Panel Data Digital Economy Environmental Regulation Coupling Coordination China Provinces 2011-2023
Description
This dataset provides the provincial-level panel data for China from 2011 to 2023, including indicators for the digital economy, environmental regulation, and their coupling coordination degree used in the study of regional carbon productivity.
Files
Steps to reproduce
1. Software & Environment Setup All empirical analyses were conducted using Stata (Version 16 or higher is strongly recommended). Before running the scripts, ensure that the following community-contributed packages are installed in your Stata environment: xsmle, spatwmat, spatgsa, reghdfe, ivreghdfe, and estout. You can install them via the ssc install command. 2. Directory Preparation Download all the provided datasets (Data_*.xlsx and Data_*.dta), spatial weight matrices (Matrix_*.dta), and Stata scripts (Code_*.do). Place all downloaded files into a single, unified working directory on your local machine. 3. Path Modification Open each Code_*.do file. At the top of every script, locate the working directory command (e.g., cd "/Users/.../..."). Modify this path to match the exact location of the folder where you saved the files on your local computer. 4. Execution Sequence To successfully replicate the tables and figures presented in the manuscript, please execute the Stata .do files in the following strict numerical order: Data & Matrix Prep: Run Code_01 (Descriptive Statistics) and Code_02 (Spatial Matrix Construction). Spatial Autocorrelation: Run Code_03 for the Global Moran's I test and Code_04 to generate the high-resolution Local Moran's scatter plots. Baseline SDM & Identification: Run Code_05 for the Likelihood Ratio (LR) tests and the Spatial Durbin Model (SDM) baseline regression. Follow with Code_06 for cross-sectional LM tests and the Hausman test. Mechanism Tests: Run Code_07 (Industrial Structure Upgrading) and Code_08 (Marketization Level) to replicate the spatial mediation regressions. Heterogeneity: Run Code_09 to execute the native matrix slicing and subgroup SDM regressions. Robustness & Endogeneity: Finally, run Code_10 for five sets of robustness checks, and Code_11 for the Shift-Share Instrumental Variable (2SLS) approach. 5. Output Retrieval Upon successful execution, all regression results will be automatically exported and saved as .rtf files (e.g., Table 5, Table 6, etc.) in your working directory. All spatial autocorrelation scatter plots will be automatically exported as publication-ready, high-resolution .tif images.