Carbon-Neutrality Gap in Resource-Based Cities: STIRPAT Simulation and Cross-Validation of Carbon-Sink Models
Open Access
- 2 July 2026
- journal article
- research article
- Published by MDPI AG in Sustainability
- Vol. 18 (13) , 6722
Abstract
Coal-dominated resource-based cities face a structurally embedded carbon-neutrality gap, shaped by the simultaneous pressures of industrial carbon lock-in and ecological fragility. China’s dual-carbon targets impose severe transition pressure on such regions, where carbon-intensive industries, strong path dependence, and limited decarbonization flexibility compound the challenge. Forest carbon sinks offer a cost-effective approach for offsetting residual emissions. However, water scarcity and restricted land-carrying capacity impose hard ecological ceilings on sink expansion in semi-arid areas such as the Loess Plateau. Existing studies have largely focused on national or provincial scales, with few addressing the coupled dynamics of industrial emissions and water-limited sink capacity at the county level. This study examines Shenmu, China’s largest coal-producing county-level city and a national energy-chemical industrial base. Using time-series data spanning 2010–2025, we project multi-scenario carbon emissions via an extended STIRPAT model with ridge regression, estimate forest carbon sink potential through a growing-stock (GS) gradient model cross-validated against GM(1,1), and systematically quantify the resulting carbon-neutrality gap. The results show that energy activities dominate total emissions throughout, consistently exceeding 90% of the aggregate. Under the baseline scenario, emissions reach 407.96 MtCO2eq in 2060 without peaking; under moderate mitigation, emissions peak at 269.39 MtCO2eq in 2050; under strengthened mitigation, emissions peak at 225.80 MtCO2eq before 2040 and subsequently decline. Forest carbon sinks are projected to offset 2.1–11.2% of emissions by 2060 under all scenarios, constrained by climatic aridity, finite afforestation potential, and water–soil carrying capacity thresholds. The carbon-neutrality gap remains structurally positive across every scenario, reflecting a fundamental asymmetry between rigid emission growth and ecologically bounded sink capacity. These findings indicate that only an integrated pathway combining industrial restructuring, energy decarbonization, diversified ecological sinks, and CCUS deployment can substantially narrow the gap; carbon neutrality by 2060 is unattainable through natural sinks alone.Keywords
Funding Information
- National Natural Science Foundation of China (42407031)
- National Natural Science Foundation of China (42330713)
- China University of Geosciences, Beijing (2023001)
- National Joint Research Center for Ecological Protection and High Quality Development in the Yellow River Basin of China (2022-YRUC-01-0306)
This publication has 88 references indexed in Scilit:
- An examination of daily CO2 emissions prediction through a comparative analysis of machine learning, deep learning, and statistical modelsEnvironmental Science and Pollution Research, 2025
- Green development and economic resilience: Evidence from Chinese resource-based citiesFrontiers of Engineering Management, 2024
- Low-carbon development pathways for resource-based cities in China under the carbon peaking and carbon neutrality goalsEnvironmental Science and Pollution Research, 2023
- Forecasting the carbon emissions in Hubei Province under the background of carbon neutrality: a novel STIRPAT extended model with ridge regression and scenario analysisEnvironmental Science and Pollution Research, 2023
- Carbon peak forecast and low carbon policy choice of transportation industry in China: scenario prediction based on STIRPAT modelEnvironmental Science and Pollution Research, 2023
- Forecasting Chinese carbon emissions based on a novel time series prediction methodEnergy Science & Engineering, 2020
- Analysis of influencing factors of the carbon dioxide emissions in China’s commercial department based on the STIRPAT model and ridge regressionEnvironmental Science and Pollution Research, 2019
- Spatial variations in soil-water carrying capacity of three typical revegetation species on the Loess Plateau, ChinaAgriculture, Ecosystems & Environment, 2019
- The necessary and sufficient condition for GM(1,1) grey prediction modelApplied Mathematics and Computation, 2013
- Bootstrap prediction intervals for autoregressive models fitted to non-autoregressive processesStatistical Methods & Applications, 1998