欧洲央行-地理与收入_碳税的异质性效应（英）-2025_69页_7mb

Abstract

Carbon taxation has significant distributive effects that must be addressed for its political acceptability. Using French data and a spatial heterogeneous-agent model, this study shows that rural households are disproportionately affected by carbon taxes due to higher fossil fuel consumption and employment in carbon-intensive sectors. A carbon tax increases welfare losses by 20% for rural households compared to urban households, and neglecting geographic factors in revenue recycling reduces welfare gains by 7%. Targeted revenue recycling, combining income and location-based transfers, is more effective at enhancing equity and reducing disparities. The findings highlight the importance of accounting for spatial heterogeneity in carbon policy design, especially for the upcoming EU-ETS2, to improve both environmental and equity objectives.

Non-technical Summary

Carbon taxes are essential tools for reducing greenhouse gas emissions but create uneven costs across households due to geographic differences. Rural households in France spend 2.8 times more fossil fuel than urban households and are more likely to work in high-emissions sectors. This study shows that rural areas experience higher welfare losses than urban areas (e.g., −20% in rural regions vs. −14.5% in Paris) during a carbon tax transition. Ignoring geographic factors in revenue recycling reduces net welfare gains by 7%. Optimal transfer policies targeting both income and location, such as progressive transfers in urban areas and reduced progressivity in rural zones, significantly enhance equity and overall welfare. These spatial distributional effects must be integrated into climate policy frameworks to ensure political feasibility and long-term effectiveness.

Key Findings

1. Distributive Impacts:

• Rural households face a 20% higher welfare loss than urban households due to higher fossil fuel consumption, living space, and carbon-intensive employment.
• Poor households suffer disproportionately higher costs under regressive carbon taxes (household carbon taxes are highly regressive; firm taxes impact middle- and high-income groups primarily through wage and interest rate effects).

2. Role of Migration and Spatial Adjustments:

• Migration gradually smooths distributive effects over time but exacerbates short-term disparities (e.g., welfare costs are 20–30% higher in the short run).
• Ignoring migration flows in policy design overestimates long-term geographic inequalities but underestimates short-term heterogeneity.

3. Optimal Revenue Recycling:

• Transfers combining income and geographic progressivity reduce the welfare loss incidence by 10% compared to income-only transfers and increase median welfare by 7–7.4%.
• Carbon tax revenue allocated to uniform transfers achieves a +7.3% median welfare gain, while targeted transfers yield even stronger results when accounting for spatial heterogeneity.

Policy Recommendations

• Carbon Pricing Strategies: Carbon taxes should incorporate geographic differentiation to minimize welfare loss disparities, particularly for households with limited mobility options.
• Revenue Use: Optimal revenue recycling via transfers combining income and location-based criteria enhances equity and public support.
• Model Integration: Future analyses must bridge spatial econometrics and heterogeneous-agent models to capture dynamic heterogeneity across geographies and income levels.

Implications for EU-ETS2

Given the projected low carbon prices in the EU-ETS2 (€45 per ton), the potential emission reductions (3%) remain modest. However, revenue recycling mechanisms informed by this study could be tailored to align with the policy's long-term goals while mitigating its regressive impacts and improving political feasibility. Effective design of carbon pricing policies requires explicit consideration of spatial distributional effects.