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China’s coordinated digital‑intelligent pilot programs raised urban green economic efficiency by roughly 5 percentage points, largely via improved ecological resilience, more green patents and a surge in new firms; effects are strongest in growing/mature resource cities and high‑fintech areas.

Can Digital–Intelligent Integration Enhance Urban Green Economic Efficiency? An Empirical Analysis Based on National Big Data Comprehensive Pilot Zones and Smart-City Dual-Pilot Programs

Feng He, Yue Zhang · February 07, 2026 · Sustainability

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Using a staggered DID on 279 Chinese cities (2010–2021), the paper finds that digital–intelligent integration driven by dual national pilots causally raised urban green economic efficiency by about 5.03 percentage points, mediated mainly through ecological resilience, green-technology innovation, and entrepreneurial vitality.

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Digital–intelligent integration (DII) has emerged as a pivotal driver for high-quality urban development, offering a pathway to overcome pressing resource and environmental constraints. By harnessing data as a core production factor and integrating advanced intelligent technologies, DII can substantially elevate urban green economic efficiency (GEE). This study constructs a quasi-natural experiment using the staggered rollout of national big data comprehensive pilot zones (initiated in 2012) and smart-city pilot programs (from 2016 onward). Employing a rigorous staggered difference-in-differences (DID) estimator on panel data from 279 Chinese prefecture-level cities over 2010–2021, we find that DII causally increases GEE by 5.03 percentage points (p < 0.01). This benchmark result remains robust across a comprehensive set of checks, including parallel-trend validation, placebo tests, double/debiased machine learning, two-stage least squares with historical IT-sector instruments, and controls for overlapping policies (e.g., ETS, low-carbon pilots, green finance zones). Mechanism analysis, conducted via a sequential 2SLS control-function approach with lagged mediators and Sobel–Goodman mediation tests, reveals three theoretically grounded channels: (i) enhanced urban ecological resilience (mediates 62%, z = 4.68), (ii) accelerated green technological innovation (55%, z = 4.12, measured by IPC/Y02 patent share), and (iii) heightened entrepreneurial vitality (58%, z = 4.39, new firms per 10,000 residents). Heterogeneity tests show pronounced effects in growing and mature resource-based cities (+1.21% and +11.21%), high-fintech cities (+11.35%), and high-river-density areas (+10.29%) but insignificant impacts in declining resource-exhausted cities (joint F p = 0.08). This study makes four key contributions: (1) it innovatively constructs a continuous DII policy variable by exploiting the synergistic timing of dual pilots, thereby overcoming the limitation of analyzing policies in isolation; (2) it opens the “theoretical black box” by integrating institutional theory and information economics into a unified conceptual framework that explicitly links DII to GEE through reduced transaction costs and alleviated information asymmetry; (3) it enriches the mediation identification strategy in staggered settings using 2SLS control functions and sequential G-estimation, addressing endogeneity in intermediary variables more rigorously than traditional three-step approaches; and (4) it delivers nuanced evidence on the contextual conditions (when and where) under which DII yields the strongest green dividends, providing actionable guidance for China’s “dual-carbon” goals and the global green transition.

Summary

Main Finding

Digital–intelligent integration (DII) — operationalized via the staggered national rollout of big-data comprehensive pilot zones (from 2012) and smart‑city pilots (from 2016) — causally raises urban green economic efficiency (GEE) in Chinese cities by 5.03 percentage points (p < 0.01). Effects are robust to a broad battery of tests and operate mainly through three channels: enhanced urban ecological resilience, accelerated green technological innovation, and increased entrepreneurial vitality.

Key Points

Estimated average treatment effect: +5.03 percentage points on GEE (p < 0.01).
Robustness: results survive parallel-trend checks, placebo tests, double/debiased machine learning, 2SLS with historical IT-sector instruments, and controls for overlapping policies (ETS, low-carbon pilots, green finance zones).
Mechanisms (sequential 2SLS control-function + Sobel–Goodman mediation tests):
- Urban ecological resilience mediates ~62% of the effect (z = 4.68).
- Green technological innovation (IPC/Y02 patent share) mediates ~55% (z = 4.12).
- Entrepreneurial vitality (new firms per 10,000 residents) mediates ~58% (z = 4.39).
Heterogeneity:
- Stronger effects in growing (+1.21%) and mature (+11.21%) resource-based cities.
- Large effects in high-fintech cities (+11.35%) and high-river-density areas (+10.29%).
- Insignificant effects in declining/resource-exhausted cities (joint F p = 0.08).
Conceptual contributions:
Constructs a continuous DII policy measure by exploiting dual-pilot timing and their synergy.
Integrates institutional theory and information economics to link DII → lower transaction costs & information asymmetry → higher GEE.
Advances mediation identification in staggered-treatment settings using 2SLS control functions and sequential G-estimation to address endogeneity of mediators.
Provides context-specific evidence on when/where DII generates green dividends, informing “dual‑carbon” policy design.

