Ecological–environmental transformation efficiency in China: regional disparities, modeling challenges, and prospects for long-term sustainability governance – Nature

Report on Ecological-Environmental Transformation Efficiency (EETE) in China: A Sustainable Development Goals Perspective

Executive Summary

In the context of increasing global pressure to achieve the Sustainable Development Goals (SDGs), particularly those balancing economic growth (SDG 8) with environmental sustainability (SDG 13, SDG 15), this report presents a comprehensive framework, Ecological Sustainability Trajectory in Dynamic Evolution (ECO-STRIDE), to evaluate and forecast regional Ecological-Environmental Transformation Efficiency (EETE) in China. The framework, applied to panel data from 30 provinces (2010–2023), integrates dynamic efficiency modeling, spatial-temporal analysis, and deep learning prediction to provide insights crucial for SDG monitoring and implementation.

1. Overall EETE Performance: EETE remains low, with significant regional disparities, indicating uneven progress towards integrated environmental and economic SDGs. Resource utilization efficiency, aligned with SDG 12 (Responsible Consumption and Production), generally surpasses environmental governance efficiency, which is central to SDG 6 (Clean Water and Sanitation) and SDG 11 (Sustainable Cities and Communities).
2. Spatial-Temporal Dynamics: While efficiency is improving, regional gaps are widening, posing a challenge to the principle of ‘leaving no one behind’. The eastern region leads in both resource utilization and environmental governance, demonstrating a stronger capacity to advance multiple SDGs simultaneously.
3. Future Projections (to 2035): A hybrid CNN-LSTM-Attention model projects continued EETE improvement, suggesting positive momentum towards the 2030 Agenda. However, persistent spatial imbalances highlight the need for targeted interventions to ensure equitable progress on the SDGs.
4. Key Drivers and Constraints: An explanatory analysis identifies urbanization (SDG 11), industrial upgrading (SDG 9), and digital infrastructure (SDG 9) as key drivers. Inefficiencies in green finance allocation (related to SDG 17) and underutilized ecological endowments remain critical constraints, hindering the full realization of goals like SDG 15 (Life on Land). This report provides a robust methodological tool for formulating region-specific strategies that advance long-term, coordinated progress toward the Sustainable Development Goals.

1. Introduction: Aligning Economic Growth with Sustainable Development Goals

The global pursuit of the 2030 Agenda for Sustainable Development necessitates a fundamental rebalancing of economic growth (SDG 8) and environmental protection. Since the Industrial Revolution, rapid economic expansion has often come at the cost of excessive resource consumption and ecosystem degradation, directly challenging the achievement of SDG 12 (Responsible Consumption and Production), SDG 13 (Climate Action), and SDG 15 (Life on Land). International frameworks like the Kunming-Montreal Global Biodiversity Framework aim to address these issues, yet ecological crises persist, underscoring the need for effective regional implementation.

With urbanization accelerating globally—projected to house 70% of the population by 2030—cities have become critical arenas for sustainable development. The associated rise in energy consumption puts immense pressure on ecosystems, making green urban development a cornerstone for achieving SDG 11 (Sustainable Cities and Communities). As the world’s largest developing nation, China’s efforts to build an “ecological civilization” are pivotal. Despite progress in establishing ecological conservation redlines and increasing forest coverage, regional disparities in economic structure and governance capacity create fragmented environmental outcomes. This fragmentation undermines coordinated efforts to meet national and global sustainability targets.

1.1. The Role of EETE in Monitoring SDG Progress

To address these challenges, this report focuses on Ecological-Environmental Transformation Efficiency (EETE) as a key performance indicator for evaluating the synergy between economic development, resource utilization, and environmental protection. EETE provides a quantitative measure of progress towards a green transition, reflecting an integrated assessment of ecological efficiency and sustainable performance. It aligns directly with the core tenets of the SDGs by emphasizing the maximization of economic and social well-being while minimizing resource inputs and environmental harm. By analyzing EETE, this report offers an empirical basis for policies aimed at achieving a range of interconnected goals, including:

• SDG 9: Industry, Innovation, and Infrastructure
• SDG 11: Sustainable Cities and Communities
• SDG 12: Responsible Consumption and Production
• SDG 13: Climate Action

Understanding the dynamic trajectory of EETE is essential for forward-looking governance. It informs green investment structures, industrial regulation, and the design of ecological compensation policies, enabling governments to anticipate risks and allocate resources effectively to accelerate the achievement of the 2030 Agenda.

