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China’s 12th Five-Year Plan emphasizes green technological advances in energy conservation, which provides a feasible quasi-natural experimental node to study the role of green technological innovation in influencing the achievement of carbon neutrality. The difference-in-difference model examines whether China’s electricity consumption efficiency has improved since the 12th Five-Year Plan and reveals the role of green technology innovation in this process. Specifically, this paper takes 216 cities in China from 2003 to 2016 as the study sample, the midpoint between China’s 11th and 12th Five-Year Plans as the quasi-natural starting point, and uses the top 50 cities in terms of the number of listed companies as the quasi-natural experimental group. The results show that China’s electricity consumption efficiency has improved significantly since the 12th Five-Year Plan, supported by different robustness tests. The mechanism analysis finds that green technology innovation positively affects energy efficiency but is not the best option for cities with many listed companies. Cities with many listed companies can achieve energy savings by adjusting their industrial structure. Energy conservation and emission reduction policies should be formulated according to the city’s situation and give full play to green technology progress and industrial transformation and upgrading, which is of great significance to achieving carbon neutrality.

The integration of the global economy has led to an increasingly strong connection between the futures and spot markets of commodities. First, based on one-minute high-frequency prices, this paper applies the thermal optimal path (TOP) method to examine the lead-lag relationship between Chinese crude oil futures and spot from March 2018 to December 2021. Second, we apply the Mixed Frequency Data Sampling Regression (MIDAS) model and indicators such as deviation degree to test the degree of prediction of high-frequency prices in the futures market to the spot market. The experimental results show that the futures markets lead the spot market most of the time, but the lead effect reverses when major events occur; 60-minute futures high-frequency prices are the most predictive of daily spot data; crude oil futures’ predictive power declined after the Covid-19 outbreak and is more predictive when night trading is available. This study has important implications, not only to guide investors but also to provide empirical evidence and valid information for policy makers.