Innovating through the storm: Green technological persistence under climate governance in textile firms

Variable	Definition	Mean value	SD	Min	Max	N
PGI	Persistent green innovation	0.0455	0.0864	0.0000	0.8438	2,908
CRC	Climate-resilient city policy	0.0144	0.1193	0.0000	1.0000	2,908
Size	Firm size	22.0761	1.0413	19.5245	26.4403	2,908
FirmAge	Firm age	2.9832	0.2879	1.6094	3.8712	2,908
Lev	Leverage	0.3874	0.1832	0.0463	0.9077	2,908
ROA	Profitability	0.0353	0.0670	−0.5562	0.2223	2,908
CashFlow	Operating cash flow	0.0563	0.0658	−0.2015	0.2669	2,908
SOE	State ownership	0.2552	0.4360	0.0000	1.0000	2,908
Indep	Board independence	0.3754	0.0537	0.3333	0.6000	2,908
ATO	Total asset turnover	0.6625	0.3239	0.0496	2.6402	2,908
HHI	Industry concentration	0.0498	0.0554	0.0161	0.3717	2,908

Table 2

Baseline regression results.

Variables	(1)	(2)
Variables	PGI	PGI
CRC	0.149***	0.139***
	(0.048)	(0.048)
Size		0.027***
		(0.005)
FirmAge		−0.088**
		(0.041)
Lev		−0.010
		(0.017)
ROA		0.028
		(0.023)
CashFlow		0.071***
		(0.021)
SOE		0.009
		(0.014)
Indep		0.020
		(0.039)
ATO		0.006
		(0.009)
HHI		0.017
		(0.028)
Constant	0.043***	−0.307*
	(0.001)	(0.157)
Firm FE	YES	YES
Time FE	YES	YES
Obs	2,908	2,908
R ²	0.563	0.578

Note: ***, **, and * denote significance at the 1, 5, and 10% levels, respectively. Firm-clustered standard errors are reported in parentheses.

Table 3

Mechanism tests.

Variables	(1)	(2)
Variables	MgtAtt	EnvCap
CRC	0.262***	0.656**
	(0.094)	(0.293)
Constant	2.426**	−19.163***
	(1.213)	(6.619)
Controls	YES	YES
Firm FE	YES	YES
Time FE	YES	YES
Obs	2,908	2,662
R ²	0.873	0.576

Note: ***, **, and * denote significance at the 1, 5, and 10% levels, respectively. Firm-clustered standard errors are reported in parentheses.

Table 4

Extended analysis.

Variables	(1)	(2)
	Green innovation quantity	Green innovation quality
	GIQ	GIQL
CRC	0.525***	0.004
	(0.197)	(0.177)
Constant	−2.894**	−2.132*
	(1.431)	(1.208)
Controls	YES	YES
Firm FE	YES	YES
Time FE	YES	YES
Obs	2,908	2,907
R ²	0.627	0.685

Note: ***, **, and * denote significance at the 1, 5, and 10% levels, respectively. Firm-clustered standard errors are reported in parentheses.

Table A1

Event-study coefficients.

Variables	PGI
pre_4	0.025
	(0.032)
pre_3	0.009
	(0.035)
pre_2	0.019
	(0.034)
current	0.052
	(0.042)
post_1	0.283***
	(0.088)
post_2	0.222***
	(0.081)
post_3	0.375***
	(0.127)
post_4	0.108**
	(0.045)
post_5	0.170
	(0.115)
post_6	0.116*
	(0.068)
Controls	YES
Firm FE	YES
Time FE	YES
Obs	2,908
R ²	0.593

Note: ***, **, and * denote significance at the 1, 5, and 10% levels, respectively. Firm-clustered standard errors are reported in parentheses. The omitted reference period is 2016 (k = −1), and the joint test of the pre-policy coefficients yields p = 0.222.

Table A2

Robustness tests I.

Variables	(1)	(2)	(3)
	City-clustered standard errors	One-period lagged controls	City clustering and lagged controls
	PGI	PGI	PGI
CRC	0.139**	0.133***	0.133**
	(0.057)	(0.047)	(0.056)
Constant	−0.307*	−0.333**	−0.333*
	(0.174)	(0.151)	(0.170)
Controls	YES	YES	YES
Firm FE	YES	YES	YES
Time FE	YES	YES	YES
Obs	2,908	2,908	2,908
R ²	0.578	0.575	0.575

Note: ***, **, and * denote significance at the 1, 5, and 10% levels, respectively. City-clustered standard errors are reported in parentheses in columns (1) and (3), and firm-clustered standard errors are reported in column (2).

