Urban Carbon Emissions Data from China's National Cultural Consumption Pilot Program (CCPP)

Title: "Urban Carbon Emissions Dataset: Impact Assessment of China’s National Cultural Consumption Pilot Program (CCPP) (2015-2023)" Description: This dataset includes carbon emissions data from 284 prefecture-level and above cities in China from 2015 to 2023. It is used to assess the impact of China’s National Cultural and Tourism Consumption Pilot Program (CCPP) on urban carbon emissions. The dataset covers per capita CO2 emissions, economic and social variables such as industrial structure, fiscal status, and population characteristics at the city level. The data has been analyzed using a dynamic difference-in-differences (DID) method to explore the effects of cultural consumption policies on carbon emission reductions, with a focus on regions with a higher proportion of the service sector and greater digitalization. Variable Definitions: CO2_per: Per capita carbon emissions (unit: tons per person) treatpost: Whether the city participated in the CCPP (0 for non-participating, 1 for participating) lngdp: The logarithm of the city's GDP (unit: RMB) pop: Total population of the city (unit: millions) fiscal: Ratio of local fiscal expenditure to GDP structure: Proportion of the tertiary industry in the city's GDP (unit: %) hum: Proportion of human resources investment in fiscal expenditure (unit: %) science: Proportion of research and development expenditure in fiscal expenditure (unit: %) Data Interpretation: This dataset is designed to analyze the environmental impact of China's cultural consumption policies, particularly in reducing carbon emissions. It offers valuable insights into how cultural consumption policies can drive green innovation and the development of low-carbon technologies, contributing to the reduction of urban carbon footprints. Additionally, the dataset provides a basis for examining how service-sector composition and digitalization amplify the effects of the policy. Usage Notes: The dataset has been cleaned and processed, and all data has been standardized, making it suitable for further statistical analysis and modeling. Please ensure proper citation of this dataset and reference it using the provided DOI. For more detailed information and data structure, please refer to the README file.