$\mathcal{W}_2(\mathbb{R})$ is isometric to a convex subset of a Hilbert space (and embedding a hyperbolic space into Euclidean/Hilbert one has a lot of history).

The argument can find an argument in S.S. Vallender "Calculation of the Wasserstein distance between probability distributions on the line". I will do an informal sketch.

I want to see probability measures in $\mathbb{R}$ as piles of sand with sand grains enumerated from left to right and indexed by $[0,1]$. For a measure $\mu$ define function $\Phi_{\mu}:[0,1]\rightarrow \mathbb{R}$ by $\Phi_\mu(t)$ be a position of $t$-th grain in $\mathbb{R}$. I claim that for every $\mu, \nu$ $$\mathcal{W}_2(\mu,\nu) = ||\Phi(\mu)-\Phi(\nu)||_{L_2}.$$

Before proving this let say how the optimal transport looks on $\mathbb{R}$.

Lemma 1: for the measures $\mu$ and $\nu$ the optimal transport plan between them sends $t$-th sand grain of $\mu$ into $t$-th sand grain of $\nu$.

I give the proof of the lemma for the case when $\mu = \frac{1}{n}(\delta(\mu_1) + \dots + \delta(\mu_n)),$ for some real $\mu_1 < \dots < \mu_n$ and $\nu = \frac{1}{n}(\delta(\nu_1) + \dots + \delta(\nu_n))$, for some real $\nu_1 < \dots < \nu_n$. I need to show that optimal transport moves $\frac{1}{n} \delta(\mu_1)$ into $\frac{1}{n}\delta(\nu_1)$ and then the rest will follow by induction. Okay, lets assume its not and (a part of) $\frac{1}{n} \delta(\mu_1)$ is moved to $\frac{1}{n} \delta(\nu_k)$ and (a part of) $\frac{1}{n}\delta(\mu_j)$ is moved to $\frac{1}{n} \delta(\nu_1)$ for some $j,k > 1$. I claim that if we change our transport by moving (the part of) $\frac{1}{n} \delta(\mu_1)$ into $\frac{1}{n}\delta(\nu_1)$ and (the part of) $\frac{1}{n}\delta(\mu_j)$ into $\frac{1}{n} \delta(\nu_k)$ it will get cheaper. Which follows from the following trivial lemma.

Lemma 2: Suppose that $a,b,c > 0$ then $a^2 + (b + c)^2 < (a + c)^2 + b^2$ iff $b < a$.

Now it's really easy to proof our main statement $$\mathcal{W}_2(\mu,\nu) = ||\Phi(\mu)-\Phi(\nu)||_{L_2}.$$
for the case when $\mu = \frac{1}{n}(\delta(\mu_1) + \dots + \delta(\mu_n)),$ for some real $\mu_1 < \dots < \mu_n$ and $\nu = \frac{1}{n}(\delta(\nu_1) + \dots + \delta(\nu_n))$, for some real $\nu_1 < \dots < \nu_n$. Indeed, both sides are equal to $$\frac{1}{n}\sum_{i=1}^{n}|\mu_i - \nu_i|^2.$$

PS: note that measures of the type $\mu = \frac{1}{n}(\delta(\mu_1) + \dots + \delta(\mu_n)),$ for some real $\mu_1 < \dots < \mu_n$ are dense in Wasserstein space see Proposition 2.10 from Karl-Theodor Sturm. "On the geometry of metric measure spaces.". So all the above can be formalized with some suffering.

$\mathcal{W}_2(\mathbb{R})$ is isometric to a convex subset of a Hilbert space (and embedding a hyperbolic space into Euclidean/Hilbert one has a lot of history).

The argument can find an argument in S.S. Vallender "Calculation of the Wasserstein distance between probability distributions on the line". I will do an informal sketch.

I want to see probability measures in $\mathbb{R}$ as piles of sand with sand grains enumerated from left to right and indexed by $[0,1]$. For a measure $\mu$ define function $\Phi_{\mu}:[0,1]\rightarrow \mathbb{R}$ by $\Phi_\mu(t)$ be a position of $t$-th grain in $\mathbb{R}$. I claim that for every $\mu, \nu$ $$\mathcal{W}_2(\mu,\nu) = ||\Phi(\mu)-\Phi(\nu)||_{L_2}.$$

Before proving this let say how the optimal transport looks on $\mathbb{R}$.

Lemma 1: for the measures $\mu$ and $\nu$ the optimal transport plan between them sends $t$-th sand grain of $\mu$ into $t$-th sand grain of $\nu$.

