Study on the spatiotemporal evolution and convergence of value conversion efficiency of forest ecological products: a case study of the southern collective forest area – Nature

Executive Summary

This report analyzes the value conversion efficiency of forest ecological products in China’s southern collective forest region from 2012 to 2021. Enhancing this efficiency is a critical strategy for achieving multiple Sustainable Development Goals (SDGs), including SDG 8 (Decent Work and Economic Growth), SDG 10 (Reduced Inequalities), SDG 13 (Climate Action), and SDG 15 (Life on Land). Using a super-EBM model that incorporates undesirable environmental outputs, the study finds an overall upward trend in efficiency, albeit with fluctuations. Significant regional disparities persist, with coastal provinces demonstrating higher efficiency than inland provinces, highlighting a challenge for SDG 10. However, convergence analysis confirms that these regional gaps are narrowing over time, indicating progress towards more equitable development. The findings underscore the need for targeted policies, including regional cooperation (SDG 17), marketization of ecological products (SDG 12), and differentiated ecological compensation, to accelerate sustainable development and ensure the harmonious coexistence of humanity and nature.

Introduction: Aligning Forest Management with Sustainable Development Goals

The global pursuit of the Sustainable Development Goals (SDGs) necessitates balancing economic growth with environmental protection. Forest ecosystems are central to this agenda, providing critical services that support SDG 15 (Life on Land) and SDG 13 (Climate Action). The efficiency with which the value of these forest ecological products is converted into tangible economic and social benefits is a key indicator of progress towards sustainable development. This process directly impacts SDG 8 (Decent Work and Economic Growth) by creating green jobs and industries, SDG 1 (No Poverty) by alleviating “green poverty” in resource-rich rural areas, and SDG 10 (Reduced Inequalities) by ensuring equitable development between regions.

This study assesses the value conversion efficiency of forest ecological products, defined as the effectiveness of transforming forest-based ecosystem services into realized economic and social outcomes. By analyzing this efficiency, the report provides a framework for measuring how well policies and market mechanisms translate natural capital into sustainable prosperity, moving beyond static valuations of ecosystem services to a dynamic assessment of governance performance.

Methodology for Assessing SDG-Aligned Efficiency

Study Area and its Relevance to SDGs

The study focuses on China’s southern collective forest area, a region vital for the nation’s ecological security and a key pilot zone for reforms aimed at sustainable resource management. This area’s rich forest resources and diverse ecosystems make it a critical testing ground for policies that advance the SDGs. Innovations in this region, such as forest carbon trading (advancing SDG 13) and eco-tourism integrated with poverty alleviation (advancing SDG 1 and SDG 8), offer scalable models for sustainable development. The analysis of this region provides crucial insights into realizing the economic potential of ecosystems without compromising their integrity, a core tenet of the 2030 Agenda for Sustainable Development.

Analytical Framework

To measure efficiency in a manner consistent with the principles of sustainable development, this study employs several analytical methods:

• Super-EBM Model: This model was selected for its ability to assess efficiency while accounting for undesirable outputs, such as pollution. This aligns with SDG 12 (Responsible Consumption and Production) by ensuring that economic gains are not achieved at the expense of environmental quality.
• Kernel Density Estimation: This method is used to analyze the dynamic evolution of efficiency levels over time, illustrating shifts towards more sustainable and efficient practices across the region.
• Dagum Gini Coefficient: This tool decomposes regional inequality, providing a detailed understanding of the disparities between and within coastal and inland areas, which is essential for crafting policies to advance SDG 10.
• Convergence Model (σ and β): These tests determine whether less efficient regions are catching up to more efficient ones, offering a quantitative measure of progress towards the goal of reducing inequalities (SDG 10).

Indicator Selection for Sustainable Value Conversion

The selection of indicators reflects a holistic approach to sustainable development, integrating economic, social, and ecological dimensions.

1. Input Indicators:
   • Labor, Land, and Capital: Traditional economic inputs essential for production.
   • Forest Ecological Product Values: Includes supply, regulation, support, and cultural services, representing the natural capital input that underpins sustainable economies (SDG 15).
2. Output Indicators:
   • Expected Output (Total Forestry Output Value): Measures the economic benefits derived from forest resources, contributing to SDG 8.
   • Unexpected Output (Industrial Pollution): Accounts for the negative environmental externalities of production, ensuring the analysis aligns with SDG 12 and the broader goal of green growth.

Analysis of Spatiotemporal Efficiency and Regional Disparities

Temporal Evolution of Value Conversion Efficiency (2012-2021)

The value conversion efficiency in the southern collective forest area evolved through three distinct stages, reflecting a dynamic interplay between policy, technology, and economic conditions in the pursuit of sustainable development:

• Growth Stage (2012–2013): Efficiency gains were primarily driven by technological progress and supportive policies for ecological restoration, demonstrating an initial alignment of development with environmental goals.
• Fluctuation Stage (2014–2019): Efficiency fluctuated due to economic pressures and policy adjustments, highlighting the challenges of maintaining momentum in the transition to a green economy.
• New Development Stage (2020–2021): A rebound in efficiency was driven by innovations in digital and intelligent forestry management, showcasing the potential of technology (SDG 9) to accelerate progress on environmental and economic goals.