Table A3

Robustness tests II.

Variables	(1)	(2)	(3)	(4)
	Excluding municipalities	Province-specific linear trends	Province-by-year fixed effects	Pre-policy city characteristics by year
	PGI	PGI	PGI	PGI
CRC	0.186***	0.194***	0.225***	0.159***
	(0.059)	(0.045)	(0.045)	(0.046)
Constant	−0.349**	−0.305*	−0.316**	0.141
	(0.176)	(0.156)	(0.155)	(0.747)
Controls	YES	YES	YES	YES
Baseline × Year	—	—	—	YES
Firm FE	YES	YES	YES	YES
Time FE	YES	YES	YES	YES
Obs	2,620	2,908	2,846	2,847
R ²	0.571	0.597	0.644	0.597

Note: ***, **, and * denote significance at the 1, 5, and 10% levels, respectively. Firm-clustered standard errors are reported in parentheses. The Baseline × Year variables in column (4) include the number of green patents (GPat × Year), number of extreme high-temperature days (ExtHot × Year), number of extreme low-temperature days (ExtCold × Year), precipitation (Precip × Year), GDP per capita (PGDP × Year), and the share of secondary industry (SecShare × Year).

Note: Year denotes the set of year indicators, and the terms represent interactions with the corresponding pre-policy city characteristics.

Table A4

Determinants of pilot selection.

Variables	CRC
GPat	0.131
	(0.091)
ExtHot	0.103
	(0.083)
ExtCold	−0.054
	(0.033)
Precip	−0.027
	(0.028)
PGDP	−0.130
	(0.261)
SecShare	−0.004
	(0.014)
Constant	−2.359
	(3.711)
Obs	285

Note: ***, **, and * denote significance at the 1, 5, and 10% levels, respectively. Robust standard errors are reported in parentheses.

Table A5

Controlling for contemporaneous environmental policies.

Variables	(1)	(2)	(3)	(4)
	Controlling for environmental vertical reform	Controlling for the low-carbon city pilot program	Controlling for green finance pilot zones	Controlling for all three policies
	PGI	PGI	PGI	PGI
CRC	0.139***	0.138***	0.155***	0.153***
	(0.048)	(0.045)	(0.052)	(0.049)
EVR	−0.004			−0.004
	(0.007)			(0.007)
LCP		0.022*		0.022*
		(0.011)		(0.012)
GFZ			−0.004	−0.005
			(0.013)	(0.012)
Constant	−0.304*	−0.353**	−0.277*	−0.289*
	(0.156)	(0.157)	(0.161)	(0.161)
Controls	YES	YES	YES	YES
Firm FE	YES	YES	YES	YES
Time FE	YES	YES	YES	YES
Obs	2,908	2,857	2,808	2,808
R ²	0.578	0.577	0.578	0.580

Note: ***, **, and * denote significance at the 1, 5, and 10% levels, respectively. Firm-clustered standard errors are reported in parentheses.

References

Authors

Metrics

Articles in this issue

DOI: https://doi.org/10.2478/ftee-2026-0011 | Journal eISSN: 2300-7354 | Journal ISSN: 1230-3666

Journal RSS Feed

Language: English

Page range: 130 - 145

Submitted on: Mar 26, 2026

Accepted on: Sep 14, 2026

Published on: Nov 3, 2026

Published by: Łukasiewicz Research Network-Łódź Institute of Technology

In partnership with: Paradigm Publishing Services

Publication frequency: Volume open

Keywords:

Climate governance,

Climate-resilient cities,

Persistent green innovation,

Textile industry

Related subjects:

Business and economics,

Business management,

Business informatics,

Process management,

Industrial chemistry,

Cellulose, paper and textiles,

Polymer science and technology,

Materials sciences,

Biomaterials and natural materials

© 2026 Hong Yu, Han Yan, published by Łukasiewicz Research Network-Łódź Institute of Technology
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Volume 34 (2026): Issue 1 (February 2026)

Innovating through the storm: Green technological persistence under climate governance in textile firms

Figure 1

Table 1

Table 2

Figure 2

Figure 3

Table 3

Figure 4

Table 4

Table A1

Table A2

Table A3

Table A4

Table A5

Paradigm

My account