Empirical analysis of the carbon-economy nexus in industrial parks using an integrated decoupling-decomposition framework from a major industrial region – Nature

Report on the Carbon-Economy Nexus in Yangtze River Delta Industrial Parks

A Study on Decoupling and Decomposition in Alignment with Sustainable Development Goals

1.0 Introduction

In the context of the global commitment to the 2030 Agenda for Sustainable Development, particularly SDG 13 (Climate Action), the imperative for low-carbon development has become a central tenet of national policy. As a key signatory, China has integrated ambitious carbon peaking and neutrality goals into its national strategy. The industrial sector, a primary engine for SDG 8 (Decent Work and Economic Growth) and SDG 9 (Industry, Innovation, and Infrastructure), is also the largest contributor to national carbon emissions. Industrial parks, as concentrated hubs of economic activity, are critical arenas for this transition, accounting for a significant portion of China’s total CO₂ emissions.

This report presents an empirical analysis of 44 industrial parks in the Yangtze River Delta, a region vital to China’s economic landscape. It aims to bridge a critical research gap by examining the drivers of carbon emissions at the park level. By analyzing the relationship between carbon emissions and economic development, this study provides actionable insights for aligning industrial growth with sustainability targets, thereby supporting the achievement of a synergistic relationship between economic prosperity (SDG 8), sustainable industrialization (SDG 9), and climate resilience (SDG 13).

2.0 Research Design and Methodology

This study employs an integrated analytical framework to provide a comprehensive assessment of the carbon-economy nexus in the selected industrial parks from 2014 to 2023. The methodology is structured to evaluate progress towards key Sustainable Development Goals.

2.1 Study Area and Data

• Area: 44 industrial parks within the Yangtze River Delta, one of China’s most economically dynamic regions. The sustainable transformation of these parks is pivotal for regional and national progress towards the SDGs.
• Period: 2014 to 2023.
• Data Sources: Annual statistical reports from each industrial park, urban statistical yearbooks, and government reports, covering energy consumption, industrial value, population, and land area.

2.2 Methodological Framework

A three-step analytical process was constructed to create a logical chain from evaluation to decomposition:

1. Economic Development Evaluation (TOPSIS Model): A multi-dimensional economic development index was constructed and quantified using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) model. This moves beyond simple economic output to assess the quality of growth, reflecting the principles of SDG 8.
2. Decoupling Status Analysis (Tapio Model): The Tapio decoupling model was used to analyze the dynamic relationship between carbon emissions and the comprehensive economic development index. This directly measures progress towards a core target of SDG 8 and SDG 12 (Responsible Consumption and Production): decoupling economic growth from environmental degradation.
3. Driving Factor Decomposition (LMDI Model): The Logarithmic Mean Divisia Index (LMDI) method was employed to decompose changes in carbon emissions into five key factors. This identifies critical leverage points for policy interventions aimed at achieving SDG 7 (Affordable and Clean Energy), SDG 9, and SDG 13.

2.3 LMDI Decomposition Factors

The factors analyzed to understand their contribution to carbon emissions were:

• Energy Structure Effect: Carbon emissions per unit of energy consumption.
• Energy Efficiency Effect: Energy consumption per unit of industrial output, a key metric for SDG 7.
• Industrial Development Effect: Per capita industrial added value, linking economic activity (SDG 8) to emissions.
• Population Density Effect: Population per unit of industrial land, relevant to SDG 11 (Sustainable Cities and Communities).
• Industrial Land Scale Effect: The total area of industrial land, reflecting the spatial footprint of industrialization.

3.0 Results and Analysis

3.1 Temporal Evolution of Carbon Emissions

From 2014 to 2023, carbon emissions in the studied industrial parks demonstrated a fluctuating growth pattern, increasing from 113.79 to 136.19 million tons. The average annual growth rate of 2.1% was below the national average, indicating preliminary success in the low-carbon transition. A notable 1.25% reduction in 2023 signals that accelerated policy implementation in pursuit of SDG 13 is yielding tangible results. However, the overall upward trend underscores the persistent challenge of reconciling industrial growth with decarbonization goals.

3.2 Decoupling of Carbon Emissions from Economic Development

The analysis of the relationship between economic growth and environmental impact reveals a positive, albeit incomplete, transition towards sustainability.

• Predominant State (2014-2022): The relationship was primarily characterized by “weak decoupling,” where economic growth occurred at a faster rate than the growth in carbon emissions. This indicates a partial alignment with the objectives of SDG 8 and SDG 12.
• Recent Progress (2023): A state of “strong decoupling” was achieved for the first time in 2023, where economic growth was accompanied by an absolute reduction in carbon emissions. This marks a significant milestone, demonstrating that it is possible to advance SDG 8 and SDG 9 without compromising SDG 13.

