







































 
 

 

139 
© 2025 by the author; licensee Asian Online Journal Publishing Group 
 

Economy 
Vol. 12, No. 2, 139-145, 2025 

ISSN(E) 2313-8181/ ISSN(P) 2518-0118 
DOI: 10.20448/economy.v12i2.7458 

© 2025 by the author; licensee Asian Online Journal Publishing Group 

 
 

 
 
 
Revisiting the growth–environment nexus in China: The role of institutions, energy 
use, and ecological stress 

 
Büşra Ağan Çelik  

 

        
 
Department of Economics, OSTİM Technical University, Ankara, Türkiye. 
Email: busra.agancelik@ostimteknik.edu.tr  
 

 
Abstract 

This study investigates the dynamic relationship between income level and key environmental and institutional 
drivers in China, focusing on energy use, ecological footprint, trade openness, and rule of law over the period 
1990–2023. The purpose is to assess how these factors jointly influence China’s growth trajectory under 
increasing sustainability challenges. The analysis employs the Autoregressive Distributed Lag (ARDL) model, 
which captures both short-run adjustments and long-run equilibrium dynamics while accommodating variables 
with mixed integration orders. The empirical results show that ecological footprint, trade openness, and 
institutional quality exert statistically significant short-run effects on GDP, whereas energy use is not significant 
in the short run. The highly significant error correction term confirms rapid adjustment toward equilibrium, 
reflecting the economy’s sensitivity to shocks. In contrast, the long-run effects of energy, environmental, and 
institutional variables are statistically insignificant, suggesting that China’s growth path cannot rely on current 
structures to achieve sustainable outcomes. These findings contribute to the debate on the compatibility of 
economic expansion with environmental sustainability by providing new evidence from the Chinese context. 
Practical implications emphasize the need for structural reforms, including reducing coal dependence, accelerating 
the transition to renewable energy, embedding environmental standards into trade policy, and strengthening 
institutional enforcement mechanisms. The results also offer policy guidance aligned with China’s carbon 
neutrality objectives and Sustainable Development Goals. 
 

Keywords: Ecological footprint, Energy use, Institutional quality, SDGs, ARDL model, China. 

 
Citation | Çelik, B. A. (2025). Revisiting the growth–environment 
nexus in China: The role of institutions, energy use, and ecological 
stress. Economy, 12(2), 139–145. 10.20448/economy.v12i2.7458. 
History:  
Received: 5 August 2025 
Revised: 8 September 2025 
Accepted: 15 September 2025 
Published: 26 September 2025 
Licensed: This work is licensed under a Creative Commons 

Attribution 4.0 License  
Publisher:  Asian Online Journal Publishing Group 
 

Funding: This study received no specific financial support. 
Institutional Review Board Statement: Not applicable. 
Transparency: The author confirms that the manuscript is an honest, accurate, 
and transparent account of the study; that no vital features of the study have 
been omitted; and that any discrepancies from the study as planned have been 
explained. This study followed all ethical practices during writing. 
Competing Interests: The author declares that there are no conflicts of 
interests regarding the publication of this paper. 

 
Contents 

1. Introduction .................................................................................................................................................................................... 140 
2. Data and Methodology ................................................................................................................................................................. 141 
3. Empirical Findings ........................................................................................................................................................................ 142 
4. Discussion ........................................................................................................................................................................................ 143 
5. Conclusion ....................................................................................................................................................................................... 143 
References ............................................................................................................................................................................................ 144 
 

 

 

 

 

 

 

mailto:busra.agancelik@ostimteknik.edu.tr
https://creativecommons.org/licenses/by/4.0/
https://creativecommons.org/licenses/by/4.0/
https://www.doi.org/10.20448/economy.v12i2.7458
https://orcid.org/0000-0003-1485-9142


Economy, 2025, 12(2): 139-145 

140 
© 2025 by the author; licensee Asian Online Journal Publishing Group 

 

 

Contribution of this paper to the literature 
This study contributes to the existing literature by integrating energy use, ecological footprint, trade 
openness, and the rule of law into a single ARDL framework for China (1990–2023). The primary 
contribution of this research is the identification of significant short-term effects, while the long-term 
linkages appear to be weak. This study provides novel evidence on China's growth–environment–
institution nexus, offering valuable insights for policymakers and researchers interested in sustainable 
development and institutional reforms. 