2. Literature Review: Measuring and Influencing SDG-Aligned Ecological Transformation

EETE serves as a critical indicator for assessing the balance between economic objectives and environmental sustainability, a central theme of the Sustainable Development Goals. Research on EETE has primarily focused on its measurement and the identification of its influencing factors, both of which are essential for evidence-based policymaking aimed at achieving the 2030 Agenda.

2.1. Evolution of EETE Measurement

Methodologies for measuring EETE have evolved to better capture the complexities of sustainable development. Non-parametric models like Data Envelopment Analysis (DEA) are widely used. Early models focused on static efficiency, but subsequent advancements incorporated undesirable outputs (e.g., pollution) and dynamic changes, providing a more realistic assessment of progress toward environmental SDGs. However, mainstream approaches like the Slacks-Based Measure (SBM) model often overlook the inter-temporal dynamics and path dependencies inherent in long-term ecological transitions. This report addresses this gap by employing a two-stage Dynamic Directional Distance Function (DDF) model, which captures the cumulative nature of sustainability efforts and allows for alignment with specific policy objectives, such as those outlined in the SDGs.

2.2. Factors Influencing Regional EETE and SDG Achievement

The literature identifies several key drivers of EETE, each with direct links to specific SDGs.

• Urban Development (SDG 11): Urbanization can enhance EETE through improved infrastructure and economic agglomeration. However, it can also increase ecological pressure. The quality of urbanization is crucial; its synergy with the digital economy and its impact on ecosystem services determine whether it supports or hinders progress on SDG 11 and related environmental goals.
• Green Governance (SDG 13, SDG 16, SDG 17): Green governance mechanisms are fundamental to improving EETE. Green finance (related to SDG 17) directs capital towards environmentally friendly industries, while green innovation supports SDG 9. The effectiveness of policy instruments like carbon trading is often contingent on regional development stages, highlighting the need for tailored governance approaches to achieve SDG 13 (Climate Action) and SDG 16 (Peace, Justice and Strong Institutions).
• Macro Environment (SDG 8): The relationship between economic growth (SDG 8) and EETE is complex. While economic agglomeration can improve resource allocation, its ecological effects may follow nonlinear patterns. Policy stability and institutional quality are key moderating factors that determine whether economic growth is compatible with environmental sustainability.
• Infrastructure (SDG 9): Sustainable infrastructure is a critical enabler of ecological transformation. Green mobility systems and digital networks enhance resource efficiency and strengthen the coupling between urbanization and environmental quality, directly contributing to SDG 9. Efficient land use is another crucial dimension, linking infrastructure development to the preservation of terrestrial ecosystems (SDG 15).
• Ecological Base (SDG 6, SDG 15): The health of the ecological foundation—including water resources, forests, and soil—is a prerequisite for sustainable EETE. Optimizing water allocation (SDG 6), restoring forest systems (SDG 15), and promoting circular economy principles through waste utilization are essential for maintaining the ecosystem services that underpin long-term development.

3. Methodology: An Integrated Framework for SDG Assessment and Forecasting

To provide a comprehensive analysis of regional progress towards integrated environmental and economic SDGs, this report introduces the ECO-STRIDE analytical framework. This framework integrates efficiency evaluation, dynamic evolution analysis, and trend forecasting to enable a full-cycle depiction of EETE.

3.1. ECO-STRIDE Framework Overview

The framework is structured in three layers to link empirical assessment with forward-looking prediction, enhancing its relevance for SDG-focused policymaking.

