In an ambitious leap forward in sustainability science, a recent study advances our understanding of China’s ecological-environmental transformation efficiency (EETE) through a sophisticated analytical framework, revealing critical regional disparities and forecasting future trajectories over the next decade. Leveraging data spanning 2010 to 2023 across 30 Chinese provinces, the research integrates advanced modeling techniques to dissect multifaceted aspects of ecological transition, emphasizing the interplay between resource utilization and environmental governance. This work not only elucidates the current landscape but also offers prescient insights into the long-term governance challenges that lie ahead, holding profound implications for policymakers and environmental strategists in China and beyond.
At its core, the study employs the ECO-STRIDE framework, a comprehensive methodology intertwining dynamic efficiency modeling with spatial-temporal analysis and intelligent forecasting. Such an innovative approach enables researchers to capture not only the static efficiency metrics but also their evolution over time and geographically nuanced disparities. By coupling this with explainability techniques, specifically the SHAP method, the study identifies pivotal drivers that influence regional performance, illuminating complex interactions between socioeconomic factors and ecological health.
The findings paint a sobering yet instructive picture: despite incremental gains, the aggregate level of EETE across Chinese provinces remains modest, accompanied by stark regional disparities. Intriguingly, resource utilization efficiency generally surpasses the effectiveness of environmental governance across most regions. Provinces such as Qinghai and Hainan stand out with exemplary transition performance, reflecting perhaps more progressive policy environments or ecological endowments. In contrast, industrial powerhouses like Henan and resource-rich Inner Mongolia exhibit persistently low efficiency, pointing toward entrenched structural challenges in harmonizing growth with sustainability objectives. Guangdong offers a distinctive profile characterized by robust resource allocation but lagging governance effectiveness, signaling an urgent need for systemic coordination.
Deeper inspection reveals a dual-trend phenomenon: while overall efficiency in both resource utilization and governance improves, regional disparities concurrently intensify. The resource utilization stage has witnessed consistent enhancement, yet structural differentiation among provinces is accelerating. Environmental governance improvement lags, exhibiting pronounced polarization where high-efficiency regions continue to forge ahead while underperforming provinces widen the gap. The eastern region consistently demonstrates remarkable efficiency advantages in both dimensions, underscoring its advanced industrial framework and capacity to integrate governance and resource management synergistically.
Forecasting the future trajectory of EETE, the research introduces a cutting-edge composite predictive model—melding convolutional neural networks (CNN), long short-term memory (LSTM) networks, and attention mechanisms—crafted to evaluate trends in green governance and urbanization effectually. Demonstrating superior performance with an R² value of 0.9153%, root mean square error (RMSE) of 0.0595%, and mean absolute percentage error (MAPE) of 9.09%, this model outperforms conventional alternatives by significant margins. Projections spanning 2024 to 2035 indicate a steady upward trend in EETE across regions, though persistent disparities are anticipated. Provinces like Ningxia emerge as potential frontrunners in ecological performance improvement, while Henan may experience only marginal progress, reaffirming the uneven pace of transition.
Central to these nuanced dynamics, the SHAP-based feature importance analysis reveals a constellation of determinants that shape provincial EETE outcomes. Urbanization rate and industrial upgrading emerge as positive contributors, enhancing transition efficiency albeit with evident threshold effects, underscoring that benefits accrue differently depending on development stages and regional contexts. The forest land area’s relationship with efficiency is paradoxical: regions abundant in forest resources sometimes achieve lower ecological transition efficiency, suggesting that ecological endowments alone do not guarantee successful environmental governance. Moreover, the study highlights inefficiencies in green finance allocation, a misalignment that suppresses gains by concentrating resources in less impactful projects rather than systemic enhancements. Digital infrastructure, particularly in telecommunications penetration, plays a facilitative role by fostering transparency and regulatory capacity, thereby bolstering ecological outcomes.
The research foregrounds the critical need for tailored governance strategies that acknowledge the heterogeneity of provincial circumstances. For provinces exhibiting strong resource utilization but weak governance systems, like Guangdong, the establishment of coordinated “resource-governance” mechanisms is urged to bridge systemic gaps in pollution control and restoration efforts. Conversely, areas with chronically low efficiency, including Henan and Inner Mongolia, require intensified investment in green institutions, technical innovation, and human capital development to build governance capabilities capable of fostering balanced ecological progress. This differentiation in strategy underscores the complexity of achieving equitable and effective sustainability transformations.
