# Distributional Trends in the Generation and End-Use Sector of Low-Carbon Hydrogen Plants

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## Abstract

**:**

## 1. Introduction

## 2. Data

## 3. Distributions of End-Use Sector Over Time

## 4. Usage Distributions

## 5. Trends in Capacity over Time and Relative to Technology and End-Use Sector

## 6. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## Appendix A. Probability Distribution Distance

**Proposition**

**A1.**

**Proof.**

## References

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**Figure 1.**Cumulative distribution functions ${F}_{S}$ for eight sectors S, (

**a**) refining (

**b**) ammonia (

**c**) synfuels (

**d**) methanol (

**e**) mobility (

**f**) domestic heat (

**g**) CHP (

**h**) power. Sectors are described in Section 2. The greatest collective similarity is observed between industrial applications, with an explosion of planned plants in the 2020’s. Power serves as an anomaly with its highly uniform trend of new plants.

**Figure 2.**Hierarchical clustering on cumulative distribution functions ${F}_{S}$ (relative to time) for all 14 end-use sectors S in our database. A strong cluster of similarity is observed for the seven industrial uses, ranging from biofuels to ammonia. A secondary cluster of more ‘consumer uses’ is revealed from grid injection to domestic heat, whereas power is observed as an outlier due to its highly uniform nature.

**Figure 3.**Distribution dendrograms between continental/technological groups G, produced by hierarchical clustering on the distance (2) for (

**a**) the entire period of analysis (

**b**) 2000—2009 (

**c**) 2010–2019 (

**d**) 2020–2029 (

**e**) 2030–2039. There are only 12 groups G as there are no fossil plants in Africa or Latin America.

**Figure 4.**Stacked bar plots showing distribution of end-use sectors for (

**a**) the entire period of analysis (

**b**) 2000–2009 (

**c**) 2010–2019 (

**d**) 2020–2029 (

**e**) 2030–2039. There are only 12 groups G as there are no fossil plants in Africa or Latin America. Empty bars indicate no plants in that group over that period.

**Figure 5.**Log capacity vs. year of construction for all plants in our dataset with available data. We classify plants by both their technology as well as their usage, using the clusters of end-use sectors from Section 3 Figure 2. We can see the early dominance of blue plants (with a few early exceptions) by several orders of magnitude, but this is closing with time.

**Table 1.**Adjusted ${R}^{2}$, a measure of goodness of fit for eight different linear regression models between (log) capacity, grouped or ungrouped sectors, and technology. A far better fit is observed for the exponential models.

Adjusted ${\mathit{R}}^{2}$ | No Separation by Tech | Stratified by Tech | ||
---|---|---|---|---|

Grouped Sectors | All Sectors | Grouped Sectors | All Sectors | |

Capacity | 0.0255 | 0.0204 | 0.0273 | 0.0226 |

Log capacity | 0.569 | 0.587 | 0.607 | 0.622 |

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**MDPI and ACS Style**

James, N.; Menzies, M. Distributional Trends in the Generation and End-Use Sector of Low-Carbon Hydrogen Plants. *Hydrogen* **2023**, *4*, 174-189.
https://doi.org/10.3390/hydrogen4010012

**AMA Style**

James N, Menzies M. Distributional Trends in the Generation and End-Use Sector of Low-Carbon Hydrogen Plants. *Hydrogen*. 2023; 4(1):174-189.
https://doi.org/10.3390/hydrogen4010012

**Chicago/Turabian Style**

James, Nick, and Max Menzies. 2023. "Distributional Trends in the Generation and End-Use Sector of Low-Carbon Hydrogen Plants" *Hydrogen* 4, no. 1: 174-189.
https://doi.org/10.3390/hydrogen4010012