I give the proof of the lemma for the case when $\mu = \frac{1}{n}(\delta(\mu_1) + \dots + \delta(\mu_n)),$ for some real $\mu_1 < \dots < \mu_n$ and $\nu = \frac{1}{n}(\delta(\nu_1) + \dots + \delta(\nu_n))$, for some real $\nu_1 < \dots < \nu_n$. I need to show that optimal transport moves $\frac{1}{n} \delta(\mu_1)$ into $\frac{1}{n}\delta(\nu_1)$ and then the rest will follow by induction. Okay, lets assume its not and (a part of) $\frac{1}{n} \delta(\mu_1)$ is moved to $\frac{1}{n} \delta(\nu_k)$ and (a part of) $\frac{1}{n}\delta(\mu_j)$ is moved to $\frac{1}{n} \delta(\nu_1)$ for some $j,k > 1$. I claim that if we change our transport by moving (the part of) $\frac{1}{n} \delta(\mu_1)$ into $\frac{1}{n}\delta(\nu_1)$ and (the part of) $\frac{1}{n}\delta(\mu_j)$ into $\frac{1}{n} \delta(\nu_k)$ it will get cheaper. Which follows from the following trivial lemma.

Lemma 2: Suppose that $a,b,c > 0$ then $a^2 + (b + c)^2 < (a + c)^2 + b^2$ iff $b < a$.

Now it's really easy to proof our main statement $$\mathcal{W}_2(\mu,\nu) = ||\Phi(\mu)-\Phi(\nu)||_{L_2}.$$
for the case when $\mu = \frac{1}{n}(\delta(\mu_1) + \dots + \delta(\mu_n)),$ for some real $\mu_1 < \dots < \mu_n$ and $\nu = \frac{1}{n}(\delta(\nu_1) + \dots + \delta(\nu_n))$, for some real $\nu_1 < \dots < \nu_n$. Indeed, squares of both sides are equal to $$\frac{1}{n}\sum_{i=1}^{n}|\mu_i - \nu_i|^2.$$

PS: note that measures of the type $\mu = \frac{1}{n}(\delta(\mu_1) + \dots + \delta(\mu_n)),$ for some real $\mu_1 < \dots < \mu_n$ are dense in Wasserstein space see Proposition 2.10 from Karl-Theodor Sturm. "On the geometry of metric measure spaces.". So all the above can be formalized with some suffering.

$\mathcal{W}_2(\mathbb{R})$ is isometric to a convex subset of a Hilbert space.

The argument can find an argument in S.S. Vallender "Calculation of the Wasserstein distance between probability distributions on the line". I will do an informal sketch.

I want to see probability measures in $\mathbb{R}$ as piles of sand with sand grains enumerated from left to right and indexed by $[0,1]$. For a measure $\mu$ define function $\Phi_{\mu}:[0,1]\rightarrow \mathbb{R}$ by $\Phi_\mu(t)$ be a position of $t$-th grain in $\mathbb{R}$. I claim that for every $\mu, \nu$ $$\mathcal{W}_2(\mu,\nu) = ||\Phi(\mu)-\Phi(\nu)||_{L_2}.$$

Before proving this let say how the optimal transport looks on $\mathbb{R}$.

Lemma 1: for the measures $\mu$ and $\nu$ the optimal transport plan between them sends $t$-th sand grain of $\mu$ into $t$-th sand grain of $\nu$.

I give the proof of the lemma for the case when $\mu = \frac{1}{n}(\delta(\mu_1) + \dots + \delta(\mu_n)),$ for some real $\mu_1 < \dots < \mu_n$ and $\nu = \frac{1}{n}(\delta(\nu_1) + \dots + \delta(\nu_n))$, for some real $\nu_1 < \dots < \nu_n$. I need to show that optimal transport moves $\frac{1}{n} \delta(\mu_1)$ into $\frac{1}{n}\delta(\nu_1)$ and then the rest will follow by induction. Okay, lets assume its not and (a part of) $\frac{1}{n} \delta(\mu_1)$ is moved to $\frac{1}{n} \delta(\nu_k)$ and (a part of) $\frac{1}{n}\delta(\mu_j)$ is moved to $\frac{1}{n} \delta(\nu_1)$ for some $j,k > 1$. I claim that if we change our transport by moving (the part of) $\frac{1}{n} \delta(\mu_1)$ into $\frac{1}{n}\delta(\nu_1)$ and (the part of) $\frac{1}{n}\delta(\mu_j)$ into $\frac{1}{n} \delta(\nu_k)$ it will get cheaper. Which follows from the following trivial lemma.