1. ECO-METRICS Benchmarking Layer: This layer uses a two-stage Dynamic DDF model to measure EETE across two critical dimensions: resource utilization (aligning with SDG 12) and environmental governance (aligning with SDG 11, 13, and 15). This decomposition identifies structural constraints within different subsystems.
2. Spatio-Dynamics Evolutionary Analysis Layer: Using the efficiency scores from the first layer, this stage employs Dagum Gini coefficients and kernel density estimation to analyze the spatial-temporal evolution of EETE. It reveals disparities in transformation trajectories, providing context for understanding uneven SDG progress.
3. DEEP-TRENDS Trajectory and Explainability Layer: This layer applies a CNN-LSTM-Attention deep learning model to forecast future EETE trends up to 2035. The Shapley Additive Explanations (SHAP) method is incorporated to identify key driving variables, ensuring model interpretability and providing actionable insights for policy intervention.

3.2. Forecasting and Driver Analysis Model (FUTURIA)

To anticipate future EETE levels and inform proactive policy design for enhancing regional sustainability, this report utilizes an advanced deep learning architecture. The CNN-LSTM-Attention model is designed to capture complex, nonlinear relationships that traditional econometric models often miss.

• Model Architecture: The model combines a Convolutional Neural Network (CNN) to extract spatial features, a Long Short-Term Memory (LSTM) network to model temporal dependencies, and an Attention mechanism to weigh the importance of different features over time.
• Feature Variables for SDG Context: Input variables were selected across five dimensions that reflect key SDG domains:
  • Urban Development: Urbanization rate, industrial upgrading (SDG 11, SDG 9).
  • Green Governance: Green coverage, environmental regulation, green finance index (SDG 13, SDG 16, SDG 17).
  • Macro Environment: Fiscal support, social consumption level (SDG 8).
  • Infrastructure: Medical institutions, digital penetration (SDG 3, SDG 9).
  • Ecological Base: Water use, forestry area, waste utilization (SDG 6, SDG 15, SDG 12).
• Model Interpretability (SHAP): To move beyond “black-box” predictions, the SHAP method is used to quantify the marginal contribution of each input feature to the predicted EETE outcome. This provides transparent, evidence-based insights into the drivers of sustainable transformation.

Performance testing confirmed the superiority of the CNN-LSTM-Attention model over benchmark models, with an R² of 0.9153, demonstrating its high accuracy and reliability for forecasting progress towards sustainability goals.

4. Analysis of Findings: Regional Performance and Future Trajectories for Sustainable Development

4.1. Efficiency Assessment and Spatial-Temporal Evolution

The analysis of EETE from 2010 to 2023 reveals significant challenges and opportunities for achieving the SDGs in China.

• Overall Performance and Disparities: The national average EETE is low, indicating that the synergy between economic activity (SDG 8) and environmental protection (SDG 13, 15) is yet to be fully realized. Pronounced regional disparities exist, with western provinces like Qinghai showing high efficiency, while central provinces like Henan lag significantly. This highlights uneven progress towards the 2030 Agenda.
• Imbalance Between Sub-stages: Most provinces demonstrate higher efficiency in resource utilization (related to SDG 12) than in environmental governance. This suggests that while economic systems are becoming more resource-efficient, governance capacity to manage pollution and ecosystem health (central to SDG 6, 11, and 15) remains a primary bottleneck. Guangdong Province exemplifies this imbalance, with top-tier resource efficiency but poor environmental governance performance.
• Widening Regional Gaps: While overall efficiency in both resource utilization and environmental governance is trending upward, the gap between regions is widening. Inter-regional differences are the primary driver of inequality in resource utilization, whereas environmental governance shows signs of polarization. The eastern region’s consistent leadership underscores its advanced capacity for integrated SDG implementation.

4.2. Future Projections and Trend Analysis (2024–2035)

The forecasting model projects the trajectory of EETE, offering a glimpse into future SDG attainment possibilities.

