  Accounting for over 20% of the total carbon emissions, the precise estimation
of on-road transportation carbon emissions is crucial for carbon emission
monitoring and efficient mitigation policy formulation. However, existing
estimation methods typically depend on hard-to-collect individual statistics of
vehicle miles traveled to calculate emissions, thereby suffering from high data
collection difficulty. To relieve this issue by utilizing the strong pattern
recognition of artificial intelligence, we incorporate two sources of open data
representative of the transportation demand and capacity factors, the
origin-destination (OD) flow data and the road network data, to build a
hierarchical heterogeneous graph learning method for on-road carbon emission
estimation (HENCE). Specifically, a hierarchical graph consisting of the road
network level, community level, and region level is constructed to model the
multi-scale road network-based connectivity and travel connection between
spatial areas. Heterogeneous graphs consisting of OD links and spatial links
are further built at both the community level and region level to capture the
intrinsic interactions between travel demand and road network accessibility.
Extensive experiments on two large-scale real-world datasets demonstrate
HENCE's effectiveness and superiority with R-squared exceeding 0.75 and
outperforming baselines by 9.60% on average, validating its success in
pioneering the use of artificial intelligence to empower carbon emission
management and sustainability development. The implementation codes are
available at this link: https://github.com/tsinghua-fib-lab/HENCE.
