数字经济如何提升全要素碳效率——基于市场扭曲视角研究

doi:10.12118/j.issn.1000-6060.2025.557

摘要/Abstract

摘要：

为了从市场扭曲视角揭示数字经济提升全要素碳效率的新机制，基于2011—2021年中国282个地级市的面板数据，构建了因果中介模型、动态空间面板杜宾模型、门槛效应模型和分位数回归模型。结果表明：（1）数字经济显著地提升全要素碳效率，在经过容忍“不外生工具变量”、双重差分内生性检验、稳健性检验后，该结论依然成立。（2）机制分析发现，缓解产品市场扭曲是数字经济提升全要素碳效率的重要渠道，在经过敏感性分析后，中介效应依然成立。（3）对于传导机制的空间溢出效应，数字经济不仅显著促进相邻城市的全要素碳效率提升，还缓解了相邻城市的产品市场扭曲。（4）对于传导机制的异质性，碳效率水平越高的地区，数字经济的碳效率提升效应越高；随着产品市场扭曲程度加剧，数字经济对产品市场扭曲的抑制作用呈“先强化、后减缓”的倒“U”型影响。（5）数字经济对碳效率的促进存在双门槛效应，表现为先提升、后抑制、再促进的“N”型影响。该传导机制的全景式揭示不仅为系统理解数字经济的碳效率提升作用提供新的视角，也为推进碳达峰碳中和提供新的路径和政策启示。

关键词: 数字经济, 全要素碳效率, 市场扭曲, 空间溢出效应, 因果中介模型, 双重差分模型

Abstract:

This study analyzes the mechanism through which the digital economy enhances total factor carbon efficiency from the perspective of market distortions, constructing causal mediation, dynamic spatial panel Durbin, threshold effect, and a quantile regression models using panel data from 282 prefecture-level cities in China from 2011 to 2021. Five relevant results emerge. (1) The digital economy significantly enhances carbon efficiency, and this conclusion remains robust following incomplete exogeneity instrumental variable tests, difference-in-differences estimation endogeneity tests, and multiple robustness tests. (2) Mechanism analysis validates reduced distortions in the product market as a primary pathway through which the digital economy enhances carbon efficiency. Subsequent sensitivity analysis confirms the robustness of this mediating effect. (3) Regarding the spatial spillover effects of the transmission mechanism, the digital economy significantly enhances total factor carbon efficiency in neighboring cities and alleviates product market distortions in these regions. (4) Concerning the heterogeneity of transmission mechanisms, the digital economy’s impact on improving carbon efficiency becomes more pronounced as a region’s carbon efficiency increases. Furthermore, as the degree of product market distortion rises, the digital economy’s effect on mitigating this distortion follows an inverted U-shaped pattern, initially intensifying before gradually weakening. (5) The digital economy’s impact on carbon efficiency exhibits an N-shaped double-threshold effect of first enhancing, then inhibiting, and subsequently promoting efficiency. This comprehensive mechanism analysis provides a novel perspective for systematically understanding the digital economy’s influence on enhancing carbon efficiency and reveals valuable pathways and policy insights for advancing carbon peak and neutrality goals.

Key words: digital economy, total factor carbon efficiency, market distortions, spillover effect, causal mediation effect, difference-in-differences model

杨晴, 张金茹, 郝嘉怡, 邹绍辉, 张磊. 数字经济如何提升全要素碳效率——基于市场扭曲视角研究[J]. 干旱区地理, 2026, 49(9): 1977-1994.

YANG Qing, ZHANG Jinru, HAO Jiayi, ZOU Shaohui, ZHANG Lei. How does the digital economy enhance total factor carbon efficiency: A study from the perspective of market distortions[J]. Arid Land Geography, 2026, 49(9): 1977-1994.

