Enterprise AI Analysis
The Mechanism of Digital Economy Improving Green Total Factor Productivity
In the crucial transition period of China's economy from high-speed to high-quality development, the digital economy's growth rate has always exceeded that of GDP, and it has become the core driving force for economic transformation and upgrading. This paper explores whether the digital economy can break through traditional production factors and act as a new driving force for China's green economic transformation. It examines the mechanism by which the digital economy boosts Green Total Factor Productivity (GTFP) by testing the mediating roles of industrial upgrading and technological progress.
Executive Impact Snapshot
A concise overview of the critical findings and their immediate implications for enterprise strategy, derived from the core research.
0
Digital Economy Index Growth (2012-2021)
0
Digital Economy Contribution to GDP (2022)
TC
Primary GTFP Improvement Driver
0
Direct Digital Economy Impact on GTFP
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
400%+
Growth rate of China's Digital Economy Index (2012-2021)
Regional Digital Divide
The national digital economy development index has steadily ascended from 0.1785 in 2012 to 1.0000 in 2021, with a growth rate exceeding 400% and an average annual growth rate surpassing 20%. However, regional disparities exist in the development of China's digital economy. The digital economy development level in the eastern region demonstrates distinct advantages in comparison to that in the central and western regions, which reflects a 'digital divide' among regions (Figure 4).
| Region | Development Trend | Key Characteristic |
|---|---|---|
| Eastern Region | Steadily ascending, Substantial upsurge post-2016 | Distinct advantages, Exceeding 16% growth |
| Central & Western Regions | Gradual increase, Lagging post-2016 | Growth rates between 10-12%, Still lag behind eastern regions |
Technological Change
Primary Driver of GTFP Improvement
GTFP Trends and Drivers
China's GTFP exhibits an overall upward trend, but with significant disparities across regions (Figure 5). Crucially, Technological Change (TC) is identified as the primary driving force for GTFP improvement, rather than Efficiency Change (EC). The contribution of TC has consistently risen over the past decade, propelling GTFP primarily through green technology advancement.
GTFP Measurement Process
Input Indicators (Labor, Capital, Energy)
→
Output Indicators (Anticipated & Unanticipated)
→
SBM Directional Distance Function
→
Malmquist-Luenberger (ML) Index
→
Green Total Factor Productivity (GTFP)
Direct and Mediating Effects
The digital economy has a significant direct and positive effect on GTFP, with a coefficient of +0.725 (Table 2). Beyond this direct influence, it also impacts GTFP through mediating mechanisms. Specifically, it effectively promotes green technological progress and industrial advancement, both of which consequently enhance GTFP through partial mediating effects. However, while the digital economy promotes industrial rationalization, this, in turn, is not beneficial for the improvement of GTFP.
Mechanism 1: Technological Progress Mediating Effect
Digital Economy Development
→
Promotes Green Technological Progress (TP)
→
Enhances GTFP
Mechanism 2: Industrial Advancement Mediating Effect
Digital Economy Development
→
Promotes Industrial Advancement (IA)
→
Enhances GTFP
Mechanism 3: Industrial Rationalization Mediating Effect (Negative)
Digital Economy Development
→
Promotes Industrial Rationalization (IR)
→
NOT Beneficial for GTFP
Managerial Implications for Promoting GTFP through Digital Economy
Scenario: To effectively leverage the digital economy for enhancing Green Total Factor Productivity, strategic interventions are required across technology, industrial advancement, rationalization, and efficiency.
Outcome: Implementation of targeted policies in these areas can lead to sustainable economic growth and environmental stewardship.
Green in Technology
Strategies to enhance the development of digital economy infrastructure, increase investment in green tech, cultivate innovation momentum, and build R&D "Quaternity Alliances" for advancing digital technologies.
Green in Advancement
Focus on fully leveraging financial market and third-party professional service institutions, promoting industrial structure advancement and digitalization through technological integration, and cultivating talents for innovative digital economy development.
Green in Rationalization
Maximizing government's influential position for differentiated development policies, promoting industrial structure rationalization, and cultivating benchmark and 'Little Giant' enterprises in the digital economy.
Green in Efficiency
Advocating for the digital economy to optimize resource utilization and allocation, deepening regional cooperation for technological innovation, and fully leveraging the spatial effect of digital economy in improving GTFP.
Project Your Enterprise AI ROI
Estimate the potential cost savings and efficiency gains your organization could achieve with a tailored AI implementation, based on industry benchmarks.
Estimated Annual Savings
$0
Reclaimed Employee Hours
0
Your AI Implementation Roadmap
A phased approach to integrate advanced AI capabilities into your enterprise, ensuring maximum impact and minimal disruption.
Phase 1: Discovery & Strategy
Comprehensive analysis of existing infrastructure, data ecosystem, and business objectives to define clear AI use cases and a strategic roadmap. Includes workshops, data audits, and initial solution design.
Phase 2: Pilot & Proof-of-Concept
Development and deployment of a targeted AI pilot program to validate technical feasibility and demonstrate initial ROI. Focus on a high-impact, low-risk area to gather critical insights and refine the approach.
Phase 3: Scaled Implementation
Full-scale integration of AI solutions across relevant departments and workflows. This phase includes robust system architecture, custom model training, and seamless API integrations with existing enterprise software.
Phase 4: Optimization & Future-Proofing
Continuous monitoring, performance tuning, and iterative enhancement of AI models. Establishment of governance frameworks, MLOps practices, and ongoing support to ensure long-term value and adaptability to evolving business needs.
Ready to Transform Your Enterprise?
Let's discuss how our AI solutions can drive efficiency, foster innovation, and deliver measurable growth for your business.
Ready to Get Started?
Book Your Free Consultation.
Let's Discuss Your AI Strategy!
Lets Discuss Your Needs
Select a Date
Sun
Mon
Tue
Wed
Thu
Fri
Sat