Spatial Distribution and Regional Disparities

A significant spatial disparity in efficiency exists, posing a challenge to achieving SDG 10 (Reduced Inequalities). Coastal provinces like Fujian and Guangxi consistently outperform inland provinces such as Jiangxi and Guizhou. This gap is attributable to several factors:

• Economic and Policy Support: Coastal regions benefit from stronger economic foundations, greater investment in green technologies, and more advanced market mechanisms like carbon trading and eco-tourism, which directly support SDG 8 and SDG 13.
• Infrastructure and Technology: Advanced infrastructure in coastal areas facilitates the efficient production and circulation of ecological products, while inland areas face challenges in technology adoption and market access.

The Dagum Gini coefficient analysis confirmed that these inter-regional differences are the primary source of overall inequality, underscoring the need for policies that promote balanced regional development.

Dynamic Evolution and Convergence Towards Equitable Development

Despite existing disparities, the analysis reveals a positive trend towards convergence, indicating that efforts to promote equitable and sustainable development are yielding results. Both σ-convergence and β-convergence tests confirm that the efficiency gap between regions is narrowing over time. Inland areas, benefiting from targeted policy support and their rich resource endowments, are demonstrating a faster “catch-up” growth rate. This convergence is a crucial step towards achieving SDG 10, ensuring that the benefits of sustainable forest management are shared more equitably across the entire region.

Conclusion and Policy Recommendations for Achieving the SDGs

Key Findings

This report confirms that the value conversion efficiency of forest ecological products in China’s southern collective forest area has improved, contributing to national and global sustainability objectives. However, significant regional disparities persist, hindering the full realization of the SDGs. The observed convergence trend is promising, but requires reinforced policy action to accelerate balanced and sustainable development. The findings provide a clear mandate for targeted interventions that leverage technology, strengthen market mechanisms, and foster inter-regional partnerships.

Policy Recommendations

To enhance value conversion efficiency and advance the Sustainable Development Goals, the following policy actions are recommended:

1. Strengthen Policy and Technology Support in Inland Areas: To address regional imbalances (SDG 10), increase financial investment and promote the adoption of advanced forestry technologies in inland provinces. This will help unlock their green economic potential, contributing to poverty reduction (SDG 1) and sustainable economic growth (SDG 8).
2. Enhance Regional Coordination and Cooperation: Foster partnerships (SDG 17) between coastal and inland regions to create a complementary development model. Coastal areas can share technology and market access, while inland areas provide ecological services, creating a mutually beneficial system that promotes balanced regional development.
3. Accelerate the Marketization of Forest Ecological Products: Develop robust markets for ecological products and services, including carbon credits and payment for ecosystem services (PES) schemes. This will create economic incentives for conservation and promote responsible consumption and production patterns (SDG 12), while driving green growth (SDG 8).
4. Develop Differentiated Ecological Compensation Standards: Implement a dynamic and differentiated compensation system that reflects the specific ecological value of different forest ecosystems. This will provide targeted support for the conservation of critical natural habitats (SDG 15) and ensure that communities are fairly compensated for their stewardship.
5. Promote Digital Transformation in Value Chains: Leverage digital finance and technologies like blockchain to enhance transparency, reduce financing barriers for rural producers, and improve the efficiency of forest product value chains. This application of innovation (SDG 9) can significantly advance progress on multiple SDGs, including SDG 1, SDG 8, and SDG 10.

Analysis of Sustainable Development Goals (SDGs) in the Article

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

• SDG 8: Decent Work and Economic Growth

  The article directly connects to SDG 8 by focusing on enhancing the “value conversion efficiency of forest ecological products,” which integrates economic, social, and ecological benefits. It aims to transform natural assets (“lucid waters and lush mountains”) into economic wealth (“golden mountains and silver mountains”), thereby promoting sustainable economic growth. The study analyzes how to improve the efficiency of the forestry industry, which contributes to economic productivity and helps alleviate issues like “green poverty.”

• SDG 10: Reduced Inequalities

  The article extensively analyzes regional disparities in the value conversion efficiency between coastal provinces (Fujian, Guangxi) and inland provinces (Jiangxi, Guizhou). It uses the Dagum Gini coefficient to measure and decompose these inequalities, finding that “inter-regional differences between coastal and inland areas were the primary source of overall inequality.” The policy recommendations aim to bridge these gaps and foster “balanced regional development,” directly addressing the goal of reducing inequalities within a country.

• SDG 12: Responsible Consumption and Production

  This goal is addressed through the article’s core concept of “efficiency.” The study employs a model that includes “unexpected outputs” such as “forestry exhaust, solid waste and wastewater” to measure the true efficiency of the forestry sector. By aiming to improve resource utilization and minimize pollution, the research promotes sustainable management and efficient use of natural resources, a key aspect of SDG 12.

