Spatiotemporal evolution regional differences and decoupling effects of greenhouse gas emissions from animal husbandry in Henan Province – Nature

Report on Greenhouse Gas Emissions from the Livestock Industry in Henan Province: A Sustainable Development Goals Perspective

1.0 Introduction and Alignment with Sustainable Development Goals (SDGs)

In the context of escalating global warming, this report examines greenhouse gas (GHG) emissions from the livestock industry in Henan Province, China, from 2001 to 2021. The livestock sector is a significant contributor to GHG emissions, posing a direct challenge to the achievement of SDG 13 (Climate Action). As a major agricultural province, Henan’s efforts to transition towards a low-carbon livestock industry are critical for national climate targets and offer valuable insights for global sustainable practices. This analysis systematically investigates the spatiotemporal dynamics of these emissions, their regional characteristics, and their relationship with economic development, framing the findings within the broader context of the Sustainable Development Goals. The study underscores the intricate balance required between ensuring food security (SDG 2: Zero Hunger), promoting economic growth (SDG 8: Decent Work and Economic Growth), and fostering sustainable production patterns (SDG 12: Responsible Consumption and Production).

2.0 Methodology for Assessing Sustainable Livestock Production

To provide a comprehensive assessment, this study employed a multi-faceted analytical approach consistent with monitoring sustainable production systems under SDG 12.

• Life Cycle Assessment (LCA): GHG emissions were estimated across the entire livestock value chain, including feed cultivation, transportation, processing, animal digestion, manure management, and product processing. This holistic method provides a detailed environmental footprint, crucial for identifying key intervention points for climate action (SDG 13).
• Theil Index: This method was used to quantify and decompose regional disparities in GHG emissions and economic development, highlighting challenges related to equitable growth and environmental responsibility.
• Tapio Decoupling Model: The relationship between GHG emissions and economic growth in the livestock sector was analyzed to determine if the province is successfully decoupling environmental pressures from economic activity, a core target of SDG 8 and SDG 12.
• Markov Chains: Both traditional and spatial Markov chain models were utilized to analyze the dynamic evolution of emission levels over time, revealing patterns of path dependence and the influence of neighboring regions on a city’s transition towards higher or lower emission states.

3.0 Analysis of Greenhouse Gas Emissions (2001-2021)

3.1 Temporal and Spatial Emission Trends

The analysis reveals a fluctuating but overall downward trend in total GHG emissions from Henan’s livestock industry over the two-decade period. This trend aligns with provincial and national efforts to meet climate targets under SDG 13.

1. Overall Trend: Total emissions decreased, indicating progress in mitigating the sector’s climate impact. Two periods of significant decline were identified: 2001-2007 and 2015-2021. The latter period’s reduction was largely driven by policy-led industrial restructuring, such as reducing cattle inventory, reflecting a strategic move towards more sustainable production (SDG 12).
2. Spatial Distribution: A distinct spatial pattern emerged, with a primary GHG emission belt forming from the northwest to the southeast of the province. This geographic concentration highlights the need for targeted regional policies to ensure that climate action is both effective and equitable.

3.2 Regional Disparities and Economic Linkages

The Theil index analysis confirmed the presence of significant regional differences in emission levels, with inter-regional disparities being the primary source of overall variation. While the emissions gap between regions has been narrowing, the economic development gap has shown a widening trend. This divergence underscores the challenge of achieving balanced regional development that integrates economic prosperity (SDG 8) with environmental sustainability (SDG 13).

4.0 Decoupling Economic Growth from Environmental Impact

The relationship between economic growth and GHG emissions in Henan’s livestock sector has evolved, demonstrating a positive shift towards sustainability. This is a critical indicator for achieving SDG 8, which calls for decoupling economic growth from environmental degradation.

• The overall decoupling state has transitioned from predominantly weak decoupling to strong decoupling. This signifies that Henan Province is increasingly able to expand its livestock economy while simultaneously reducing its GHG emissions.
• This trend indicates that policies promoting environmental protection and economic development are becoming more coordinated, moving the province closer to the sustainable production models envisioned in SDG 12.
• However, the persistence of weak decoupling in some regions and instances of negative decoupling highlight the instability of this relationship and the ongoing need for robust policy enforcement to maintain progress towards SDG 13.