1. Sustained Upward Trend: EETE levels across China are projected to improve continuously through 2035. This suggests that current policies and structural shifts are steering the country toward a more sustainable development pathway, in line with the long-term vision of the SDGs.
2. Phased Improvement:
   • 2024–2025: Most regions will remain in the early stages of transformation, with EETE values below 0.5.
   • 2026–2030: A significant improvement is expected, with most regions moving into medium-high efficiency levels (0.5-0.7), indicating that the effects of green policies will become more pronounced.
   • 2031–2035: Further gains are projected, but the number of high-performing regions will remain limited, indicating that spatial imbalances in SDG achievement will persist without targeted interventions.
3. Notable Regional Trajectories: Regions like Ningxia are projected to achieve rapid improvements, becoming models for ecological transition in less-developed areas. Conversely, provinces like Henan are forecast to show limited progress, highlighting persistent structural barriers to achieving sustainable development.

4.3. Key Drivers of EETE: An Explanatory Analysis

The SHAP analysis provides insights into the factors driving or constraining EETE, which are crucial for designing effective policies to accelerate SDG progress.

• Primary Drivers:
  • Urbanization and Industrial Upgrading (SDG 11 & 9): These are core drivers of EETE. Higher urbanization rates and a greater share of the tertiary industry are strongly correlated with improved efficiency, reflecting the benefits of economies of scale, technological diffusion, and a shift towards less resource-intensive economic activities.
  • Digital Infrastructure (SDG 9): The telephone penetration rate, a proxy for digital infrastructure, shows a clear positive impact. This highlights the role of digitalization in enhancing information transparency, resource management, and intelligent environmental regulation.
• Critical Constraints:
  • Green Finance Inefficiency (SDG 17): High levels of green finance do not always translate to higher EETE. In some regions, a negative correlation suggests structural inefficiencies, where funds may be misallocated to low-impact projects. This points to a need for better alignment between financial flows and tangible sustainability outcomes.
  • Underutilized Ecological Endowments (SDG 15): A large forestry land area does not guarantee high EETE. This counterintuitive finding suggests that some resource-rich regions lack the green technology and industrial structures to convert natural capital into sustainable performance, creating a mismatch between ecological assets and development outcomes.
  • Resource-Intensive Development Models: A high industrial solid waste utilization rate is negatively correlated with EETE. This indicates that even with improvements in recycling (a target of SDG 12), regions with a heavy industrial base still carry a significant overall ecological burden, highlighting the need for a systemic shift away from resource-intensive models.

5. Conclusions and Policy Recommendations for Accelerating SDG Progress

5.1. Conclusion

This report, utilizing the integrated ECO-STRIDE framework, provides a comprehensive assessment of China’s regional Ecological-Environmental Transformation Efficiency (EETE) and its alignment with the Sustainable Development Goals. The key conclusions are as follows:

1. Uneven SDG Progress: China’s overall EETE remains low, with significant regional disparities. Efficiency in resource utilization (SDG 12) is outpacing environmental governance (SDG 6, 11, 13, 15), indicating an imbalance in the pursuit of integrated sustainable development.
2. Growing Disparities: While efficiency levels are improving, the gap between regions is widening. This trend poses a challenge to the inclusive principles of the 2030 Agenda and requires targeted policies to support lagging regions.
3. Positive but Inequitable Future Trajectory: Projections to 2035 show a continuous upward trend in EETE, suggesting positive momentum. However, spatial imbalances are expected to persist, necessitating proactive measures to ensure a more equitable and widespread green transition.
4. Identified Drivers and Bottlenecks: Urbanization (SDG 11), industrial upgrading (SDG 9), and digital infrastructure (SDG 9) are confirmed as key drivers of sustainable transformation. Critical bottlenecks include structural inefficiencies in green finance allocation (SDG 17) and the failure to translate rich ecological endowments into high performance (SDG 15), often due to path dependency on resource-intensive industries.