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变量	全要素碳效率
变量	（1）	（2）	（3）	（4）	（5）
dige	0.011^***（0.002）	0.047^***（0.002）	0.005^***（0.002）	0.025^***（0.002）	0.006^***（0.002）
常数项	0.583^***（0.002）	0.685^***（0.004）	0.782^**（0.013）	0.631^***（0.008）	0.813^***（0.025）
R²	0.015	0.364	0.838	0.532	0.842
调整后R²	0.015	0.362	0.821	0.530	0.826
控制变量	否	否	否	是	是
地区效应	否	否	是	否	是
时间效应	否	是	是	是	是

平均效应	置信区间	中介变量：产品市场扭曲
平均因果中介效应	[0.001, 0.003]	0.002
平均直接效应	[0.042, 0.049]	0.045
平均处理效应	[0.044, 0.050]	0.047

变量	SDM			SAR
变量	W1	W2	W3	W1	W2	W3
dige	0.024^***（0.002）	0.020^***（0.002）	0.024^***（0.002）	0.025^***（0.002）	0.023^***（0.002）	0.025^***（0.002）
W*dige	0.015^**（0.006）	0.020^***（0.006）	0.016^**（0.007）	-	-	-
直接效应	0.024^***（0.002）	0.022^***（0.002）	0.024^***（0.002）	0.025^***（0.002）	0.024^***（0.002）	0.025^***（0.002）
间接效应	0.023^***（0.008）	0.036^***（0.008）	0.024^***（0.008）	0.005^***（0.001）	0.009^***（0.001）	0.005^***（0.001）
总效应	0.047^***（0.008）	0.058^***（0.008）	0.048^***（0.008）	0.030^***（0.003）	0.033^***（0.003）	0.030^***（0.003）
rho	0.182^***（0.034）	0.300^***（0.030）	0.183^***（0.034）	0.161^***（0.030）	0.286^***（0.027）	0.161^***（0.030）
R²	0.551	0.570	0.551	0.544	0.545	0.544
控制变量	是	是	是	是	是	是
地区效应	否	否	否	否	否	否
时间效应	是	是	是	是	是	是

变量	SDM			SAR
变量	W1	W2	W3	W1	W2	W3
dige	-0.719^***（0.079）	-0.698^***（0.086）	-0.719^***（0.080）	-1.087^***（0.068）	-1.020^***（0.067）	-1.087^***（0.068）
W*dige	-1.669^***（0.194）	-1.105^***（0.183）	-1.673^***（0.195）	-	-	-
直接效应	-0.733^***（0.081）	-0.735^***（0.085）	-0.732^***（0.081）	-1.088^***（0.070）	-1.028^***（0.069）	-1.088^***（0.070）
间接效应	-1.837^***（0.191）	-1.517^***（0.198）	-1.842^***（0.192）	-0.198^***（0.046）	-0.331^***（0.050）	-0.120^***（0.046）
总效应	-2.570^***（0.160）	-2.252^***（0.163）	-2.574^***（0.161）	-1.287^***（0.085）	-1.359^***（0.094）	-1.287^***（0.085）
rho	0.065^*（0.035）	0.193^***（0.032）	0.065^*（0.035）	0.158^***（0.033）	0.252^***（0.031）	0.158^***（0.033）
R²	0.207	0.198	0.207	0.180	0.183	0.180
控制变量	否	否	否	否	否	否
地区效应	否	否	否	否	否	否
时间效应	是	是	是	是	是	是

变量	10%分位点	30%分位点	50%分位点	70%分位点	90%分位点	东部	中部	西部
dige	0.010^*** （0.003）	0.014^*** （0.002）	0.015^*** （0.003）	0.023^*** （0.004）	0.031^*** （0.005）	0.015^*** （0.004）	0.005^* （0.003）	0.001 （0.004）
常数项	0.586^*** （0.006）	0.606^*** （0.009）	0.614^*** （0.008）	0.625^*** （0.009）	0.630^*** （0.017）	0.828^*** （0.049）	0.650^*** （0.024）	0.646^*** （0.032）
R²	0.241	0.294	0.332	0.371	0.447	0.858	0.823	0.742
控制变量	是	是	是	是	是	是	是	是
地区效应	否	否	否	否	否	是	是	是
时间效应	是	是	是	是	是	是	是	是