5.0 Dynamic Evolution and Spatial Dependencies of Emissions

The Markov chain analysis revealed important insights into the dynamics of regional emission changes, which are crucial for designing effective, long-term climate strategies.

• Path Dependence: Regional GHG emission levels exhibit strong inertia, meaning a region’s current emission type is a strong predictor of its future state. This suggests that breaking out of high-emission pathways requires significant and sustained intervention.
• Spatial Influence: The transition of a region’s emission type is significantly influenced by the emission levels of its neighbors. Proximity to high-emission regions increases the probability of a region transitioning to a higher emission state, while proximity to low-emission regions has a positive spillover effect. This finding emphasizes the importance of collaborative, inter-regional strategies to achieve widespread success under SDG 13 and SDG 17 (Partnerships for the Goals).

6.0 Conclusions and Policy Recommendations for Sustainable Livestock Management

This report concludes that Henan Province has made measurable progress in reducing GHG emissions from its livestock industry, contributing to SDG 13 (Climate Action). However, significant regional disparities and the risk of emission rebounds persist. To accelerate the transition to a fully sustainable livestock sector, the following policy actions are recommended:

1. Promote Integrated and Efficient Production Systems: To advance SDG 2 and SDG 12, policies should focus on optimizing feed composition to reduce methane from enteric fermentation and promoting circular economy models that integrate crop and livestock farming. This includes utilizing manure as organic fertilizer and crop residues as animal feed, reducing waste and emissions across the food system.
2. Implement Differentiated Regional Management Strategies: Acknowledging regional imbalances, differentiated policies are needed. High-emission regions should be supported with incentives and compensation mechanisms for emission reduction. Low-emission regions should be developed as models of sustainable livestock farming, fostering innovation and technology transfer. This approach supports equitable economic growth (SDG 8) while advancing climate goals (SDG 13).
3. Strengthen Environmental Governance and Supervision: To ensure sustained progress, it is essential to strengthen government environmental regulations and establish a robust GHG emissions monitoring system. Clear, legally binding reduction targets for the livestock industry should be set, and a transparent supervision framework should be implemented to hold stakeholders accountable and inform adaptive policymaking, in line with the principles of strong institutions (SDG 16).

Analysis of Sustainable Development Goals in the Article

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

The article on greenhouse gas (GHG) emissions from the livestock industry in Henan Province addresses several Sustainable Development Goals (SDGs). The analysis connects environmental impacts, economic development, and policy-making, which are central themes of the SDGs.

• SDG 13: Climate Action

  This is the most prominent SDG in the article. The entire study is centered on investigating, quantifying, and analyzing GHG emissions, which are the primary drivers of global warming and climate change. The article explicitly mentions “global warming,” “GHG emissions,” and China’s “‘dual-carbon’ targets,” directly aligning with the goal of taking urgent action to combat climate change and its impacts.

• SDG 12: Responsible Consumption and Production

  The article analyzes the environmental impact of the livestock industry, a major production sector. It employs the Life Cycle Assessment (LCA) method, which evaluates the environmental footprint across the entire production chain, from feed cultivation to product processing. The discussion on manure management, resource utilization, and the “combination of planting and rearing” to form a circular model directly relates to ensuring sustainable consumption and production patterns.

• SDG 8: Decent Work and Economic Growth

  The study explicitly examines the relationship between economic development and environmental impact through the Tapio decoupling model. It analyzes whether economic growth in Henan’s livestock industry can be achieved without a corresponding increase in GHG emissions. The finding that the decoupling state is moving towards “weak and strong decoupling” indicates an effort to “promote sustained, inclusive and sustainable economic growth,” a core tenet of SDG 8.

• SDG 2: Zero Hunger

  While not the main focus, the article is rooted in the context of the livestock industry, a critical component of agriculture and food production systems. The policy recommendations for optimizing feed composition and promoting integrated livestock and crop production aim to make this food source more sustainable, which is essential for long-term food security and achieving sustainable agriculture (Target 2.4).

• SDG 9: Industry, Innovation, and Infrastructure

  The article implicitly addresses this goal by highlighting the need for technological and structural improvements in the livestock industry. The discussion on optimizing the spatial layout, improving feed efficiency, and implementing modern manure management systems points towards the need to “build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation.”

