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<p>Several countries around the world subsidize electrical tariffs. Mexico’s residential tariff structure is among the most complex and subsidized, depending on blocks of consumption and local temperature. In 2016 the residential electricity subsidy was 100&#xa0;billion Mexican Pesos. To understand the increases in residential electricity consumption at the municipal level in Mexico in a climate change context, we used a machine learning approach based on gradient boosted trees to test how electricity consumption is related to population, GDP, temperature, and precipitation, while controlling for month of the year and for the country’s different states. We evaluated the impact of consumption increases, coupled with the future behavior of the current tariff policy, on the future increase of the total subsidy in real terms. The subsidy would grow to 132.4&#xa0;billion MXN in real terms by 2030, and 203.6&#xa0;billion MXN in 2070 under a business as usual scenario; climate change makes the subsidy rise by 2.2% by 2030 and 5.3% by 2070. Projected increases in temperature should be incorporated explicitly in the planning of the National Electric System, as the subsidy would continue to grow in real terms. This situation is worsened by the current tariff structure based on the average temperature of the locality. A tariff change proposal is discussed; it maintains the current structure while ameliorating the distributional incidence, with a tariff increase of 18% for the lowest income decile and 80% for the highest income bracket, and reducing future subsidy increases under climate change by 50% by 2070.</p>

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Impacts of climate change on electrical subsidies’ public policies: the case of Mexico

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月19日

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  • Rodrigo Muñoz Sánchez,
  • Julián A. Velasco,
  • Francisco Estrada Porrúa,
  • Oscar Calderón Bustamante

摘要

Several countries around the world subsidize electrical tariffs. Mexico’s residential tariff structure is among the most complex and subsidized, depending on blocks of consumption and local temperature. In 2016 the residential electricity subsidy was 100 billion Mexican Pesos. To understand the increases in residential electricity consumption at the municipal level in Mexico in a climate change context, we used a machine learning approach based on gradient boosted trees to test how electricity consumption is related to population, GDP, temperature, and precipitation, while controlling for month of the year and for the country’s different states. We evaluated the impact of consumption increases, coupled with the future behavior of the current tariff policy, on the future increase of the total subsidy in real terms. The subsidy would grow to 132.4 billion MXN in real terms by 2030, and 203.6 billion MXN in 2070 under a business as usual scenario; climate change makes the subsidy rise by 2.2% by 2030 and 5.3% by 2070. Projected increases in temperature should be incorporated explicitly in the planning of the National Electric System, as the subsidy would continue to grow in real terms. This situation is worsened by the current tariff structure based on the average temperature of the locality. A tariff change proposal is discussed; it maintains the current structure while ameliorating the distributional incidence, with a tariff increase of 18% for the lowest income decile and 80% for the highest income bracket, and reducing future subsidy increases under climate change by 50% by 2070.