Carbon Emission Forecasting and Economic Analysis

Carbon emission forecasting and economic analysis intertwine quantitative prediction methods with assessment of economic drivers to inform climate policy and investment decisions. Forecasting models range from econometric and scenario-based approaches to advanced machine learning algorithms, each calibrated against historical emissions, energy consumption and socio-economic data. Economic analysis evaluates factors such as carbon intensity, marginal abatement costs and the impact of pricing instruments on emission pathways. Integrating these two strands allows stakeholders to simulate the effects of policy interventions—from carbon pricing and emissions trading to technology subsidies—on future emission trajectories and economic growth. Recent advancements include the coupling of high-resolution databases with dynamic system models to capture sectoral interactions and regional heterogeneity. Practical applications span national carbon budgets, corporate climate risk assessments and infrastructure planning, contributing to transparent, evidence-based strategies aimed at achieving net-zero targets globally.

Research from Nature Portfolio

A recent study developed driving-force models for national CO₂ emissions by decomposing sources into energy industries, industrial processes and fuel combustion. Employing multiple linear regression on data spanning three decades, researchers quantified the relationships between economic output, energy intensity and emission trends, then forecasted carbon intensity and total emissions to 2030. The results indicate a continued decline in emission intensity but highlight the need for strengthened measures to meet international climate commitments. The work underscores the importance of aligning economic growth with low-carbon pathways through targeted policy levers.

Carbon Emission Forecasting and Economic Analysis publication trend

The graph below shows the total number of articles in carbon emission forecasting and economic analysis across all publications each year (not limited to Nature Index journals).

Technical terms

Carbon intensity: Emissions of carbon dioxide per unit of economic output, typically measured relative to GDP.

Scenario analysis: Examination of potential future outcomes by modelling different assumptions about drivers such as energy mix and policy.

Machine learning: Algorithms that enable computers to learn patterns from data and generate forecasts without explicit programming.

Root mean square error (RMSE): Statistical measure of the average magnitude of forecast errors, expressed in the same units as the data.

Mean absolute percentage error (MAPE): Average absolute difference between forecasted and actual values, presented as a percentage.

References

1. Estimation of greenhouse gas emissions using linear and logarithmic models: A scenario-based approach for Turkiye's 2030 vision. Energy Nexus (2024).
2. Analysis of decarbonization path in New York state and forecasting carbon emissions using different machine learning algorithms. Carbon Neutrality (2024).
3. Regression analysis and driving force model building of CO2 emissions in China. Scientific Reports (2021).