• SDG 11: Sustainable Cities and Communities

  The study’s focus on regional and city-level analysis within Henan Province, using tools like the Theil index and Markov chains to understand spatial disparities and interactions, connects to sustainable regional planning. The recommendation to “formulate differentiated emission reduction policies and optimise the spatial layout of the livestock industry” aligns with making human settlements and regions inclusive, safe, resilient, and sustainable.

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

Based on the article’s detailed analysis, several specific SDG targets can be identified:

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

   The article directly references China’s national strategy to incorporate climate change by setting “‘dual-carbon’ targets.” At the provincial level, it mentions that Henan has introduced “a series of emission reduction policies” and refers to the “’14th Five-Year Plan for National Economic and Social Development of Henan Province'” and the “‘Work Arrangements for Energy Conservation, Emission Reduction and Carbon Reduction in Henan Province in 2016’.” This demonstrates the integration of climate action into policy and planning.

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

   This target is explicitly addressed through the use of the “Tapio decoupling model.” The article’s analysis aims to determine the decoupling relationship between “GHG emissions and economic growth in the livestock sector.” The conclusion that the overall state is characterized by “weak and strong decoupling” and that “environmental protection and economic development… are gradually moving toward coordination” is a direct measurement of progress towards this target.

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

   The article discusses methods to improve resource efficiency within the livestock industry. The policy recommendation to form a “circular mode of ‘breeding-planting-breeding'” by using “livestock and poultry excrement… into organic fertiliser” and “straw produced by planting as feed” is a clear example of promoting the sustainable management and efficient use of natural resources.

4. Target 2.4: By 2030, ensure sustainable food production systems and implement resilient agricultural practices that increase productivity and production, that help maintain ecosystems…

   The study’s overall goal is to provide a basis for “formulating differentiated low-carbon development policies for the livestock industry.” Recommendations such as “optimising feed nutrient composition” and promoting “integrated livestock and crop production” are strategies to create more sustainable and resilient agricultural systems that reduce environmental harm (GHG emissions) while maintaining production.

5. 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 analysis of emissions from different parts of the life cycle (e.g., manure management systems, energy consumption) and the policy proposal to “strengthen comprehensive management of CH₄ and N₂O emissions” imply a need to upgrade industrial processes. The shift from traditional small-scale farming to modern, large-scale operations with better manure management, as discussed in the article, reflects an effort to retrofit the industry to be more sustainable.

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 implies several quantitative and qualitative indicators that can be used to measure progress towards the identified SDG targets.

• Total GHG Emissions from the Livestock Industry (in CO2-eq)

  This is the primary indicator used throughout the study. The article quantifies total emissions over time, stating they “decreased significantly from 4829.90 ten thousand tons in 2001 to 3805.48 ten thousand tons in 2021.” This directly measures progress on climate action (SDG 13).

• GHG Emissions by Source

  The article breaks down emissions into specific sources using the LCA method, including “gastrointestinal fermentation,” “manure management system,” “energy consumption,” and “feed grain planting.” Tracking these individual components allows for targeted policy-making and measures progress in specific areas of sustainable production (SDG 12).

• Tapio Decoupling Index

  This is a specific, calculated indicator used in the article to measure the relationship between economic growth and environmental pressure. The article classifies the decoupling status into eight types (e.g., “weak decoupling,” “strong decoupling”) and tracks how this status changes over time. It serves as a direct indicator for Target 8.4.

• Theil Index

  The article uses the Theil index to “analyse regional differences and spatial decomposition” of GHG emissions. It states the index “decreased from 0.1064 to 0.0978,” indicating that “regional disparity in emissions between regions has gradually narrowed.” This can serve as an indicator for measuring progress towards balanced regional development and reducing inequalities (relevant to SDG 10 and SDG 11).

• Markov Chain Transition Probability

  The study calculates the probability of a region’s GHG emission level transitioning to another level over time. The finding that the “self-locking probability… is significantly higher than the transition probability” is an indicator of path dependence and the stability of emission patterns. This can be used to assess the effectiveness of policies aimed at transforming industrial structures (SDG 9).

• GHG Emission Intensity (Implied)

  Although not explicitly calculated as a standalone figure, the concept of GHG emission intensity (emissions per unit of economic output) is the foundation of the decoupling analysis. The entire discussion on decoupling economic growth from emissions is a measure of this intensity, making it an implied indicator for sustainable economic growth (SDG 8) and production (SDG 12).

4. Table of SDGs, Targets, and Indicators

Source: nature.com