Lemma 2: Suppose that $a,b,c > 0$ then $a^2 + (b + c)^2 < (a + c)^2 + b^2$ iff $b < a$.

Now it's really easy to proof our main statement $$\mathcal{W}_2(\mu,\nu) = ||\Phi(\mu)-\Phi(\nu)||_{L_2}.$$
for the case when $\mu = \frac{1}{n}(\delta(\mu_1) + \dots + \delta(\mu_n)),$ for some real $\mu_1 < \dots < \mu_n$ and $\nu = \frac{1}{n}(\delta(\nu_1) + \dots + \delta(\nu_n))$, for some real $\nu_1 < \dots < \nu_n$. Indeed, both sides are equal to $$\frac{1}{n}\sum_{i=1}^{n}|\mu_i - \nu_i|^2.$$

PS: note that measures of the type $\mu = \frac{1}{n}(\delta(\mu_1) + \dots + \delta(\mu_n)),$ for some real $\mu_1 < \dots < \mu_n$ are dense in Wasserstein space see Proposition 2.10 from Karl-Theodor Sturm. "On the geometry of metric measure spaces.". So all the above can be formalized with some suffering.

$\mathcal{W}_2(\mathbb{R})$ is isometric to a convex subset of a Hilbert space (and embedding a hyperbolic space into Euclidean/Hilbert one has a lot of history).

The argument can find an argument in S.S. Vallender "Calculation of the Wasserstein distance between probability distributions on the line". I will do an informal sketch.

I want to see probability measures in $\mathbb{R}$ as piles of sand with sand grains enumerated from left to right and indexed by $[0,1]$. For a measure $\mu$ define function $\Phi_{\mu}:[0,1]\rightarrow \mathbb{R}$ by $\Phi_\mu(t)$ be a position of $t$-th grain in $\mathbb{R}$. I claim that for every $\mu, \nu$ $$\mathcal{W}_2(\mu,\nu) = ||\Phi(\mu)-\Phi(\nu)||_{L_2}.$$

Before proving this let say how the optimal transport looks on $\mathbb{R}$.

Lemma 1: for the measures $\mu$ and $\nu$ the optimal transport plan between them sends $t$-th sand grain of $\mu$ into $t$-th sand grain of $\nu$.

I give the proof of the lemma for the case when $\mu = \frac{1}{n}(\delta(\mu_1) + \dots + \delta(\mu_n)),$ for some real $\mu_1 < \dots < \mu_n$ and $\nu = \frac{1}{n}(\delta(\nu_1) + \dots + \delta(\nu_n))$, for some real $\nu_1 < \dots < \nu_n$. I need to show that optimal transport moves $\frac{1}{n} \delta(\mu_1)$ into $\frac{1}{n}\delta(\nu_1)$ and then the rest will follow by induction. Okay, lets assume its not and (a part of) $\frac{1}{n} \delta(\mu_1)$ is moved to $\frac{1}{n} \delta(\nu_k)$ and (a part of) $\frac{1}{n}\delta(\mu_j)$ is moved to $\frac{1}{n} \delta(\nu_1)$ for some $j,k > 1$. I claim that if we change our transport by moving (the part of) $\frac{1}{n} \delta(\mu_1)$ into $\frac{1}{n}\delta(\nu_1)$ and (the part of) $\frac{1}{n}\delta(\mu_j)$ into $\frac{1}{n} \delta(\nu_k)$ it will get cheaper. Which follows from the following trivial lemma.

Lemma 2: Suppose that $a,b,c > 0$ then $a^2 + (b + c)^2 < (a + c)^2 + b^2$ iff $b < a$.

Now it's really easy to proof our main statement $$\mathcal{W}_2(\mu,\nu) = ||\Phi(\mu)-\Phi(\nu)||_{L_2}.$$
for the case when $\mu = \frac{1}{n}(\delta(\mu_1) + \dots + \delta(\mu_n)),$ for some real $\mu_1 < \dots < \mu_n$ and $\nu = \frac{1}{n}(\delta(\nu_1) + \dots + \delta(\nu_n))$, for some real $\nu_1 < \dots < \nu_n$. Indeed, both sides are equal to $$\frac{1}{n}\sum_{i=1}^{n}|\mu_i - \nu_i|^2.$$

PS: note that measures of the type $\mu = \frac{1}{n}(\delta(\mu_1) + \dots + \delta(\mu_n)),$ for some real $\mu_1 < \dots < \mu_n$ are dense in Wasserstein space see Proposition 2.10 from Karl-Theodor Sturm. "On the geometry of metric measure spaces.". So all the above can be formalized with some suffering.