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The work of this paper is sponsored by National Key R&D Program of China (Grant No. 2020YFE0200300), Applied Basic Research Project of Sichuan Province (Project No. 2017JY0253) and Fundamental Research Funds for the Central Universities (Project No. 2682020CX28 and 2682020CX36).
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DOI: https://doi /10.1007/s11630-023-1749-3
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Natural gas is a potent greenhouse gas (GHG) with a global warming potential 28 times higher than CO2 over a 100-year timescale13. The leakage rate in the production and transportation system of natural gas is a relevant factor affecting the carbon intensity of blue hydrogen14,15,16. In contrast, hydrogen produced through water electrolysis has a carbon intensity linked to the source of electricity and, to a lesser extent, the manufacturing of the system17. Currently, water electrolysis accounts for only 2% of global hydrogen production5. However, its widespread adoption alongside renewable technologies like solar and wind power could facilitate large-scale production of low-carbon hydrogen.
We estimate the potential demand for hydrogen at both country and sector levels for years 2020 and 2050. Detailed country- and sector-specific results can be found in the Supplementary Dataset. While the hydrogen demand in 2020 is currently met by fossil resources, our 2020 scenario represents a counterfactual situation where all fuel or feedstock inputs to the chemical, cement, refineries and light industry, steel production, and transport sectors are instead provided by electrolytic hydrogen (see Methods subsection "Hydrogen demand").
Figure 1 illustrates the electricity demand necessary for electrolytic hydrogen production across different sectors. The left y-axis represents the per capita capacity required for electrolytic hydrogen production, while the right y-axis compares the per capita capacity for electrolytic hydrogen with the per capita capacity for direct electricity consumption. There is a substantial difference between 2020 and 2050, primarily driven by an 80% reduction in hydrogen demand in refineries, a threefold increase in hydrogen demand in the chemical sector (ammonia and methanol production), and the transport sector transitioning from zero to over 200 Mt/y globally.
Land scarcity induced by hydrogen production in 2050 across countries worldwide, considering various fractions of land coverage for a, c, e solar and b, d, f onshore wind power production. The degree of land scarcity depends on the assumed eligible land coverage (fcoverage) for renewable technologies in each country, reflecting economic and socio-political constraints. Notably, the lower power density of onshore wind leads to a significantly higher number of countries experiencing scarcity. Countries depicted in gray lacked available data. The maps are created with the Matplotlib and Geopandas packages for Python101,102.
Exacerbation of water scarcity resulting from additional water required for hydrogen production in 2050 through water electrolysis, compared to current water withdrawals for agricultural, industrial, and municipal activities. Power production from a solar panels and b onshore wind. The demand for water in hydrogen production alone does not create water scarcity in countries where it is not already present. However, the additional water demand for hydrogen production can exacerbate scarcity in countries already affected by water scarcity. Countries without color in the figure do not experience water scarcity. Gray-colored countries indicate unavailable data. The maps are created with the Matplotlib and Geopandas packages for Python101,102.
Land and water scarcity induced by projected hydrogen production in 2050 for all countries worldwide, considering various fractions of land coverage for a, c, e solar and b, d, f onshore wind power production. The color-coded regions represent the scarcity status: green for no scarcity, red for both land and water scarcity, yellow for land scarcity only, and blue for water scarcity only. Gray-colored countries indicate unavailable data. The maps are created with the Matplotlib and Geopandas packages for Python101,102.
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