisms and aligned fiscal incentives in digitally mature areas can spur employment in emerging green service sectors. Simultaneously, targeted reskilling programs and industrial transition support are vital to alleviate the short-term displacement effects tied to ecological restructuring and technological substitution, ensuring longer-term labor market sustainability.
The research also highlights a dynamic digital–green feedback loop. As counties advance in digital platform participation, the nature and effectiveness of green policies evolve, with implications for subsidy designs and market intervention strategies. Understanding these nuanced dynamics could unlock new opportunities for driving a just transition that leverages both digital innovation and green development to maximize rural employment diversification.
In sum, this expansive analysis elucidates the multifaceted and context-dependent roles of digital economy factors in modulating green policy outcomes for non-agricultural employment. It reveals a complex tapestry where technological modernization, market incentives, and ecological reforms intersect, shaping labor market trajectories in profound, sometimes counterintuitive ways. This work not only enriches academic discourse but also provides actionable intelligence to guide the delicate balancing act of nurturing sustainable rural labor markets amid accelerating digital and environmental transformations.
Subject of Research: The joint impact of digital economy development and green, low-carbon policies on non-agricultural employment in rural Chinese counties.
Article Title: How digital economy and green and low carbon policies affect non-agricultural employment?—Evidence from China.
Article References:
Hu, Z. How digital economy and green and low carbon policies affect non-agricultural employment?—Evidence from China.
Humanit Soc Sci Commun 12, 1260 (2025). https://doi.org/10.1057/s41599-025-05398-3
Image Credits: AI Generated