3.3 Analysis of Carbon Emission Driving Factors

The LMDI decomposition identified the primary factors influencing carbon emission trends, offering critical insights for targeted climate action.

• Primary Drivers of Emission Growth:
  • Per Capita Industrial Development Level: This factor was the single largest contributor to increased emissions, highlighting that the current model of economic growth remains carbon-intensive.
  • Industrial Land Scale Effect: The expansion of industrial land was the second-largest driver, underscoring the negative environmental impact of a development model reliant on spatial expansion rather than intensification. This poses a challenge to SDG 11 and SDG 12.
• Primary Driver of Emission Reduction:
  • Energy Efficiency: Consistent improvements in energy efficiency were the most significant factor suppressing emissions. This demonstrates the effectiveness of technological upgrades and policies promoting SDG 7 and SDG 9.
• Variable Factor:
  • Energy Structure: The contribution of the energy structure fluctuated, turning negative in later years. This reflects a gradual but inconsistent shift towards cleaner energy sources, indicating that efforts under SDG 7 must be sustained and accelerated.

4.0 Discussion and Policy Recommendations

The findings indicate that while the Yangtze River Delta’s industrial parks are on a positive trajectory towards sustainable development, the transition is not yet complete. The achievement of strong decoupling in 2023 is a promising sign, but the continued dominance of industrial scale as a driver of emissions highlights a fundamental tension between current growth models and long-term climate goals. To solidify progress and accelerate the transition, policies must be strategically aligned with the Sustainable Development Goals.

4.1 Policy Recommendations

1. Promote Intensive and Carbon-Efficient Land Use: In response to the significant impact of the industrial land scale effect, a shift towards a “land intensification” model is crucial. This supports SDG 11 and SDG 12 by establishing carbon efficiency-oriented land management systems, including setting carbon emission constraints per unit of land and linking land allocation to carbon reduction performance.
2. Accelerate High-Value, Low-Carbon Industrial Upgrading: Given that per capita industrial development is the primary emissions driver, optimizing the industrial structure is paramount for achieving SDG 9. Policies should focus on attracting and cultivating low-carbon, high-value-added industries, promoting circular economy principles within parks, and fostering industrial symbiosis to reduce waste and emissions across the value chain.
3. Strengthen and Systematize Low-Carbon Policies: To maintain and build upon the “strong decoupling” status achieved in 2023, policy interventions must be consistent and long-term. This requires moving from fragmented initiatives to a systematic framework that reinforces the synergy between economic growth (SDG 8) and climate action (SDG 13), ensuring that technological gains in energy efficiency (SDG 7) are not offset by unsustainable expansion.

5.0 Conclusion

This report confirms that industrial parks in the Yangtze River Delta have made measurable progress in their low-carbon transition, culminating in a strong decoupling of economic growth from carbon emissions in 2023. However, this progress is challenged by a development model still heavily reliant on industrial and spatial expansion. The primary drivers of emissions remain the per capita level of industrial development and the scale of industrial land, while improvements in energy efficiency serve as the main counteracting force.

The integrated application of the TOPSIS, Tapio, and LMDI models provides a robust and universally applicable framework for analyzing the carbon-economy nexus in industrial parks. The findings offer a clear directive for policymakers: to secure a sustainable future, industrial parks must transition from a model of extensive growth to one of intensive, high-quality development. By focusing on land-use efficiency, industrial upgrading, and coherent policy, these economic engines can be transformed into exemplars of sustainable development, driving progress on SDG 8, SDG 9, SDG 11, SDG 12, and SDG 13 simultaneously.

Analysis of Sustainable Development Goals (SDGs) in the Article

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

1. SDG 8: Decent Work and Economic Growth

   The article directly addresses the relationship between economic development and environmental impact, which is a core theme of SDG 8. It analyzes the economic growth of 44 industrial parks in the Yangtze River Delta and uses the Tapio decoupling model to assess whether this growth can be sustained without a corresponding increase in carbon emissions. The study’s focus on achieving “strong decoupling,” where economic growth occurs alongside a decrease in emissions, aligns with the goal of promoting sustainable economic growth.

2. SDG 9: Industry, Innovation, and Infrastructure

   This goal is central to the article, as its subject is “industrial parks as critical carriers of China’s industrial development.” The research explores pathways for the “low-carbon transformation” of these industrial hubs. It discusses industrial efficiency, upgrading industrial structures, improving energy efficiency through technological upgrades, and implementing policies to make industries more sustainable, all of which are key components of building resilient and sustainable infrastructure and industry.