Enhancing the regional monitoring and forecasting apparatus is imperative to support proactive ecological management. The study advocates for developing a dynamic EETE monitoring platform powered by deep learning analytics that integrate heterogeneous datasets encompassing green governance metrics, urban growth indicators, and ecological baselines. Such a system would enable real-time ecological transition early warnings, facilitating adaptive policy interventions tailored to the evolving needs of each province. Regions with high improvement potential like Ningxia should be leveraged as benchmarks to emulate best practices, while provinces exhibiting sluggish advancements necessitate phased, milestone-oriented roadmaps to systematically address governance deficiencies.
Optimizing green investment allocation emerges as a salient prescription. The current orientation of green finance shows structural inefficiencies by disproportionately favoring end-of-pipe solutions or projects with low marginal returns in ecological transition. Redirecting financial flows toward preventive, capacity-enhancing initiatives, particularly in emerging or structurally disadvantaged urban centers, would yield better systemic results. Concurrently, digital infrastructure expansion focused on environmental supervision and carbon monitoring can enhance the granularity and agility of ecological governance through improved data transparency and regulatory oversight.
The study’s insights into the nuanced interplay between ecological endowment and transition efficiency reveal persistent mismatches warranting targeted intervention. Regions with substantial forest coverage that still rank low in EETE illustrate how natural wealth, if not paired with adept governance and green technology adoption, fails to translate into tangible sustainability progress. For such provinces, integrating ecological asset management with industrial upgrading and innovation diffusion is critical to break out of resource-dependency traps and accelerate green transformation.
This research, pioneering in its methodological integration and regional depth, also highlights the broader systemic challenges underpinning ecological transition efforts within China. It demonstrates that achieving long-term sustainability requires harmonizing multifarious drivers—from urbanization to digital infrastructure—while addressing ingrained regional inequalities and structural inefficiencies in financing and governance. The composite model’s success showcases the immense potential of artificial intelligence in environmental policy formulation and monitoring, marking a pivotal moment in harnessing data-driven insights for sustainable futures.
Looking forward, the findings prompt a reevaluation of top-down approaches that treat provincial ecological transitions uniformly. Instead, a differentiated, evidence-based policy framework responsive to local conditions and capacities emerges as paramount. Enhanced data integration, coupled with transparent governance and smart financing strategies, can empower provinces to chart more precise development pathways aligned with ecological imperatives. China’s experience serves as a vital case study for other nations grappling with the complexities of ecological transformation amidst rapid urban and industrial growth.
In sum, the study’s comprehensive evaluation of ecological-environmental transformation efficiency across China not only advances academic understanding but also provides actionable guidance for practitioners and policymakers. By unraveling the multifaceted factors influencing EETE and projecting future scenarios with high precision, it fosters a more nuanced, strategic approach to sustainability governance that is desperately needed in the face of escalating environmental pressures and global climate challenges.
Some provinces display exceptional promise in reconciling economic vitality with ecological stewardship, setting exemplary standards for leveraging green governance innovations. Meanwhile, others underline the enduring obstacles borne of legacy industrial structures, governance deficiencies, and insufficient green financial support. Addressing these divergent realities through tailored, data-driven, and adaptive management strategies represents a critical frontier for achieving China’s ambitious sustainability goals.
Ultimately, this research exemplifies how blending sophisticated quantitative methods with domain expertise and explainability techniques can illuminate pathways toward resilient and equitable ecological transitions. As nations strive to meet the imperatives of climate action and environmental justice, such integrative frameworks will be indispensable in orchestrating effective, regionally attuned sustainability transformations that leave no province behind.
Subject of Research: Ecological-environmental transformation efficiency and sustainability governance across Chinese provinces.
Article Title: Ecological–environmental transformation efficiency in China: regional disparities, modeling challenges, and prospects for long-term sustainability governance.
Article References:
Fang-Rong, R., Tao-Feng, W. & Qing-Qing, Z. Ecological–environmental transformation efficiency in China: regional disparities, modeling challenges, and prospects for long-term sustainability governance. Humanit Soc Sci Commun 12, 1734 (2025). https://doi.org/10.1057/s41599-025-06013-1
Image Credits: AI Generated
