@article{liu_economic_2026,
	title = {Economic growth, industrial concentration, and carbon emissions in the textile industry},
	issn = {1222-5347},
	url = {https://revistaindustriatextila.ro/images/2026/2/010%20FENG%20LIU_INDUSTRIA%20TEXTILA%20no.2_2026.pdf},
	doi = {10.35530/IT.077.02.202566},
	abstract = {Against the backdrop of global “dual-carbon” goals and China’s economic transformation, the textile industry, as a key
high-emission sector, has attracted much attention regarding the dynamic correlations between its carbon emissions,
economic growth, and industrial concentration. Based on China’s textile industry data from 2001 to 2020, this paper
constructs a Vector Autoregression (VAR) model and systematically explores the interactive relationships among the
three through methods such as the Granger causality test, the cointegration test, impulse response, and variance
decomposition. The findings are as follows: the Granger causality test shows that only the growth rate of carbon
emissions in the textile industry (D\_CO2) is a significant Granger cause of changes in industrial concentration (D\_IC),
while there is no significant causal relationship between other variables, indicating that changes in carbon emissions
have a one-way driving effect on the adjustment of industrial concentration; the unrestricted cointegration test indicates
that there is one long-term cointegration relationship among the three; in the long-term equilibrium, D\_IC has a
significant impact on D\_CO2, reflecting that the improvement of industrial concentration can effectively curb carbon
emissions; impulse response analysis shows that the response of D\_CO2 to its own shock is significant in the short term,
and the impact of D\_IC shock on it is transient; D\_IC shows a positive response to D\_CO
₂ shock, while the impact of
its own shock is weak; the response of D\_ISV (growth rate of economic growth) to D\_CO2 shock lasts longer, and the
dynamic interaction among variables is centered on D\_CO2; the variance decomposition results show that the long-term
explanatory power of D\_CO2 to D\_IC reaches 48.85\%, but the explanatory power of D\_IC to D\_CO2 is only 11.75\%; the
impact of D\_IC on D\_ISV (11.63\%) is stronger than that of D\_CO2 (7.32\%); in the long run, the forecast error variances
of the three variables are mainly dominated by their own shocks (about 80\% for D\_CO2 and D\_ISV, and about 50\% for
D\_IC), and the system eventually tends to equilibrium. The study reveals the key role of carbon emissions in the textile
industry in adjusting industrial concentration, providing empirical evidence for coordinating industrial concentration and
carbon emission governance through policy guidance.},
	urldate = {2026-05-17},
	journal = {Industria Textila},
	author = {Liu, Feng and Zou, Fei},
	month = may,
	year = {2026},
	pages = {279},
}