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• The 57.00% of the Yangtze River Economic Belt area exhibited a significant increase in annual NPP. • The response of NPP on climate factors was nonlinear, with greater sensitivity to temperature. • The impact of topographic factors on NPP changes had a threshold effect. Net primary productivity (NPP) of terrestrial ecosystems serves as a crucial carbon sink, playing a significant role in mitigating climate change. This study, focusing on the Yangtze River Economic Belt (YREB), a key area for both economic development and ecological conservation in China, evaluates the temporal and spatial changes in regional NPP and its responses to climatic factors against the backdrop of global change. Utilizing long-term NPP and meteorological grid data from 1981 to 2017, we analyzed the annual change patterns of NPP and its linear and nonlinear responses to climatic factors using copula functions. Our findings indicate that 57.00 % of the YREB has experienced a significant increase in NPP, with the most substantial decreases observed in Shanghai and its surrounding areas (P < 0.05). Temperature emerged as the primary climatic driver, positively correlating with NPP changes in over half of the study area (P < 0.05). Moreover, the nonlinear response of NPP to temperature and precipitation underscores the complex interactions between these factors, revealing a sensitivity to temperature variations that surpasses that to precipitation. The study also highlights the influence of solar radiation and topographic factors, with a significant positive correlation between NPP and solar radiation observed in 15.85 % of the area (P < 0.05). The practical significance of these findings lies in their implications for regional ecological management and climate resilience strategies. The positive correlation between temperature and NPP underscores the potential for targeted temperature management to optimize carbon sequestration across the YREB. Furthermore, the identified nonlinear responses and the role of solar radiation and topography in influencing NPP offer valuable insights for the formulation of adaptive strategies that enhance ecosystem productivity and contribute to achieving carbon neutrality goals. By elucidating the dynamics of NPP in response to a range of environmental factors, this study provides a foundation for informed decision-making in ecological conservation and climate change mitigation efforts within the YREB and similar regions globally. [ABSTRACT FROM AUTHOR]

Coastal salt marshes suffering from anthropogenic coastal development and sea level rise have attracted much attention because of their capacity for carbon sequestration and global climate change mitigation. Accurate mapping of coastal salt marshes is always the first step for their protection, management, and restoration. The inherent complexities of vegetation, dynamics of tides, and anthropogenic disturbances pose challenges for remote sensing-based approaches. Existing studies have utilized phenology information and various black-box algorithms to reduce misclassifications. However, the approaches with dual-temporal images containing phenology information have suffered from inefficiency; the misclassifications have been objectively post-processed rather than considered in the method design, and the tacit knowledge of the trained black-box models has not been revealed. To address the above issues, we proposed a new approach with solid improvements in dual-temporal image construction, misclassification processing, and tacit knowledge analysis, including an efficient method to synthesize dual-temporal images based on the common divisor of the green-up season or senescence season resulting from latitudinal gradients in narrow coastal areas of China, a detailed classification scheme by interpretation of iteratively collected samples, and a key decision rule approximating the trained model. We applied the approach to Sentinel-1/2 images and DEM data, thus deriving a 10-m resolution coastal salt marsh map of China with an overall accuracy of 92.5%. A qualitative comparison showed that the map produced in this study was fitted well with actual salt marsh distributions, resulting in improved accuracy when compared to recently generated maps. The most important contribution is that the overall nature of the trained model observed from the training samples was approximated by a four-feature decision rule following the principle of explainable artificial intelligence, i.e., B8 senescence /B4 senescence < 2.06 & B4 green /B8 green < 0.78 & B12 green /B11 green < 0.72 & elevation < 2.13 , and thus established a new potential classification mechanism for dual-temporal black-box algorithms, i.e., the water signal was covered up by flourishing vegetation, but the situation changed when vegetation withered. This study not only generates an accurate coastal salt marsh map at a national scale but also provides a classification mechanism for dual-temporal image-based coastal salt marsh identification and mapping. • Developed an efficient method for constructing senescence and green images. • Established a mechanism for dual-temporal image-based coastal salt marsh mapping. • Provided a coastal salt marsh map with solid improvements on the most recent one. • Explored a case of explainable artificial intelligence (XAI) in the coastal. [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]

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]

• CO 2 sequestration and EGR are experimentally evaluated in two shale gas reservoirs. • Pore heterogeneity and connectivity are evaluated via multifractal analysis. • Shale composition controls on gas storage is evaluated from gas adsorption measurements. • Links between MICP and gas adsorption measurements were used to evaluate EGR suitability. In the global effort toward climate change mitigation, geologic storage of CO 2 in shale plays is becoming critical while it can improve production from these reservoirs. In this study, the gas storage capacity and CO 2 sequestration suitability of the Longmaxi and Niutitang shale gas reservoirs is investigated and compared via XRD analysis, programmed pyrolysis, low pressure N 2 and CO 2 adsorption, high pressure CH 4 adsorption, and MICP analysis. Results showed that clay and TOC controls on micropore heterogeneity and connectivity happens only within the Longmaxi Formation. Furthermore, both components play a role in the adsorbed quantities of gas in the formation which revealing a higher storage capacity in mesopore-macropores. This was verified by a positive correlation between adsorbed volume within the mesopore-macropore space. The Longmaxi Formation was also found to own better pore connectivity and PSD homogeneity of mesopore-macropores, indicating easier pathways for CO 2 movement channels within the pore network. Furthermore, the samples from the Longmaxi exhibited a positive relationship between adsorbed volume with the CO 2 breakthrough pressure and median pore diameter, alike between the mesopore-macropore volume and CO 2 breakthrough pressure. This means, the injected CO 2 will displace adsorbed methane within the mesopore-macropore and will be adsorbed and stored in such pores. Overall, superiority of the Longmaxi shale over the Niutitang for CO 2 storage and EGR would be significant for future operations in similar shale gas reservoirs in China and around the globe. [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]

• An integrated top-down and bottom-up modeling framework was developed. • Analyze low-carbon pathways for Chinese iron & steel industry towards carbon neutrality. • Both the supply-side and demand-side contributions to decarbonization are explored. • Energy-saving retrofitting technologies help short-term CO 2 emissions reduction. • Breakthrough technologies are crucial in achieving carbon neutrality. Iron and steel production in China contributes to 14% of China's total energy-related CO 2 emissions. Decarbonizing the iron and steel sector will therefore play an important role in achieving the goal of carbon neutrality. This study explored possible low-carbon transition pathways for China's iron and steel industry to achieve carbon neutrality by 2050. An integrated approach was developed that combined a computable general equilibrium model and a bottom-up technology-selection module. The results indicated that although energy-saving technologies can reduce CO 2 emissions in the short term, in the long term, adopting breakthrough technologies (e.g., carbon capture and storage (CCS) and hydrogen-based direct reduction (DR)), increasing the share of scrap-based electric arc furnace (EAF) steel production, and decarbonizing upstream energy-supply sectors will be crucial for climate change mitigation. Hydrogen-based DR could be an effective option for CO 2 emission reduction in scenarios where CCS is not available, with its share increasing to 23%–25% by 2050. System-wide cross-sector decarbonization can help achieve climate targets at lower costs through flexible technology combinations and avoid carbon leakage into upstream energy-supply sectors. [ABSTRACT FROM AUTHOR]