5.2. Policy Recommendations

Based on the findings, the following recommendations are proposed to enhance EETE and accelerate progress towards the SDGs:

• Strengthen Integrated Governance Systems for Balanced Development:
  • For regions with high resource utilization but weak environmental governance (e.g., Guangdong), establish coordinated “resource-governance” mechanisms to improve systemic efficiency in pollution control and ecological restoration, thereby better integrating SDG 8 with SDG 13 and 15.
  • For persistently low-efficiency provinces (e.g., Henan), prioritize building environmental governance capacity through institutional support, technology transfer, and human capital development to create a balanced foundation for sustainable growth.
• Develop a Dynamic SDG Monitoring and Forecasting System:
  • Establish a national EETE monitoring platform using deep learning models to provide early warnings on provincial ecological transition trends. This will support adaptive management and evidence-based policymaking aligned with the 2030 Agenda.
  • Provide targeted policy support for high-potential regions (e.g., Ningxia) to serve as benchmarks for green governance, while developing phased roadmaps with clear performance milestones for slowly improving regions.
• Optimize the Allocation of Green Development Factors:
  • Reform green finance allocation (SDG 17) to prioritize preventive and systemic projects over end-of-pipe treatments. Ensure capital flows support structural transformation in disadvantaged sectors and smaller cities to improve overall impact.
  • Accelerate the development of digital infrastructure for environmental supervision and carbon monitoring to enhance intelligent governance and transparency, directly supporting SDG 9 and SDG 13.
  • For regions with strong ecological foundations but low EETE, promote investment in green technologies and industrial diversification to break the mismatch between natural endowments and sustainable performance, fully leveraging assets to meet SDG 15 targets.

Analysis of Sustainable Development Goals in the Article

1. Which SDGs are addressed or connected to the issues highlighted in the article?

The article addresses several Sustainable Development Goals (SDGs) by focusing on the balance between economic growth and environmental sustainability in China. The core concept of Ecological–Environmental Transformation Efficiency (EETE) connects directly to goals concerning economic development, industrial practices, urban living, consumption patterns, and ecosystem health.

• SDG 8: Decent Work and Economic Growth

  The article’s central theme is the challenge of balancing “economic growth and environmental sustainability.” It analyzes how to achieve economic output while minimizing resource consumption and environmental degradation, which directly relates to the goal of promoting sustainable economic growth.

• SDG 9: Industry, Innovation and Infrastructure

  The study identifies “industrial upgrading” and “digital infrastructure” as key drivers of EETE. This aligns with SDG 9’s emphasis on building resilient infrastructure, promoting inclusive and sustainable industrialization, and fostering innovation.

• SDG 11: Sustainable Cities and Communities

  The article extensively discusses the impact of urbanization, noting that by 2030, “urban energy consumption will account for more than 75 percent of the global total.” It identifies the “urbanization rate” as a primary factor influencing EETE, directly connecting to the goal of making cities and human settlements inclusive, safe, resilient, and sustainable.

• SDG 12: Responsible Consumption and Production

  The concept of EETE is defined as maximizing economic output with “minimal resource consumption.” The analysis of “resource utilization efficiency” and “industrial solid waste utilization” directly addresses the core principles of SDG 12, which aims to ensure sustainable consumption and production patterns.

• SDG 13: Climate Action

  Although not the primary focus, the article’s discussion of environmental governance, pollution control, and the mention of “carbon emission trading” policies are integral to climate action. Improving environmental efficiency is a key strategy for mitigating climate change.

• SDG 15: Life on Land

  The article addresses the degradation of ecosystems and efforts toward conservation. It mentions China’s “ecological conservation redlines,” “national forest coverage rate,” and uses “forestry land area” and the “Normalized Difference Vegetation Index (NDVI)” as indicators, which are all relevant to protecting, restoring, and promoting the sustainable use of terrestrial ecosystems.

2. What specific targets under those SDGs can be identified based on the article’s content?

Based on the issues discussed, several specific SDG targets can be identified:

1. Target 8.4: Improve progressively, through 2030, global resource efficiency in consumption and production and endeavour to decouple economic growth from environmental degradation.

