Exploring regional differences and evolution of science-driven green development in Chinese agriculture
Picture two farmers in China: one in the coastal east, using drones and sensors to apply precise amounts of organic fertilizer, and another in the western interior, still relying heavily on chemical pesticides that gradually seep into the soil and water systems. This contrast represents one of the most pressing challenges in modern agriculture—the uneven adoption of green technologies across regions. As China feeds nearly 20% of the world's population with less than 9% of its arable land, the tension between agricultural output and environmental sustainability has never been more critical 4 .
Eastern regions utilize precision farming with drones, sensors, and AI-driven technologies for optimal resource management.
Western regions often rely on conventional methods with higher chemical inputs and less technological integration.
Recent research reveals a fascinating story of how science and technology innovation is driving agricultural transformation across China's diverse landscapes. A groundbreaking 2025 study examining data from 30 Chinese provinces between 2013 and 2022 demonstrates that while innovation is indeed propelling agriculture toward greener practices, its effectiveness varies dramatically from region to region 1 .
Before delving into the regional differences, it's essential to understand what researchers mean by "green development efficiency" in agriculture. This concept moves beyond simple yield measurements to encompass how effectively agricultural systems can produce food while minimizing environmental harm and resource use. It represents the balance between economic output and ecological preservation 1 4 .
Balance between agricultural output and environmental preservation
Agricultural Science, Technology and Innovation as drivers of change
Varying agricultural systems across China's regions
| Region | Technology Infrastructure | Policy Support | Resource Allocation | Institutional Coordination |
|---|---|---|---|---|
| Eastern | Strong | Well-coordinated | Efficient | Highly integrated |
| Central | Moderate | Moderately coordinated | Fragmented | Partial integration |
| Western | Developing | Weaker institutional support | Limited & fragmented | Less coordinated |
How do researchers actually measure something as complex as "green development efficiency" across an entire country? The 2025 study employed sophisticated analytical techniques that break new ground in agricultural economics 1 .
The research team constructed an evaluation model and indicator system specifically designed to capture the multifaceted nature of innovation-driven green development.
Advanced statistical method measuring efficiency without problematic assumptions about data shape 1 .
Quantified disparities between and within regions to understand distribution patterns.
Visualized distribution and evolution of efficiency scores over time.
Tracked how efficiency patterns evolve and influence neighboring areas 1 .
This comprehensive approach allowed researchers to move beyond simple snapshots to dynamic analyses that reveal how green efficiency trends develop across China's agricultural landscape.
The findings reveal a China divided in its journey toward agricultural sustainability. The data shows a clear regional imbalance, with eastern China demonstrating significantly higher green development efficiency compared to central and western regions 1 .
| Region | Average Efficiency Score | Primary Strengths | Main Challenges |
|---|---|---|---|
| Eastern | 0.825 | Strong innovation investment, effective policy coordination | Maintaining momentum, addressing sub-regional variations |
| Central | 0.720 | Moderate infrastructure, growing policy attention | Resource fragmentation, slower technology adoption |
| Western | 0.666 | Emerging opportunities, increasing policy focus | Limited infrastructure, institutional coordination gaps |
The kernel density curves showed a clear trend of multi-polarization—meaning that instead of regions converging toward similar efficiency levels, they were clustering into distinct high-performing and low-performing groups 1 . This polarization followed observable patterns of spatial agglomeration effects and green infrastructure investments.
One of the most intriguing findings from the research concerns what scientists call "path dependence"—the phenomenon whereby a region's historical development trajectory constrains its future options 1 . The spatial Markov transformation analysis highlighted how regions with initially low green efficiency tended to remain stuck in that pattern, while high-performing regions maintained their advantage 1 .
This path dependency creates what amounts to development traps for some regions. A province with historically resource-intensive agricultural practices, limited R&D infrastructure, and weaker policy support finds it difficult to break free from these patterns, even when new technologies become available.
Compounding this challenge is the powerful role of "neighborhood effects." The research demonstrated that a province's green development efficiency is significantly influenced by what's happening in neighboring regions 1 .
These findings fundamentally challenge the notion that geographical distance doesn't matter in our hyper-connected world. Instead, they highlight the enduring importance of physical proximity in the adoption and implementation of agricultural innovations.
The compelling evidence of regional disparities naturally leads to the question: What can be done? The research points to several promising pathways for creating a more balanced and nationally effective green agricultural transformation.
Tailored approaches for eastern (cutting-edge innovation), central (infrastructure development), and western (foundational investments) regions.
Leveraging neighborhood effects by creating catalysts for broader transformation through targeted support.
Strengthening mechanisms for sharing best practices and facilitating partnerships between regions.
| Innovation Category | Specific Technologies | Primary Function | Regional Applicability |
|---|---|---|---|
| Precision Agriculture | AI-driven monitoring, drones, sensors | Optimize resource use, reduce waste | All regions, with varying complexity |
| Biotechnology | Drought-resistant crops, biofertilizers | Enhance resilience, reduce chemical inputs | Particularly valuable in challenging environments |
| Carbon Utilization | Biochar, soil carbon sequestration | Capture atmospheric carbon, improve soil | Regions with carbon market access |
| Circular Systems | Agricultural waste recycling, green hydrogen | Create value from waste products | Regions with processing infrastructure |
The regional differences in science and technology innovation-driven green development efficiency in Chinese agriculture tell a story of both challenge and opportunity. The stark disparities between east, central, and western regions highlight the persistent inequalities that can emerge even in a nationwide transformation. Yet the dynamic evolution of these patterns also reveals the potential for change and convergence.
What makes this research particularly significant is its demonstration that technology alone is insufficient. The most advanced green innovations will inevitably have limited impact without the proper institutional support, resource allocation, and policy coordination.
As China continues its journey toward agricultural sustainability, the insights from this research offer valuable lessons for other developing countries facing similar challenges of regional disparity and environmental pressure.
With thoughtful policies that acknowledge both the power of innovation and the persistence of geographical constraints, the future may see a more balanced distribution of green agricultural practices across China's diverse landscape—benefiting not just Chinese farmers and consumers, but contributing to global sustainable development goals through more resilient food systems and reduced environmental impact.