| Title |
Characterizing the spatiotemporal evolution of building material stock in China's Greater Bay Area: A statistical regression method |
| ID_Doc |
22040 |
| Authors |
Yuan, L; Lu, WS; Wu, YJ |
| Title |
Characterizing the spatiotemporal evolution of building material stock in China's Greater Bay Area: A statistical regression method |
| Year |
2023 |
| Published |
Journal Of Industrial Ecology, 27.0, 6 |
| DOI |
10.1111/jiec.13438 |
| Abstract |
More than half of the materials extracted from natural environments eventually accumulate as building material stock (BMS). From a linear-to-circular economy perspective, BMS transforms the building sector from a virgin material consumer and a waste generator to a future depository of secondary resources. Studies characterizing the amount and distribution of BMS adopt different approaches, but high data requirements restrict their applicability. This research proposes an alternative method for regional BMS quantification. The method leverages the permanent population, electricity consumption, and BMS of a sample city to develop a statistical regression model; then uses it to estimate the BMS of a larger, homogenous region. With relatively low data requirements, the new method is especially applicable in underdeveloped areas where data required for BMS quantification methods are usually unavailable or incomplete. We apply the method to characterize the spatiotemporal evolution of BMS in China's Greater Bay Area. From 2000 to 2021, the total BMS in this region increased from 4.4 to 7.7 billion tonnes, with concrete, brick, and steel accounting for 72.32%, 17.57%, and 4.71% of the total BMS, respectively. The most rapid BMS growth occurred in Guangzhou (from 534.75 to 1277.82 Mt) and Shenzhen (517.80 to 1235.48 Mt). A core-edge BMS accumulation pattern emerged in this area while the BMS peak showed a coast-to-inland shift. Future studies can explore generalizing this new method to characterize BMS in other developing regions. |
| Author Keywords |
building material stock; circular economy; construction material; Greater Bay Area; industrial ecology; statistical regression |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:001067169900001 |
| WoS Category |
Green & Sustainable Science & Technology; Engineering, Environmental; Environmental Sciences |
| Research Area |
Science & Technology - Other Topics; Engineering; Environmental Sciences & Ecology |
| PDF |
https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/jiec.13438
|