 
 
1. Introduction 

The escalating threat of climate change has prompted unprecedented global awareness and policy attention 
toward sustainable development. Scientific research widely agrees that anthropogenic activities are the primary 
drivers of global warming and environmental degradation (Furtuna & Atis, 2024; Idroes, Rahman, Uddin, Hardi, & 
Falcone, 2024; Kirikkaleli & Adebayo, 2021). As climate risks intensify, both developed and developing economies 
are pressed to adopt strategies that mitigate environmental harm while maintaining economic stability (Adebayo, 
Alola, Ullah, & Abbas, 2023). Among the factors shaping this transition, energy consumption patterns, institutional 
quality, and trade policies have emerged as critical determinants in the debate on sustainable growth. 

Environmental challenges stemming from industrial activities, such as air and water pollution, pose profound 
threats to human health, food security, and ecosystem integrity (Eweade, Uzuner, Akadiri, & Lasisi, 2024). These 
adverse effects are closely tied to the scale and composition of energy use. Fossil fuels, while sustaining industrial 
output, emit substantial greenhouse gases and pollutants, creating long-term ecological strain. Consequently, 
accelerating the transition to clean energy sources and strengthening environmental governance have become key 
priorities for major economies (Udemba, Tosun, & Matasane, 2021). 

China offers a particularly relevant context for this inquiry. Since economic liberalization in 1978, the country 
has achieved extraordinary growth, averaging over 9% annually, and has lifted more than 800 million people out of 
poverty (Cai, 2023). However, this rapid expansion has been accompanied by extensive industrialization, surging 
energy demand, and significant environmental costs. Although the Chinese government has increased investments 
in renewable energy and enacted environmental legislation, fossil fuels, especially coal, continue to dominate the 
national energy mix (Ivanovski, Hailemariam, & Smyth, 2021). 

The share of coal in China’s energy mix declined from 68.5% in 2000 to 60.6% in 2017, while oil consumption 
dropped slightly from 22.2% to 18.9%. Despite these changes, coal still constitutes around two-thirds of total energy 

consumption, and CO₂ emissions have been rising steadily since 2013. Meanwhile, per capita GDP rose from RMB 
7,912 in 2000 to RMB 59,855 in 2017 (China Statistical Yearbook (CSY), 2018), intensifying pressure on natural 
systems and exposing the limits of China's environmental carrying capacity.  

Accordingly, China has set forth a series of long-term sustainability goals. As reiterated in the 2019 “China–EU 
Joint Statement on Climate Change,” the country aims to attain carbon neutrality by 2060 (Dong, Sun, & Dong, 
2018). These objectives are incorporated into China's 14th Five-Year Plan, which emphasizes increasing non-fossil 
energy sources and strengthening the enforcement of environmental regulations (Organisation for Economic Co-
operation and Development (OECD), 2011). 

Institutionally, China’s environmental governance has undergone significant evolution. Although no unified 
legal code governs resource protection, a growing body of national and sub-national regulations now covers air, 
water, soil, waste, and ecosystem conservation (Liu, 2023; Zhao, Taghizadeh-Hesary, Dong, & Dong, 2023). The 
revised Environmental Protection Law and sector-specific acts have contributed to building a more coherent legal 
framework for ecological sustainability. Despite these reforms, several structural challenges persist. The tension 
between economic expansion and environmental conservation, coupled with fragmented enforcement capacity and 
uneven institutional development, raises important questions about the sustainability of China’s growth model (Jiang, 
Zhu, & Wang, 2022). In particular, the interrelated effects of energy consumption, ecological pressure, trade 
liberalization, and institutional quality on economic growth remain underexplored. 

