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Cities consume most of the global energy and account for approximately 75% of global CO 2 emissions. Moreover, resource-based cities tend to consume higher amounts of fossil fuel and produce higher carbon emissions than other cities, whereas there are still insufficiencies about energy carbon reduction in resource-based cities in developed urban agglomerations. Therefore, it is necessary to explore the energy planning of such cities to achieve carbon peaking and carbon neutrality, and this is crucial for achieving carbon neutrality in urban agglomerations. This study aims to apply a scenario-based analysis approach to a resource-based heavy industrial city in China, Tangshan, based on the Long-range Energy Alternative Planning model, to explore Tangshan's future energy consumption, generation, and associated carbon emissions from 2010 to 2050. The impacts of different energy policies are analyzed for the Tangshan energy system by considering four scenarios. Under each scenario, energy consumption increased steadily by 2050, but the growth rate is different. The business as usual scenario is the fastest, and the low-carbon scenario is the slowest. In the comprehensive and integrated low-carbon transition scenarios, CO 2 is expected to peak at 298.34 million tons (Mt) in 2028. The main industrial sectors of Tangshan with high output value, high energy consumption, and high carbon emissions include smelting and pressing of ferrous metals, petroleum processing and coking, raw chemical materials and chemical products, etc. Combining Tangshan's coal resource endowment, the development of renewable energy, and introducing advanced technology can effectively reduce carbon emissions and increase the diversity of energy supply. We analyzed the cost-benefit of the energy system and found that the implementation of carbon capture and storage technology could effectively reduce carbon emissions by 219.62 Mt and increased costs by 563.12 billion yuan. The research could provide a scientific basis for the medium and long-term energy sustainable transformation of resource-based cities, especially for resource-based cities in urban agglomerations. [ABSTRACT FROM AUTHOR]

This paper develops an interval multi-stage robust programming model for regional low-carbon electric power system planning with energy-water nexus. The model integrates interval programming and multi-stage stochastic programming to handle uncertainties with different probability distribution information. In addition, under the framework of robust programming, the optimal solutions could provide tradeoff information between system cost and decision maker's risk preference. The hybrid programming method is tailored for a case study of Shandong Province, China to validate its applicability and provide useful managerial insights, where energy-water interactions are gain great concerns. The model helps to make robust strategies associated with risk control for capacity expansion, carbon capture and storage (CCS) technology retrofitting, power generation, imported electricity, and water allocation for electricity generation, with the goal of minimizing total cost. Besides, considering the variation of water resource conditions and environmental policies under climate change, the impacts of water resource availability and carbon emission caps on regional electric power system planning are explored and discussed. It is found that stricter carbon emission cap policy would facilitate the investment on CCS devices but have little impact on the installed capacity structure, meanwhile it would increase water consumption per power generation. However, scarcer water resource situation would promote the development of renewable energy generation, and limit the investment on CCS technology. • Low-carbon power system management optimization with energy-water nexus under uncertainties. • Impact analysis of various water resource availability and carbon emission cap on optimal strategies. • Useful managerial insights on energy system planning from energy-water nexus perspective. • Robust solutions with risk-cost tradeoff information to reflect different risk preferences. [ABSTRACT FROM AUTHOR]