Purpose Carbon trading mechanism has been adopted to foster the green transformation of the economy on a global scale, but its effectiveness for the power industry remains controversial. Given that energy-related greenhouse gas emissions account for most of all anthropogenic emissions, this paper aims to evaluate the effectiveness of this trading mechanism at the plant level to support relevant decision-making and mechanism design. Design/methodology/approach This paper constructs a novel spatiotemporal data set by matching satellite-based high-resolution (1 × 1 km) CO2 and PM2.5 emission data with accurate geolocation of power plants. It then applies a difference-in-differences model to analyse the impact of carbon trading mechanism on emission reduction for the power industry in China from 2007 to 2016. Findings Results suggest that the carbon trading mechanism induces 2.7% of CO2 emission reduction and 6.7% of PM2.5 emission reduction in power plants in pilot areas on average. However, the reduction effect is significant only in coal-fired power plants but not in gas-fired power plants. Besides, the reduction effect is significant for power plants operated with different technologies and is more pronounced for those with outdated production technology, indicating the strong potential for green development of backward power plants. The reduction effect is also more intense for power plants without affiliation relationships than those affiliated with particular manufacturers. Originality/value This paper identifies the causal relationship between the carbon trading mechanism and emission reduction in the power industry by providing an innovative methodology for identifying plant-level emissions based on high-resolution satellite data, which has been practically absent in previous studies. It serves as a reference for stakeholders involved in detailed policy formulation and execution, including policymakers, power plant managers and green investors.

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Electrocatalytic CO2-reduction technology can convert CO2 into methanol and other chemicals using renewable electricity, but the techno-economic prospects of the large-scale electrocatalytic reduction in CO2 into methanol are not clear. This paper conducted sensitivity analysis to confirm the key parameters affecting the cost of methanol production from an alkaline flow cell and a neutral MEA electrolyzer, compared the cost of the two electrolyzers under laboratory data and optimized data scenarios, and analyzed the key parameter requirements of the two electrocatalytic systems to achieve profitable methanol production. The results show that electricity price, Faradaic efficiency, cell voltage, and crossover/carbonate formation ratio are the most sensitive parameters affecting the cost of methanol production. The alkaline flow cell had higher energy efficiency than the MEA cell, but the saving cost of electricity and the eletrolyzer cannot cover the cost of the regeneration of the electrolyte and CO2 lost to carbonate/bicarbonate, resulting in higher methanol production costs than the MEA cell. When the crossover/carbonate formation ratio is zero, the cost of methanol production in an alkaline flow cell and a neutral MEA cell can reach under 400 USD/tonne in the cases of energy efficiency more than 70% and 50%, respectively. Therefore, enhancing energy efficiency and ensuring a low crossover/carbonate formation ratio is important for improving the economy of electrocatalytic methanol production from CO2 reduction. Finally, suggestions on the development of electrocatalytic CO2 reduction into methanol in the future were proposed.