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Beijing-Tianjin-Hebei metropolitan area is a significant carbon emission center. The region's early achievement of peak carbon targets is critical to the nation's achievement of peak carbon targets. In this paper, it is proposed to use different orders of grey models to classify into three scenarios. Based on three scenarios, the grey multivariate convolutional model with new information priority accumulation is adopted to predict carbon emissions in the Beijing-Tianjin-Hebei region and select the scenario suitable for local development. The results show that: (1) The Beijing region has already achieved peak carbon, the Tianjin region may not reach its peak carbon target by 2030, and the Hebei region is expected to reach its peak carbon target by 2030. (2) The high rate of carbon emission reduction scenario will greatly improve the air quality of Beijing. The low-speed growth carbon emission scenario is more in line with the future development of Tianjin city. The low-rate carbon reduction scenario is more in line with the synergistic governance of pollution reduction and carbon reduction in Hebei Province. (3) Beijing's population policy in the most recent years has been conducive to improving the local environment. Tianjin's medium-term population policy is more in line with the local area. Hebei's medium-term industrial structure reform is favorable to local development. • Grey correlation analysis is used to analyze the factors affected carbon emissions in a region. • The factors with high correlation are predicted by using grey prediction models of different orders. • The high rate of carbon emission reduction scenario will greatly improve the air quality of Beijing. [ABSTRACT FROM AUTHOR]

Forecasting the future emission trajectories and the relating sensitive driving factors of emissions for cities is of great significance to formulate realizable CO 2 mitigation policies. To proceed the forecasting, studies on peak prediction and quantification of reduction potential at the city level are essential. However, the studies in the area are very limited. Selecting the Beijing-Tianjin-Hebei urban agglomeration (BTH) as the study region, this paper aims to contribute to the research area and provides implications for other cities or urban agglomerations. The Kaya identity and multi-scenario simulation were employed to predict the dynamic evolution pathways of CO 2 emissions from 2021 to 2035 and explore the differential CO 2 peak time, peak value, and reduction potential for 13 cities in BTH. Monte Carlo simulation, Mann-Kendall trend test and Sen's slope estimation method are jointly used to reduce uncertainties in estimation. The Monte Carlo simulation results show that most cities in BTH have already reached their CO 2 emissions peak, while Tianjin, Langfang, Cangzhou and Tangshan are expected to reach their peaks between 2025 and 2030. Among them, 5 and 8 cities have the risk of not reaching their peak before 2035 in the high consumption scenario (HCS) and extensive development scenario (EDS) respectively. Comparative analysis reveals that low-carbon scenario (LCS) and sustainable development scenario (SDS) have significant effects on emissions reductions. The top three cities in terms of accumulative emission reduction in 2021–2035 are Tianjin, Tangshan and Cangzhou, estimated as 117.82–250.75 Mt CO 2 in LCS and 217.77–454.10 Mt CO 2 in SDS, respectively. The results of sensitivity analysis reveal that the most critical driver of CO 2 emissions in Beijing is population, while that is GDP per capita for Tianjin. Langfang and Hengshui showed the highest sensitivity to energy intensity. Accordingly, these cities have differentiated concerns and priorities to achieve their carbon peak goal as scheduled. • Kaya identity and scenario simulation are combined to predict future CO 2 emissions. • The BTH would reach CO 2 emission peak by 2025, but the time varies on city-level. • Tangshan, Langfang, and Cangzhou are at greater risk of not reaching the peak by 2030. • Tianjin, Tangshan and Cangzhou have the greatest potential to reduce emissions. • Differentiated priorities are suggested for specific city's CO 2 mitigation targets. [ABSTRACT FROM AUTHOR]

The carbon peaking and carbon neutrality strategy have become China's national strategy. Given the significant regional differences in China, the Chinese government has proposed "one policy for one province" guiding principle for carbon emission reduction. This study takes Tianjin as a representative of "a province-level city with high degree of industrialization and prominent emerging industries", and Gansu Province a representative of "a province with a low degree of industrialization but the potential for green energy". Using the integrated modeling frameworkthis study conducts a comparative analysis of the hidden flows generated by inter-provincial inward and outward transfers and extracts a feasible model for carbon neutrality transformation. The study also focuses on carbon emission, GDP changes, carbon hidden flow change in trade and carbon emission reduction path. By comparing the results, this study identifies the mechanism of the industrial spillover effect and carbon emission spillover effect between the two regions. Then it concludes the "cross-regional transfer mode of high-carbon industries" of industrial carbon neutral transformation: more carbon emission allowances are allocated to developing regions represented by Gansu Province, implement regionally differentiated carbon emission reduction constraint policies, and achieve industrial spatial transfer through more inter-provincial transfer. The paper found that in implementing a regionally differentiated carbon emission reduction constraint policy (DE scenario), the transfer of high-carbon industries can achieve the "superposition effect" through inter-provincial transfer. Accordingly, three carbon policy mechanisms for cross-regional transfer of high-carbon industries are summarized. [Display omitted] [ABSTRACT FROM AUTHOR]

China's Jing-Jin-Ji urban agglomeration shows exceptionally high urbanization and resource utilization problems that require to identify hot paths and sectors of energy metabolism, thus recognizing key links of cleaner production, and supporting energy conservation and emission reduction. In this paper, an energy metabolic network model was built for the urban agglomeration to simulate energy transfers between sectors using ecological network analysis, and path analysis was performed starting from initial production to identify critical paths and sectors. The chain of paths through 0–3 intermediate nodes dominated the energy metabolic processes, becoming the key to diagnosing problems. The critical paths in the urban agglomeration evolved from joint dominance by intra- and inter-city paths to inter-city paths, indicating increasingly coordinated development. Intra-city paths from Beijing's and Tianjin's Industry were always critical, whereas inter-city paths mainly started from Shijiazhuang's, Handan's, Tangshan's or Baoding's Industry; Beijing's, Tianjin's, Shijiazhuang's, Tangshan's and Handan's Industry played export roles, with Beijing's, Tianjin's, and Shijiazhuang's Industry also playing transfer roles and the other sectors (especially Construction) playing import roles. Above results will serve for the agglomeration's cleaner, more sustainable and more coordinated development. [Display omitted] • A sector-scale energy metabolic network of Jing-Ji-Ji urban agglomeration was developed. • Path analysis was conducted to identify key paths and nodes. • Paths with length ≤4 accounted for around 70% of the integrated metabolic intensity. • Critical paths evolved from joint dominance by intra- and inter-city paths to inter-city paths. • Industry sector often played export and transfer role, the other sectors played import roles. [ABSTRACT FROM AUTHOR]