Machine Learning Predicts CO2 Emissions and Economic Growth: Implications for Sustainable Policies in Oceania

Introduction

Balancing climate change and economic growth is a global challenge, especially for vulnerable island and coastal economies. A study published in *Scientific Reports* uses machine learning models to predict the relationship between CO2 emissions, GDP, and agricultural land in Indian Ocean Rim Association (IORA) countries. Its methodology and insights offer valuable references for the sustainable development pathways of Oceania, particularly Pacific Island nations.

The study focuses on four IORA member states: Malaysia, Mauritius, Sri Lanka, and Madagascar. These countries, like Pacific Island nations, face climate risks such as sea-level rise and extreme weather, and their economies depend on agriculture, tourism, and resource exports. The proposed XOS-ELM-GA model can more accurately predict economic-environmental coupling trends, providing data support for policymakers.

Background: The Link from IORA to Oceania

IORA comprises 23 member states, including island and coastal nations along the Indian Ocean. Their development challenges share many similarities with those of Pacific Islands Forum (PIF) members: limited land resources, vulnerability to climate change, and the urgent need to balance economic growth with environmental protection.

Traditionally, CO2 emissions have been positively correlated with GDP growth, while agricultural land changes are influenced by both. Pacific Island nations such as Fiji, Papua New Guinea, and Samoa face conflicts between shrinking agricultural land and tourism-related carbon emissions. The historical data (1960–2020) and analytical framework used in this study can be directly applied to similar research in the Pacific region.

In-Depth Analysis: Regional Economic Impacts

Potential Decoupling of Economic Growth and Carbon Emissions

The study shows that machine learning models can capture the nonlinear relationship between CO2 emissions and GDP. For Oceania economies, this suggests a potential for “decoupling”—achieving economic growth while reducing carbon emission intensity. For example, New Zealand has set a 2050 net-zero emissions target, while Australia remains heavily dependent on fossil fuel exports. This model could provide simulation tools for developing differentiated pathways for both countries.

Agricultural Land Prediction and Management

Agriculture is the economic backbone of many Pacific Island nations. The study’s predictions for agricultural land (achieving an average SMAPE of around 3% for Sri Lanka and Madagascar) indicate that the model can assess the impact of climate change on arable land. Pacific Island nations like Tonga and the Solomon Islands, whose agriculture is vulnerable to hurricanes and sea-level rise, could use such predictions to adjust land use policies in advance.

Policy Implications: Localizing SDGs

The study emphasizes that its findings contribute to achieving UN Sustainable Development Goals (SDGs) 8 (economic growth), 13 (climate action), and 15 (life on land). For Oceania, this model can directly support the Pacific sustainable development roadmap (e.g., the “Blue Pacific” initiative), helping countries quantify environmental costs in tourism, fisheries, and renewable energy investments.## Regional Comparison: Australia, New Zealand, and Pacific Island Countries

• Australia: As the largest economy and carbon emitter in Oceania, its economic growth is strongly correlated with mining and LNG exports. The model can be used to predict short-term fluctuations in GDP under emission reduction policies, thereby optimizing the transformation pathway.
• New Zealand: Agriculture (dairy, meat) is the main source of emissions, while tourism relies on aviation carbon emissions. This research can help New Zealand find a balance between agricultural land use and carbon emissions.
• Pacific Island Countries: These countries have small GDPs but are highly ecologically vulnerable. The model's predictions for agricultural land are particularly critical, as land is both a survival resource and a carbon sink space. For example, changes in sugarcane planting area in Fiji conflict with tourism development; the model can assist in planning.

Long-term Trends and Future Outlook

Over the next 3-5 years, with the enrichment of artificial intelligence and remote sensing data, similar models will be more frequently applied in the Oceania region. It is expected that by 2030, Pacific Island countries may formulate quantitative targets for their Nationally Determined Contributions (NDCs) based on such analyses. In addition, the XOS-ELM-GA algorithm adopted in this study can be extended to predict returns on investment in renewable energy, such as assessing the contribution of solar and wind power projects to GDP.

In the long term (5-10 years), machine learning-driven predictions will be integrated into the framework of regional trade cooperation in Oceania. For example, in climate financing between Australia and Pacific Island countries, projects could be required to use such models to assess environmental and economic benefits. Meanwhile, New Zealand is expected to be the first to incorporate prediction results into adjustment parameters of the Carbon Border Adjustment Mechanism (CBAM) for agricultural exports.

Conclusion

This study provides a reliable economic-environmental prediction tool for IORA countries, and its methodology is fully applicable to Oceania. For Australia and New Zealand, the model can support the quantification of decarbonization policies; for Pacific Island countries, it directly serves agricultural land protection and climate adaptation planning. The Oceania Economic Review believes that this cross-regional research reveals that data-driven modeling is a key bridge connecting economic growth and sustainable development. Countries in Oceania should actively adopt similar tools and promote them in Asia-Pacific economic cooperation.

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*Source: Nature Scientific Reports, DOI: 10.1038/s41598-026-51807-1*