Data & Methods

Data: panel of 279 Chinese prefecture-level cities, 2010–2021.
Quasi-experimental variation: staggered introduction of national big-data comprehensive pilot zones (2012+) and smart-city pilots (2016+).
Main estimator: staggered difference-in-differences (DID) with a continuous DII policy variable capturing the combined timing/synergy of the two pilots.
Robustness and identification checks:
- Parallel-trend tests and placebo inference.
- Double/debiased machine learning for flexible covariate adjustment.
- Two-stage least squares (2SLS) using historical IT-sector instruments.
- Controls for contemporaneous policies (emissions trading systems, low‑carbon pilot programs, green finance zones).
Mechanism analysis:
- Sequential 2SLS control-function approach with lagged mediators to handle mediator endogeneity in staggered settings.
- Sobel–Goodman mediation tests for significance and share of mediation.
Outcome measure: green economic efficiency (GEE) — city-level indicator (paper constructs/uses standard GEE measure; mechanism proxies include IPC/Y02 patent share for green innovation, new firms per 10k for entrepreneurship, and an urban ecological-resilience index).

Implications for AI Economics

Data + AI as green inputs: The study shows data-driven intelligent technologies (DII) appreciably boost environmental productivity. In AI economics terms, data and AI constitute a measurable production factor that raises total factor productivity in a “green” dimension.
Market frictions and information economics: DII reduces information asymmetries and transaction costs, improving resource allocation for green goods and services. This underscores the value of information-market interventions (platforms, data-sharing regimes, governance) in enabling AI-driven environmental gains.
Innovation and diffusion: AI-enabled DII accelerates green technological innovation (measured by green patent shares). This suggests complementary effects between digital/AI infrastructure and the rate of invention/adoption in green tech — relevant for modeling endogenous innovation with AI as an accelerator.
Entrepreneurship and creative destruction: Higher entrepreneurial vitality implies AI/digital integration lowers setup costs or markets frictions for green startups. AI economics models should account for how AI infrastructure changes firm entry/exit dynamics and startup ecosystems, especially for green-oriented firms.
Heterogeneity matters: The strong regional heterogeneity (finance, natural endowments, resource maturity) indicates AI/digital policies produce uneven gains — important for welfare and distributional modeling. Policy targeting and complementary investments (finance, institutions, natural-resource management) amplify AI’s green impact.
Policy design and measurement:
- Multi-policy synergy: Combining digital pilots with other environmental policies (ETS, green finance) requires careful accounting for interactions when evaluating AI policies.
- Causal identification: The paper’s mediation identification advances are relevant for AI economists seeking to unpack causal channels (e.g., distinguishing algorithmic performance improvements vs. adoption effects).
Research directions:
- Micro-level analyses linking firm-level AI adoption to emissions/output to trace granular channels.
- Labor-market impacts of DII: skills, reallocation, and wage/inequality dynamics in green-AI transitions.
- Cost-benefit and distributional assessments of AI/digital infrastructure investments for climate goals.
- External validity: testing similar DII–GEE links in non‑Chinese institutional contexts.

Limitations to note for AI economists: while identification strategies are strong, external validity beyond the Chinese institutional/policy environment requires further testing. The study’s mediator measures are aggregated city-level proxies; firm- and household-level mechanisms deserve deeper microdata investigation.

Assessment

Paper Typequasi_experimental Evidence Strengthhigh — The study uses a quasi-natural experiment with staggered DID and constructs a continuous treatment intensity, and it conducts an extensive battery of robustness checks (parallel trends, placebo tests), modern estimation techniques (DML), IVs (historical IT-sector instruments), and explicit mediation analysis with 2SLS control functions — collectively providing strong causal evidence that DII increased urban green economic efficiency in the sample. Methods Rigorhigh — Methods are rigorous and state-of-the-art: careful staggered-DID design, continuous treatment measure, multiple diagnostic and placebo exercises, machine-learning–based robustness, instrumental-variables estimation, and an explicit sequential 2SLS control-function approach to address endogeneity of mediators; the paper also reports heterogeneity and overlapping-policy controls. Remaining concerns are standard for such designs (e.g., instrument exogeneity, spillovers, heterogeneous treatment timing assumptions), but the authors take multiple sensible steps to mitigate them. SamplePanel of 279 Chinese prefecture-level cities observed annually from 2010–2021; treatment is city-level exposure/intensity to combined national big-data pilot zones (from 2012) and smart-city pilot programs (from 2016); outcome is city-level green economic efficiency (GEE); mediators include urban ecological resilience index, green-technology innovation measured by IPC Y02 patent share, and entrepreneurial vitality measured by new firms per 10,000 residents; controls include city socioeconomics and overlapping national green policies (ETS, low-carbon pilots, green finance zones). Themesproductivity adoption innovation governance IdentificationStaggered difference-in-differences exploiting staggered national pilot rollouts (big-data comprehensive pilot zones from 2012 and smart-city pilots from 2016) to construct a continuous DII policy exposure, supplemented by placebo tests, parallel-trend checks, double/debiased machine learning, two-stage least squares using historical IT-sector instruments, and controls for overlapping policies (ETS, low-carbon pilots, green finance zones). Mechanisms tested with sequential 2SLS control-function and Sobel–Goodman mediation tests. GeneralizabilityResults are specific to Chinese prefecture-level cities and the institutional context of national pilot programs, so transferability to other countries or non-policy-driven AI adoption is limited., Treatment measures policy-driven DII exposure, not direct firm- or worker-level AI adoption/intensity, limiting inference about micro-level productivity or labor-market impacts., Outcome focuses on green economic efficiency rather than broad productivity, wages, or employment, so applicability to general AI-driven productivity debates is partial., Time period ends in 2021; rapidly evolving AI technologies and post-2021 deployments may alter effects., Potential spatial spillovers between cities could bias city-level estimates if not fully accounted for.

