Search

• Revealing superliner link between CFP and population density in the lower CFP cities. • Population clustering may mitigate CFP in the higher CFP cities. • Presenting policy implication of differentiated population density regulation. The growing imbalance between anthropogenic carbon emissions and vegetation carbon sequestration is impeding cities' sustainability. However, previous research has not reached a consensus on whether population density regulation can mitigate this growing imbalance. In this study, we first used the carbon footprint pressure (CFP) indicator to examine the temporal and spatial characteristics of the imbalance in 370 Chinese cities over the last 20 years. Second, we demonstrated the favorable but limited effect of existing technology on CFP mitigation using the IPAT equation, the Logarithmic Mean Divisia Index decomposition approach, and the Kaya identity. Further, we emphasized the dominant effect of the population on CFP in urban agglomerations and provincial capital cities through Maxwell's triangle. Finally, using a built panel data model, we identified an inverted U-curve relationship between CFP and population density at the middle and low quartiles, whereas population clustering promoted CFP mitigation at the upper quartiles. Given the limited effectiveness of current climate actions, as well as the coexistence of urban diseases and unoccupied buildings, we provided differentiated policy implications in city-scale for population density regulation. This study provides some inspiration for authorities to formulate city-scale population policies to mitigate climate change and achieve sustainable development. [ABSTRACT FROM AUTHOR]

Carbon capture, utilization, and storage (CCUS) is a crucial technology for global climate mitigation. To implement large-scale CCUS, decisions about where and how to build a CO 2 transportation network are urgently required. However, previous studies are limited in proposing the economical transportation network for CCUS that considered comprehensive risks from socio-economic, geographical and geological factors. Here, we develop a pipeline layout planning model to investigate the cost-effective and reliable transportation network for CCUS. Results show that to meet the annual CO 2 emission reduction target of 654 million tons for CCUS in the coal-fired power sector under China's carbon neutral goal, pipelines with a total length of 17,589 km need to be constructed. The emission sources can be transported in pipelines with an average radius of 58 km, and with diameters of 8, 12, and 16 inches. Northeast, north, and northwest China are the main areas suitable for pipeline construction. • Northeast, North, and Northwest in China are vital areas for capture, transport, and storage. • Five large-scale pipeline network groups can be constructed in China. • Pipeline construction is greatly affected by a variety of practical factors. [ABSTRACT FROM AUTHOR]

• We construct a multi-model comparison framework by employing typical IAMs. • We examine how China will contribute to the global 1.5 °C climate target. • China could contribute nearly one-fifth of the global negative emissions by 2100. • The proportion of BECCS could dramatically increase to 29% under the 1.5 °C case. • Energy demand control may rely more on coordination between CCS and clean energy. Enhancing the warm-limiting targets to from below 2 °C to 1.5 °C is of great significance for climate change mitigation, during which the carbon capture and storage (CCS) of China is playing an important role. By framing a multi-model analysis to compare IAMs, this study aims to explore how China can contribute to the global 1.5 °C target, consistent with achieving its carbon neutrality goal. Our multi-model assessment shows that the enhanced targets will advance the reach time of China's carbon peak and neutrality. Under a relatively smooth emission path curve with CCS, China's carbon emissions will decrease sharply by the end of this century. It is estimated that China's negative carbon emissions will contribute nearly one-fifth of the global negative emissions in 2100, with its CCS-captured emissions accounting for 14%. In the energy system, fossil- and biomass-CCS will develop rapidly, with the proportion of biomass with CCS dramatically increasing to 29% and 17% under the 1.5 °C and 2 °C scenarios, respectively. There will be a relatively large gap between the capital and final use costs of CCS, which may weaken the technical advantage when competing with renewables during the life cycle. While the overall control of the energy consumption in the later period will rely more on the proportional adjustment and the coordination between biomass with CCS and clean energy. [ABSTRACT FROM AUTHOR]

[Display omitted] • A bi-level joint-probabilistic programming approach is developed. • It can handle leader–follower issues and joint-probabilistic constraints. • The proposed approach is applied to China's multi-regional energy system model. • The optimal schemes during 2021–2050 under three mitigation scenarios are obtained. • Results are helpful to achieve low-carbon goals in multi-regional energy system. Climate change mitigation and renewable resources utilization are becoming particularly urgent for energy system management. In this study, a bi-level joint-probabilistic programming (BJPP) method is developed for planning multi-regional energy system under different mitigation policies and uncertainties. BJPP can handle leader–follower issues in decision-making process as well as examine the risk of violating joint-probabilistic constraints. Based on the BJPP method, a China's multi-regional energy system (named as BJPP_CMES) model is formulated to provide optimal scheme for energy system planning of China over a long-term horizon (2021–2050) by synergistically minimizing carbon dioxide (CO 2) emission and system cost. A series of scenarios associated with different carbon capture and storage (CCS) levels and violation risks of energy-demand constraints are examined. Results reveal that: (i) the share of non-fossil energy in China's energy supply would keep increasing in 2021–2050, and the highest growth of the renewable supply would occur in Ningxia (rising 47.7%); (ii) Sichuan, Inner Mongolia, and Gansu would be the top three suppliers of renewable electricity; (iii) the CO 2 emission of China would reach a peak of [44.3, 54.8] billion tonnes during the period of 2026–2030; Shandong, Inner Mongolia, and Shanxi would be main contributors of CO 2 emission in the future; (iv) compared with the single-level model, the CO 2 emission from the BJPP_CMES model would reduce by [2.7, 5.7]%; (v) among developed regions, the individual probability level of Jiangsu-Zhejiang-Shanghai is the most significant parameter for both CO 2 emission and system cost. The findings are helpful for decision makers to optimize multi-regional energy system (MES) with a low-carbon and cost-effective manner, as well as to provide useful information for renewable energy utilization and regional sustainable development. [ABSTRACT FROM AUTHOR]