Predicting Urban Carbon Peak Through an Extended STIRPAT Model: A Scenario Analysis of Bozhou, China
Year: 2026
Type: article
Abstract: Cities play an important role in achieving carbon emission peaking and neutrality targets, yet identifying feasible mitigation pathways remains challenging for prefecture-level cities with limited historical data. This study develops a context-specific extended STIRPAT framework to examine the relationships between urban carbon emissions and nine socioeconomic and sector-related indicators in Bozhou, China. Based on annual data from 2010 to 2022, grey relational analysis was used to support indicator selection, and ridge regression was applied to address severe multicollinearity. Ten scenarios, comprising one baseline and nine alternative development pathways, were constructed to assess carbon emission trajectories during 2023–2035. Population exhibited the largest standardized ridge regression coefficient among the selected explanatory variables. Scenario analysis indicates that carbon emissions under the baseline scenario (S1) reach a projected maximum of 16.78 Mt CO2 in 2030. The energy consumption adjustment scenario (S6) and the coordinated development scenario (S8) reach their projected maxima in 2025, at 15.60 and 15.69 Mt CO2, respectively. Under the integrated scenarios S9 and S10, emissions decline continuously throughout 2023–2035 without reaching a new peak during the projection period. Compared with S1, projected emissions in 2030 are 10.65%, 9.06%, 24.74%, and 37.99% lower under S6, S8, S9, and S10, respectively. A supplementary Monte Carlo analysis further illustrates the sensitivity of these projections to uncertainty in the assumed explanatory-variable growth rates. The findings should be interpreted as conditional scenario outcomes rather than precise forecasts or causal estimates of policy effectiveness.
Source: Buildings
Institution: Anhui Jianzhu University
Language: English
Subfield: Economics and Econometrics
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W7225661302
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