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• We investigate the integration cost of wind and solar on both the power supply and demand sides. • The integration costs from both sides are increasing with the penetration of variable renewable electricity. • Consumers will bear more cost of variable renewable electricity integration than power system. • Solar power has a lower integration cost than wind power. • Blindly reducing the renewable energy curtailment rate may not be cost-effective. Variable renewable electricity (VRE) is expected to play an important role in global decarbonization. However, due to VRE's intermittency and uncertainty, integrating VRE into the power system may cause additional integration costs for both power systems and consumers. Estimating these integration costs can provide insights for power system planning, support policy making and power market designs. In this paper, the integration costs of wind and solar on both demand and supply sides are quantitatively investigated by an economic power dispatch model combined with Monte Carlo simulation. A case study of Guangdong Province in China is conducted under three scenarios of power mix changes (only increasing the VRE installation; replacing the existing coal-fired power; and replacing the existing coal-fired power without power storage). The results show that integration costs from both supply and demand sides are non-negligible when VRE replaces existing capacity, and the costs are increasing with the penetration rate of VRE. On supply side, it ranges from −2.3 to 12.1 $/MWh for wind penetration from 5% to 30%, and −5.5 to 7.1 $/MWh for that of solar penetration. Moreover, consumers on demand side will bear −7.3 to 185.9 $/MWh integration costs for wind and solar integration, which is much higher than the supply side. In addition, solar power has a lower integration cost than wind power due to a more consistent power output with the load curve of power demand on the time scale. Moreover, our results reveal that blindly reducing the renewable energy curtailment rate may not be cost-effective. [ABSTRACT FROM AUTHOR]

The aviation industry is one of the key areas to achieve carbon neutrality due to its difficulty in fuel substitution. In developing countries, aviation industry will be in a period of rapid growth for a long time, which means that it will be more affected by the Carbon Offsetting and Reduction Scheme for International Aviation (CORSIA). Therefore, aviation carbon emission reduction in developing countries needs to be paid more attention. This study utilizes the full sample data of global developing countries' fight-level information of June 2018, including a total of 1,048,655 flight data, to account for the aviation fuel consumption, CO 2 emissions, and emission intensity of 109 developing countries based on 70 aircraft types. The impacts of aircraft structure and flight pattern on energy efficiency and emission intensity are explored by combining micro fight-level aviation data with spatial index decomposition method. The results show that the average emission intensity of developing countries is actually better than that of developed countries. Most of them are concentrated in the range of 65–85 gCO 2 per seat-kilometers. The emission reduction potential of traditional measures in developing countries can theoretically close to 20%, of which about three-fifths comes from the adjustment of aircraft structure, while the remainder comes from the improvement in flight pattern. There is still a lot of room for structural adjustment of long-haul large-sized aircraft in developing countries by avoiding less efficient four-engine aircrafts and choosing more efficient ones in terms of new fleet deployments. But the potential for restructuring of medium-sized aircraft is limited, thus needing to further improve efficiency by increasing flight distance. Large-sized aircraft should be avoided as much as possible within the range that medium-sized aircraft can reach, which is crucial in route planning in many developing countries. • Full sample data of global developing countries' fight-level information is used. • Aviation fuel consumption, CO 2 emissions, and emission intensity are accounted for. • Flight-level aviation data is combined with the spatial index decomposition method. • Impacts of aircraft structure and flight pattern on energy efficiency are explored. [ABSTRACT FROM AUTHOR]