   The entire study is framed around this target. The development of the Ecological–Environmental Transformation Efficiency (EETE) framework is a direct attempt to measure and forecast the efficiency of decoupling economic activity from resource consumption and environmental harm.

2. Target 9.4: By 2030, upgrade infrastructure and retrofit industries to make them sustainable, with increased resource-use efficiency and greater adoption of clean and environmentally sound technologies and industrial processes.

   The article identifies “industrial upgrading,” “green innovation,” and “digital infrastructure” as key drivers for improving EETE, directly reflecting the goals of this target.

3. Target 11.3: By 2030, enhance inclusive and sustainable urbanization and capacity for participatory, integrated and sustainable human settlement planning and management in all countries.

   The analysis of the “urbanization rate” as a top influencing factor on EETE and the discussion of managing the ecological pressures of urban growth connect directly to this target.

4. Target 11.6: By 2030, reduce the adverse per capita environmental impact of cities, including by paying special attention to air quality and municipal and other waste management.

   The article’s focus on “environmental governance,” “pollution control,” and the use of the “industrial solid waste utilization rate” as an indicator align with this target’s objective to manage urban environmental quality.

5. Target 12.2: By 2030, achieve the sustainable management and efficient use of natural resources.

   This target is explicitly addressed through the article’s two-stage evaluation of EETE, which separates “resource utilization efficiency” from “environmental governance efficiency,” providing a detailed assessment of how efficiently natural resources are being used.

6. Target 12.5: By 2030, substantially reduce waste generation through prevention, reduction, recycling and reuse.

   The inclusion of the “industrial solid waste utilization rate” as a key variable in the analysis directly relates to this target’s focus on recycling and reuse to manage waste.

7. Target 15.2: By 2020, promote the implementation of sustainable management of all types of forests, halt deforestation, restore degraded forests and substantially increase afforestation and reforestation globally.

   The article mentions China’s “national forest coverage rate exceeding 25 percent” and uses “forestry land area” as a key indicator in its model, reflecting the importance of forest management for ecological sustainability.

3. Are there any indicators mentioned or implied in the article that can be used to measure progress towards the identified targets?

Yes, the article explicitly develops, uses, and analyzes several quantitative indicators that can measure progress towards the identified targets.

• Ecological–Environmental Transformation Efficiency (EETE): This is the central composite indicator developed in the study to serve as a “quantitative indicator for evaluating the coordination between urban economic development, resource utilization, and environmental protection.” It is a direct measure for Targets 8.4 and 12.2.
• Resource Utilization Efficiency and Environmental Governance Efficiency: These are the two sub-indicators of EETE, allowing for a more granular measurement of progress. Resource utilization efficiency measures the economic output relative to resource inputs (Target 12.2), while environmental governance efficiency measures the effectiveness of pollution control and ecosystem protection (Target 11.6).
• Urbanization Rate: Used as a primary explanatory variable in the predictive model, this indicator measures the pace and scale of urbanization, which is crucial for monitoring Target 11.3.
• Industrial Upgrading: Identified as a key driver, this indicator (measured in the study as the proportion of tertiary industry) tracks the transition to more sustainable industrial structures, relevant to Target 9.4.
• Digital Infrastructure (Telephone Penetration Rate): The article uses this as a proxy for digital infrastructure, which it identifies as a key driver for EETE, linking to the infrastructure aspect of Target 9.4.
• Green Finance Index: This composite indicator is used to assess the role of financial investment in green projects, which is a crucial enabling factor for achieving multiple targets (8.4, 9.4, 11.6).
• Industrial Solid Waste Utilization Rate: This is an explicit indicator used in the analysis that directly measures progress towards Target 12.5 by quantifying the extent of waste recycling.
• Forestry Land Area and National Forest Coverage Rate: These indicators are mentioned and used to assess the ecological base of a region, directly measuring progress related to the sustainable management of forests under Target 15.2.

4. Summary Table of SDGs, Targets, and Indicators

Source: nature.com