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Abstract Promoting greenhouse gas (GHG) emission reduction within China’s food system through a multi-objective synergistic approach is crucial for China to achieve carbon neutrality targets and sustainable development goals. Climate change has become one of the major challenges facing human society, requiring coordinated global actions to mitigate its impacts. To effectively address climate change and play an active role in the global actions of GHG reduction, China pledged in 2020 to achieve carbon neutrality by 2060, and incorporated this commitment into its Nationally Determined Contributions (NDCs). In this context, the agricultural food system, as both an emission source and a carbon sink, plays a crucial role in achieving carbon neutrality.
The Chinese agricultural food system contributes to GHG emissions through land-use changes, agricultural production, input manufacturing, and post-production activities such as food processing, packaging, transportation, and consumption. It is the second-largest source of anthropogenic GHG emissions, surpassed only by fossil fuel combustion. Moreover, the food system also substantially impacts social and economic development goals, including rural development, food security, sustainable agricultural development, and public nutritional health. Therefore, it is critical to thoroughly explore the emission reduction effects of policy measures and their roles in achieving multiple objectives related to health, economic, and environmental outcomes.
This study systematically reviews the development of research methodologies in GHG emission reduction and agricultural food system transformation, in China and globally, with a particular focus on land use, agricultural production, and food consumption. We identify the contributions and limitations of integrated assessment models (IAMs), econometric approaches, bottom-up research based on micro-level data, and machine learning approaches. These tools enable researchers to evaluate the complex interactions between agricultural practices, environmental outcomes, and socioeconomic factors across various spatial and temporal scales.
Based on existing research, we outline future research agenda for GHG emission reduction and sustainable transformation of China’s food system. While the existing literature provides valuable insights into GHG emissions accounting, emission reduction strategy evaluation, and food system transformation pathways, most of the research mainly focuses on a single objectives or measures. To address this, a more comprehensive research paradigm is proposed. First, given that accuracy in estimating key parameters of IAMs is often constrained by data limitations, future studies can leverage micro-level, large-sample data, machine learning, and econometric methods to optimize model parameterization, thereby supporting policy-making. Second, life-cycle estimation for food system GHG emissions should be prioritized, enabling a more comprehensive assessment of emission reduction potential. Third, integrating interdisciplinary data and coupling different model systems are essential to building multi-objective optimization frameworks. By combining agroeconomic models, vegetation and crop models, health risk models, and climate models, comprehensive assessments can be conducted to reveal the health, economic, and environmental impacts of GHG emission reduction policies in the agricultural food systems and to identify synergies with sustainable development goals. Fourth, future research should delve into spatial and demographic heterogeneity in agricultural emissions and mitigation potential, with particular attention to underdeveloped regions, key agricultural products, and vulnerable populations. This focus could make policy-making more targeted and inclusive. Finally, by building scenario simulation and transition pathway databases, training data can be provided for artificial intelligence (AI) algorithms. This would facilitate the development of AI models tailored to China’s food system, significantly improving decision-making efficiency and supporting major policy decisions.
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Published: 06 March 2025
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