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Planning power system (PS) towards renewable energy is crucial for reducing carbon dioxide (CO 2) emission as well as mitigating climate change. However, the inherent uncertainty and hierarchical structure of PS pose intractable challenges to the planning process. This study develops a multi-GCM-based interval bi-level programming (MIBP) method that can balance two-level decision-making conflict between CO 2 -emisison reduction and system cost minimization. MIBP can also deal with uncertainties expressed as interval values in objective functions and constraints. Then, a MIBP-PS model is formulated to plan PS for China (2021–2050), where the upper-level objective is to minimize CO 2 emission and the lower-level objective is to minimize the system cost. The major findings are: (i) the CO 2 emission for PS would reach peak value before 2030 and then reduce by [10.2, 20.4]% in 2050, which is mainly owing to the curtailment of fossil fuel; (ii) compared with the conventional model, CO 2 emission from MIBP-PS model could decrease by [3.1, 4.8]%, which shows the superiority of the developed model; (iii) the fossil-fuel power would be gradually replaced by renewable energy, and the proportion of coal-fired power would decrease by 38.8% by 2050, especially small coal-fired facilities (i.e., below 0.3 GW) should be gradually phased down. [Display omitted] [ABSTRACT FROM AUTHOR]

Abstract: Characterizing the carbon footprint (CF) of agricultural production offers key information for pursuing low carbon agriculture and food consumption. While China has long strived for increasing food production capacity for its large and still growing population, the high emissions cost, especially from the over use of agro-chemicals, has been widely debated for the last decade. However, the CF of China''s crop production has not yet been assessed. This paper reports a basic estimate of CF of crop production using national statistical data available for the period of 1993–2007. The dataset includes the amount of individual agricultural inputs (fertilizer, pesticide, diesel, plastic film, etc.), cultivation area and total of production whole crops. Using the emission factors estimated for China''s agricultural features and available abroad, the mean overall CF of China''s crop production was estimated to be 0.78±0.08tCEha−1 yr−1 and 0.11±0.01tCEt−1 yr−1, for land use and bulk production respectively. For the duration the data covered, the carbon intensity under cultivation land use was seen to increase since 1993. Among the total, fertilizer induced emissions exerted the largest contribution of ∼60%, being 0.45±0.04tCE per ha and 0.07±0.01tCE per ton of production, on average. Compared to the UK, the estimated overall CF of China''s crop production was higher in terms of cultivation land use. While there was a significant positive correlation of carbon intensity with total production, carbon efficiency was shown in a decreasing trend during 2003-2007. Therefore, low carbon agriculture should be pursued, and the priority should be given to reducing fertilizer application in agriculture of China. However, for developing best management practices for climate change mitigation in crop production of China, further studies of crop and regional specific CFs and the variation with climate conditions and agricultural managements are needed. [Copyright &y& Elsevier]

• A multi-GCM ensemble simulation and optimization approach is developed. • The developed approach can effectively tackle multiple uncertainties. • The impact of climate change on energy-economy-carbon nexus system is investigated. • Renewable energy will gradually replace fossil fuel for mitigating climate change. • There is a tradeoff between carbon dioxide emission and system cost. Climate change mitigation by reducing carbon dioxide emission becomes one of the major challenges for energy systems. In this study, a multi-GCM ensemble simulation and optimization approach is developed for analyzing the impact of climate change on China's energy-economy-carbon nexus system under multiple uncertainties through integrating techniques of multiple global climate models, support-vector-regression, Monte Carlo simulation, and interval chance-constrained programming within a general framework. The developed approach can tackle multiple uncertainties existed in global climate models, random carbon dioxide emission and complex optimization process. Results disclose that (i) the national electricity demand would grow around 58.6% in the next 30 years under climate change; (ii) for climate change mitigation and sustainable development, fossil fuel would be gradually replaced by renewable energy (i.e. the share of fossil fuel to the total energy supply decreasing 22.5% and the share of electricity generated from renewable energy increasing 27.0% by 2050); (iii) compared to the peak value in 2030, carbon dioxide emission would reduce 15.1% by 2050, most reduction from coal-fired power generation; (iv) there is a tradeoff between carbon dioxide emission and system cost as p-level decreases from 0.15 to 0.01 (i.e. carbon dioxide emission can reduce 1.9% with 3.7% of raised system cost). It is desired for China to adjust its current energy supply structure for reducing carbon emission and seeking a low-carbon developmental path. The most effective way is to eliminate some small coal-fired units. A number of wind-power and solar-power projects are expected to be implemented due to their abundant potential markets. [ABSTRACT FROM AUTHOR]