Replication Data for: Carbon Trading, Energy Structure Connectivity, and Green Total Factor Energy Efficiency

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🔬研究者:Spatial econometric methods for evaluating carbon pricing policies and spillover effects.

🏢実務担当者:Insights into how carbon trading can affect energy efficiency across regions, informing corporate strategy.

🏛政策担当者:Evidence on the effectiveness and spatial dynamics of ETS, useful for designing carbon pricing mechanisms.

1. Overview This repository contains the dataset and replication code for the manuscript titled "Carbon Trading, Energy Structure Connectivity, and Green Total Factor Energy Efficiency: A Dynamic Spatial Analysis of Structural Change". It provides all the necessary files to replicate the empirical results, including spatial econometric regressions, mechanism analyses, and data visualizations. 2. Data Files Panel Data.csv: The main balanced panel dataset covering 30 Chinese provinces from 2005 to 2022. It includes the dependent variable (Green Total Factor Energy Efficiency, GTFEE), the core independent variable (ETS policy dummy, DID), the energy structure proxy (coal share), and other provincial-level control variables. Weight Matrix.csv: The custom row-standardized Energy Structure Distance Matrix ($W_{struc}$) constructed based on the 2015 coal consumption shares, used to capture the non-geographic "Structural Connectivity" among provinces. 3. Code Files The empirical analysis is conducted using Stata, while the matrix construction and visualizations are handled in Python. Stata Scripts (.do): 01_Data_and_Trend.do: Generates descriptive statistics and conducts the parallel trend test for the DID model. 02_Master_Regressions.do: Executes the baseline Dynamic Spatial Durbin Model (D-SDM) to estimate direct and spillover effects. 03_Spatial_and_Mechanism.do: Performs spatial correlation tests (Moran's I) and the mechanism analysis (testing the "Siphoning-Synergy Paradox"). 04_Robustness_and_IV.do: Contains all robustness checks, including matrix substitution, placebo tests, and the Instrumental Variable (IV-2SLS) estimation. Python Scripts (.py): step2_build_weight_matrix.py: The script used to calculate and construct the energy structure distance matrix from raw data. 05_Python_Plots.py: Generates the figures used in the manuscript (e.g., matrix heatmaps and spatial distribution plots). 4. Software Requirements Stata: Version 16 or higher is recommended for running the .do files (requires spatial econometrics packages like xsmle or spatgsa). Python: Version 3.8 or higher, requiring standard data analysis and visualization libraries (e.g., pandas, numpy, matplotlib, seaborn).

• openalex https://doi.org/10.5281/zenodo.21736007first seen 2026-08-03 04:50:16 · last seen 2026-08-03 04:50:18