3. SDG 11: Sustainable Cities and Communities

   The article’s focus on industrial parks, which are integral parts of urban and regional landscapes, connects to SDG 11. The analysis includes factors like “industrial land scale effects” and “population density,” which are critical elements of urban planning and sustainable community development. By proposing strategies to reduce the carbon footprint of these industrial zones, the study contributes to making human settlements more inclusive, safe, resilient, and sustainable.

4. SDG 12: Responsible Consumption and Production

   SDG 12 is addressed through the article’s emphasis on decoupling economic activity from resource use and environmental degradation. The study analyzes “resource consumption and pollutant emissions” in industrial parks and identifies “energy efficiency” as a key factor in reducing carbon emissions. The policy recommendation to build a “circular economy system in the park” and promote the “utilization of waste resources” directly supports the goal of achieving sustainable consumption and production patterns.

5. SDG 13: Climate Action

   This is the most prominent SDG in the article. The entire study is framed around the challenge of carbon emissions and the need for climate action. It explicitly mentions China’s national goals of “carbon peaking and neutrality” and aims to provide “actionable insights for industrial park decarbonization.” The analysis of carbon emission drivers and the development of targeted emission reduction strategies are direct contributions to the urgent action required to combat climate change and its impacts.

Specific Targets and Indicators

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

• Target 8.4: Improve resource efficiency and decouple economic growth from environmental degradation.

  The article is fundamentally about this target. The use of the “Tapio decoupling model” is a direct attempt to measure the relationship between economic development and carbon emissions. The finding that the industrial parks transitioned from “weak decoupling” to “strong decoupling by 2023” shows a clear effort to sever the link between economic growth and environmental harm, as stipulated by this target.

• Target 9.4: Upgrade infrastructure and retrofit industries for sustainability.

  The study’s analysis supports this target by identifying drivers for a low-carbon transition in industry. It highlights that “energy efficiency improvements are the core driver of emission reduction” and points to the benefits of “technological upgrades on energy intensity.” The policy recommendation to “optimize the industrial structure from the source, promote the high-end and low-carbon transformation of the industrial structure” is a direct strategy for retrofitting industries to make them more sustainable and efficient.

• Target 12.2: Achieve the sustainable management and efficient use of natural resources.

  This target is reflected in the article’s focus on energy as a key natural resource. The LMDI decomposition method analyzes “energy consumption per unit industrial output” (energy efficiency) and “carbon emission per unit energy consumption” (energy structure). By identifying these as critical factors, the study provides a basis for policies aimed at more efficient resource use within the industrial sector.

• Target 13.2: Integrate climate change measures into national policies, strategies and planning.

  The article explicitly states that China “integrates carbon peaking and neutrality into its strategic framework.” The research itself is designed to provide a “scientific foundation for targeted emission reduction strategies” and “actionable insights for industrial park decarbonization,” which can then be integrated into regional and local planning. The discussion on how policy interventions influenced the trajectory of emissions underscores the importance of this target.

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 mentions and uses several specific indicators to measure progress:

• Total CO2 Emissions

  This is the primary indicator used to track the environmental impact of the industrial parks. The article quantifies it in “million tons” and analyzes its “fluctuating growth trajectory” from 2014 to 2023, providing a direct measure of climate impact.

• Decoupling Elasticity Coefficient

  Calculated using the Tapio decoupling model, this indicator measures the relationship between the rate of change in carbon emissions and the rate of change in economic development. It is used to classify the relationship into states like “weak decoupling” or “strong decoupling,” directly measuring progress towards Target 8.4.

• Energy Efficiency (EY)

  Used in the LMDI decomposition, this is defined as “energy consumption per unit industrial output.” The article identifies it as a consistent “adverse inhibitory effect” on emissions, making it a key performance indicator for industrial sustainability and resource efficiency (Targets 9.4 and 12.2).

• Energy Structure (CE)

  Another factor from the LMDI analysis, defined as “carbon emission per unit energy consumption.” This indicator reflects the carbon intensity of the energy mix (e.g., reliance on fossil fuels vs. renewables). Its fluctuation in the article reflects the “gradual optimization of the regional energy structure.”

• Per Capita Industrial Development Level (YP) and Industrial Land Scale (L)

  These are identified as the “dominant factors driving carbon emissions.” Measuring “Per capita industrial added value” and “industrial land area” serves as an indicator of how economic and spatial expansion contribute to emissions, highlighting areas for policy intervention related to sustainable industrialization (Target 9.4) and land use (Target 11).

Summary Table of Findings

4. Create a table with three columns titled ‘SDGs, Targets and Indicators” to present the findings from analyzing the article.

Source: nature.com