Claims (12)

Claim	Direction	Outcome	Confidence & Evidence	Details
Digital–intelligent integration (DII) causally increases urban green economic efficiency (GEE) by 5.03 percentage points (p < 0.01). Firm Productivity	positive	urban green economic efficiency (GEE)	Reading fidelity high Study strength high	n=279 5.03 percentage points (p < 0.01) 0.8
The benchmark DII → GEE result remains robust across multiple checks including parallel-trend validation, placebo tests, double/debiased machine learning, two-stage least squares with historical IT-sector instruments, and controls for overlapping policies (e.g., ETS, low-carbon pilots, green finance zones). Firm Productivity	positive	urban green economic efficiency (GEE) robustness	Reading fidelity high Study strength high	n=279 0.8
DII enhances urban ecological resilience, which mediates 62% of the DII effect on GEE (z = 4.68). Other	positive	urban ecological resilience (mediator)	Reading fidelity high Study strength medium	n=279 62% (z = 4.68) 0.48
DII accelerates green technological innovation, which mediates 55% of the DII effect on GEE (55%, z = 4.12), measured by the IPC/Y02 patent share. Innovation Output	positive	green technological innovation (IPC/Y02 patent share)	Reading fidelity high Study strength medium	n=279 55% (z = 4.12) 0.48
DII heightens entrepreneurial vitality, which mediates 58% of the DII effect on GEE (58%, z = 4.39), measured by new firms per 10,000 residents. Hiring	positive	entrepreneurial vitality (new firms per 10,000 residents)	Reading fidelity high Study strength medium	n=279 58% (z = 4.39) 0.48
Heterogeneity tests show pronounced positive DII effects on GEE in growing resource-based cities (+1.21%), mature resource-based cities (+11.21%), high-fintech cities (+11.35%), and high-river-density areas (+10.29%). Firm Productivity	positive	urban green economic efficiency (GEE) by subgroup	Reading fidelity high Study strength medium	n=279 +1.21%, +11.21%, +11.35%, +10.29% (for specified subgroups) 0.48
DII has an insignificant impact on GEE in declining/resource-exhausted cities (joint F p = 0.08). Firm Productivity	null_result	urban green economic efficiency (GEE) in declining/resource-exhausted cities	Reading fidelity high Study strength medium	n=279 insignificant (joint F p = 0.08) 0.48
The study uses a panel of 279 Chinese prefecture-level cities over 2010–2021. Other	null_result	sample coverage and period	Reading fidelity high Study strength high	n=279 0.8
Methodological contribution: the paper constructs a continuous DII policy variable by exploiting the synergistic timing of dual pilots (big data comprehensive pilot zones and smart-city pilots), overcoming limitations of analyzing policies in isolation. Other	positive	policy variable construction	Reading fidelity high Study strength medium	n=279 0.48
Identification/mediation strategy contribution: the study enriches mediation identification in staggered settings by using 2SLS control functions and sequential G-estimation, addressing endogeneity in intermediary variables more rigorously than traditional three-step approaches. Other	positive	mediation identification strategy	Reading fidelity high Study strength medium	n=279 0.48
Conceptual/theoretical contribution: the paper integrates institutional theory and information economics to link DII to GEE via reduced transaction costs and alleviated information asymmetry. Governance And Regulation	positive	theoretical linkage (transaction costs, information asymmetry)	Reading fidelity high Study strength speculative	n=279 0.08
Policy relevance claim: the results provide actionable guidance for China's 'dual-carbon' goals and the global green transition. Governance And Regulation	positive	policy guidance relevance	Reading fidelity high Study strength speculative	n=279 0.08