Correction: Wu et al. (2026). How Can New Quality Productive Forces Reshape the Industrial Landscape?—The Dual Enabling Effects of Factor Endowments and Synergies. Economies, 14(3), 83

In the original publication (Wu et al., 2026), a error occurred in Table 3. Specifically, the explanatory note at the bottom of the table was missing in the published version. The complete and corrected version of Table 3 is presented below.

Table 3. Levels of new quality productive forces in China.

Province	fe	fs	nqp	Sorting Changes
Shanxi	0.057	0.118	0.007	4
Hebei	0.077	0.139	0.011	2
Anhui	0.094	0.200	0.015	2
Inner Mongolia	0.053	0.104	0.006	1
Liaoning	0.088	0.150	0.014	1
Hubei	0.095	0.156	0.016	1
Hunan	0.087	0.145	0.014	1
Guizhou	0.057	0.111	0.007	1
Shaanxi	0.085	0.145	0.013	1
Beijing	0.352	0.263	0.095	0
Tianjin	0.112	0.164	0.019	0
Jilin	0.064	0.122	0.008	0
Heilongjiang	0.059	0.115	0.007	0
Shanghai Municipality	0.239	0.235	0.058	0
Jiangsu	0.286	0.252	0.077	0
Zhejiang	0.203	0.222	0.048	0
Fujian	0.136	0.173	0.025	0
Jiangxi	0.074	0.131	0.010	0
Shandong	0.157	0.189	0.032	0
Henan	0.102	0.154	0.017	0
Guangdong	0.381	0.276	0.114	0
Guangxi Zhuang Autonomous Region	0.067	0.117	0.008	0
Chongqing Municipality	0.078	0.140	0.011	0
Sichuan	0.095	0.150	0.015	0
Xinjiang Uygur Autonomous Region	0.049	0.094	0.005	0
Yunnan	0.058	0.108	0.007	−1
Ningxia Hui Autonomous Region	0.053	0.101	0.006	−1
Hainan	0.082	0.122	0.011	−2
Qinghai	0.060	0.090	0.006	−4
Gansu	0.098	0.109	0.011	−6
Average	0.117	0.153	0.023

Note: In Table 3, columns fe, fs, and nqp present the provincial averages of each variable over the sample period, where ${fe}_i={\sum }_{t=2013}^{2024}{fe}_{it}$, ${fs}_i={\sum }_{t=2013}^{2024}{fs}_{it}$, and ${nqp}_i={\displaystyle \frac{1}{12}}{\sum }_{t=2013}^{2024}\left({fe}_{it}\ast {fs}_{it}\right)$.

Table 3. Levels of new quality productive forces in China.

Province	fe	fs	nqp	Sorting Changes
Shanxi	0.057	0.118	0.007	4
Hebei	0.077	0.139	0.011	2
Anhui	0.094	0.200	0.015	2
Inner Mongolia	0.053	0.104	0.006	1
Liaoning	0.088	0.150	0.014	1
Hubei	0.095	0.156	0.016	1
Hunan	0.087	0.145	0.014	1
Guizhou	0.057	0.111	0.007	1
Shaanxi	0.085	0.145	0.013	1
Beijing	0.352	0.263	0.095	0
Tianjin	0.112	0.164	0.019	0
Jilin	0.064	0.122	0.008	0
Heilongjiang	0.059	0.115	0.007	0
Shanghai Municipality	0.239	0.235	0.058	0
Jiangsu	0.286	0.252	0.077	0
Zhejiang	0.203	0.222	0.048	0
Fujian	0.136	0.173	0.025	0
Jiangxi	0.074	0.131	0.010	0
Shandong	0.157	0.189	0.032	0
Henan	0.102	0.154	0.017	0
Guangdong	0.381	0.276	0.114	0
Guangxi Zhuang Autonomous Region	0.067	0.117	0.008	0
Chongqing Municipality	0.078	0.140	0.011	0
Sichuan	0.095	0.150	0.015	0
Xinjiang Uygur Autonomous Region	0.049	0.094	0.005	0
Yunnan	0.058	0.108	0.007	−1
Ningxia Hui Autonomous Region	0.053	0.101	0.006	−1
Hainan	0.082	0.122	0.011	−2
Qinghai	0.060	0.090	0.006	−4
Gansu	0.098	0.109	0.011	−6
Average	0.117	0.153	0.023

Note: In Table 3, columns fe, fs, and nqp present the provincial averages of each variable over the sample period, where ${fe}_i={\sum }_{t=2013}^{2024}{fe}_{it}$, ${fs}_i={\sum }_{t=2013}^{2024}{fs}_{it}$, and ${nqp}_i={\displaystyle \frac{1}{12}}{\sum }_{t=2013}^{2024}\left({fe}_{it}\ast {fs}_{it}\right)$.

The authors state that the scientific conclusions are unaffected. This correction was approved by the Academic Editor. The original publication has also been updated.