• SDG 13: Climate Action

  The article implicitly connects to SDG 13 by highlighting the critical role of forest ecosystems in providing regulatory services, specifically mentioning their “high carbon sequestration capacity” and “climate regulation value.” Policy examples like Fujian Province’s pilot projects for “forest carbon trading” are direct mechanisms for integrating climate action into economic strategies, which is central to SDG 13.

• SDG 15: Life on Land

  SDG 15 is the most central goal discussed. The entire article revolves around the sustainable management of forest ecosystems. It seeks to “promote harmonious coexistence between humanity and nature” by finding ways to realize the economic value of forest ecological products without degrading the ecosystem. The study evaluates the value of services like “water conservation,” “soil conservation,” and “biodiversity,” all of which are critical components of protecting and restoring terrestrial ecosystems as outlined in SDG 15.

• SDG 17: Partnerships for the Goals

  The article concludes with policy recommendations that emphasize the importance of partnerships. It calls for “regional coordination and cooperation” and the establishment of “cross-regional collaboration mechanisms” to leverage the complementary strengths of coastal and inland areas. This aligns with the spirit of SDG 17, which promotes partnerships to achieve sustainable development goals.

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

1. SDG 8: Decent Work and Economic Growth
   • Target 8.4: Improve progressively, through 2030, global resource efficiency in consumption and production and endeavour to decouple economic growth from environmental degradation. The study’s central metric, “value conversion efficiency,” which balances economic output against resource inputs and pollution, is a direct application of this target.
   • Target 8.9: By 2030, devise and implement policies to promote sustainable tourism that creates jobs and promotes local culture and products. The article mentions Guangxi’s promotion of “eco-tourism” as a successful strategy for realizing the market value of ecological products.
2. SDG 10: Reduced Inequalities
   • Target 10.2: By 2030, empower and promote the social, economic and political inclusion of all, irrespective of age, sex, disability, race, ethnicity, origin, religion or economic or other status. The article’s focus on reducing the development gap between inland and coastal regions and alleviating “green poverty” supports the principle of economic inclusion for populations in lagging regions.
3. SDG 12: Responsible Consumption and Production
   • Target 12.2: By 2030, achieve the sustainable management and efficient use of natural resources. The research is fundamentally about optimizing the use of forest resources by measuring and improving the efficiency of converting their ecological value into economic benefits.
   • Target 12.4: By 2020, achieve the environmentally sound management of chemicals and all wastes throughout their life cycle… and significantly reduce their release to air, water and soil in order to minimize their adverse impacts on human health and the environment. The inclusion of “industrial SO₂ emissions, industrial solid waste generation and industrial wastewater discharges” as “unexpected outputs” in the efficiency model directly addresses this target.
4. SDG 13: Climate Action
   • Target 13.2: Integrate climate change measures into national policies, strategies and planning. The article highlights the “value of carbon sequestration and oxygen release” and mentions “forest carbon trading” as a policy mechanism, demonstrating the integration of climate mitigation into economic and forestry planning.
5. SDG 15: Life on Land
   • 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 entire study is a framework for promoting the sustainable management of forests by making it economically viable.
   • Target 15.9: By 2020, integrate ecosystem and biodiversity values into national and local planning, development processes, poverty reduction strategies and accounts. The article’s methodology, which calculates the “value of forest ecological products” (including biodiversity and ecosystem services) and incorporates it into an efficiency analysis, is a direct implementation of this target.
6. SDG 17: Partnerships for the Goals
   • Target 17.17: Encourage and promote effective public, public-private and civil society partnerships, building on the experience and resourcing strategies of partnerships. The recommendation to establish “cross-regional collaboration mechanisms” between coastal and inland provinces to share technology, market access, and resources is a clear call for such partnerships.

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

• Value Conversion Efficiency of Forest Ecological Products (ETFP): This is the primary composite indicator developed and analyzed in the study. It measures how efficiently forest ecological values are converted into economic and social outcomes, serving as a direct measure for Target 8.4 and Target 12.2.
• Total Forestry Output Value: Mentioned as the “expected output” in the efficiency calculation. This is a key economic indicator for measuring the economic productivity of the forestry sector (relevant to SDG 8).
• Industrial Pollution Indicators: The article explicitly mentions using “industrial SO₂ emissions, industrial solid waste generation and industrial wastewater discharges” as “unexpected outputs.” These are direct indicators for measuring progress on Target 12.4.
• Value of Forest Ecological Products: This is calculated based on services like “carbon sequestration and oxygen release, water conservation value, sedimentation reduction value and climate regulation value.” This monetary valuation is an indicator for Target 15.9, reflecting the integration of ecosystem values into economic analysis.
• Forest Land Area / Forest Cover: Used as a key input indicator in the efficiency model and for calculating the total value of ecological products. It is a fundamental indicator for monitoring progress on SDG 15.
• Dagum Gini Coefficient: This is used to measure the “overall inequality” in value conversion efficiency between and within regions. It serves as a specific statistical indicator for monitoring progress on SDG 10.
• Per Capita GDP: Used as an explanatory variable to represent the “Economic Development Level.” It is a standard indicator for tracking economic growth under SDG 8.

4. Summary Table of SDGs, Targets, and Indicators

Source: nature.com