Forecast of the U.S. Copper Demand: a Framework Based on Scenario Analysis and Stock Dynamics

In a world of finite metallic minerals, demand forecasting is crucial for managing the stocks and flows of these critical resources. Previous studies have projected copper supply and demand at the global level and the regional level of EU and China. However, no comprehensive study exists for the U.S., which has displayed unique copper consumption and dematerialization trends. In this study, we adapted the stock dynamics approach to forecast the U.S. copper in-use stock (IUS), consumption, and end-of-life (EOL) flows from 2016 to 2070 under various U.S.-specific scenarios. Assuming different socio-technological development trajectories, our model results are consistent with a stabilization range of 215–260 kg/person for the IUS. This is projected along with steady growth in the annual copper consumption and EOL copper generation driven mainly by the growing U.S. population. This stabilization trend of per capita IUS indicates that future copper consumption will largely recuperate IUS losses, allowing 34–39% of future demand to be met potentially by recycling 43% of domestic EOL copper. Despite the recent trends of “dematerialization”, adaptive policies still need to be designed for enhancing the EOL recovery, especially in light of a potential transitioning to a “green technology” future with increased electrification dictating higher copper demand

Quantitative comparison of campus and shelf datasets with PCP.

Results for other methods are taken from their respective papers.</p

Datasets description.

The details of experimental datasets are described in the separated S1 File. (DOCX)</p

The results of ablation analysis experiment.

(a) shows our best result and it is chosen as the benchmark for comparison. (b) and (c) shows the visual result for changing the 3D common space voxel anchor scale as 0.15m and 0.25m. (d) and (e) shows the visual result when the 3D bounding box voxel anchor scale is changed as 0.1m and 0.25m. In (f), (g) and (h), the 3D bounding box scale is changed as 1m, 2.8m and 6.4m for ablation analysis. In order to speed up calculations, the 3D bounding box voxel anchor scale is set to 0.1m and compared with (d).</p

The result shows the 3D pose estimation on Campus dataset.

There are only three people with small scale, the complete 3D poses are also estimated, which proves that our backbone is effective.</p

Our CTP network avoids the complex matching task.

(a) We estimate 2D heatmaps from all views. (b) When all 2D keypoint heatmaps projected into 3D common space, the space is voxelized into regular grids. (c) After convolution by front-layer in backbone, we get the preliminary 3D feature maps. (d) The 3D feature maps are transformed into 2D feature maps and passed into 2D CNN network. The center of one person is generated in top view. (e)The 3D bounding box is regressed. (f) The 3D bounding box is voxelized into more detailed grids for estimating accurate 3D pose. (g) The estimation of 3D poses outputs from our network.</p

Comparison with [37] on CMU Panoptic dataset under 5 views.

Comparison with [37] on CMU Panoptic dataset under 5 views.</p

The multiple people visual result on CMU Panoptic dataset.

We select some datasets with different numbers of people to evaluate our method. Our method is also robust in multi-person scenes with more than 5 people.</p

The visual results on shelf dataset.

(a1) is the regular show on shelf dataset. (a2) shows the projected 2D pose from 3D pose and the 2D poses are not the estimated poses from HRNet. The result in (a2) shows that the multiple views 3D pose estimated can compensate for invisible pose information.</p

The PCP result of ablation about our CTP network.

The number in parameters name represent the anchor scale or box scale.</p