This study is grounded in the theoretical framework of the Environmental Kuznets Curve (EKC), which suggests 
an inverted U-shaped relationship between income level and environmental degradation (Grossman & Krueger, 
1991). The EKC has been extensively examined in the context of China, with studies providing mixed evidence 
depending on the pollutants, time periods, and policy regimes considered (Wang, Zhang, Nathwani, Yang, & Shao, 
2022). While some analyses confirm the presence of a turning point as income levels rise (Kostakis, Armaos, Abeliotis, 
& Theodoropoulou, 2023), others argue that the structural dominance of coal, industrial expansion, and delayed 
enforcement of environmental regulations have postponed or prevented the expected decline in environmental 
pressure (Gu, Shen, Zhong, Wu, & Rahim, 2023). 

Similarly, this study draws upon the institutional theory of development (North, 1990), which highlights the 
central role of institutional quality, particularly legal frameworks, regulatory effectiveness, and governance, in 
shaping sustainable economic outcomes. In the Chinese context, institutional reforms have been found to influence 
both environmental policy enforcement and green technology adoption (Jiang et al., 2022; Zhao et al., 2023). 
Empirical evidence suggests that stronger institutions enhance the effectiveness of environmental regulations, 
facilitate investment in renewable energy, and improve the alignment of income levels with environmental objectives 
(Ozturk, Farooq, Majeed, & Skare, 2024). 

This study contributes to the literature by employing the dynamic relationship between GDP and four structural 
factors: energy use, ecological footprint, trade openness, and the rule of law in China. While each of these variables 
has been individually explored in prior research, there is a lack of integrated frameworks that examine their combined 
influence within a single econometric model. By bringing these dimensions together, the study enhances 
understanding of how environmental and institutional forces jointly shape economic performance in a rapidly 
developing economy. 

By employing the Autoregressive Distributed Lag (ARDL) model using annual data from 1990 to 2023, this 
study bridges a critical gap in the literature. The ARDL approach accommodates variables with different integration 



Economy, 2025, 12(2): 139-145 

141 
© 2025 by the author; licensee Asian Online Journal Publishing Group 

 

 

orders and distinguishes between short-run adjustments and long-run equilibrium dynamics. The expected policy 
relevance of this study lies in its potential to inform evidence-based strategies that align China’s economic growth 
objectives with environmental sustainability commitments, particularly under the Sustainable Development Goals 
(SDGs) 7 (Affordable and Clean Energy), 8 (Decent Work and Economic Growth), 12 (Responsible Consumption 
and Production), and 13 (Climate Action). 
 

2. Data and Methodology 
This study utilizes annual time-series data for China spanning the period 1990–2023 to investigate the 

relationship between income level and key environmental, institutional, and trade-related factors. The analysis 
adopts the ARDL framework to capture both short-run dynamics and long-run equilibrium relationships. 

Table 1 presents the variables used in this study, their abbreviations, detailed descriptions, and data sources. The 
dependent variable is real GDP at constant 2015 US dollars (LNGDP), serving as a proxy for income level, obtained 
from the World Bank’s World Development Indicators (WDI). Energy consumption (LNEC) is measured as total 
primary energy use in kilograms of oil equivalent, with data sourced from the International Energy Agency (IEA). 

The ecological footprint (LNECF), retrieved from the Global Footprint Network, provides an aggregate measure 
of environmental pressure. Trade openness (LNTO), expressed as the ratio of total trade to GDP, is obtained from 
the WDI. Institutional quality is captured through the rule of law index (LNRL) from the World Bank’s Worldwide 
Governance Indicators (WGI), reflecting the effectiveness of legal enforcement and the strength of governance 
institutions in China. 
 
Table 1. Variables and descriptions. 

Variable Abbreviation Description Source 

Gross domestic product per 
capita 

LNGDP Constant 2015 US$ WDI 

Energy consumption LNEC Energy use (kg of  oil equivalent per 
capita, log-transformed) 

WDI 

Ecological footprint LNECF Ecological footprint (global hectares 
per capita, log-transformed) 

Global footprint network 

Trade openness LNTO Trade as a percentage of  GDP (log-
transformed) 

WDI 

Rule of  law LNRL Governance indicator (Index, log-
transformed)) 

Worldwide Governance 
Indicators (WGI) 

 
The ARDL approach, developed by Pesaran, Shin, and Smith (2001), is particularly well-suited for time-series 

analysis when the regressors exhibit mixed integration orders, i.e., I(0) and I(1), but are not integrated of order two. 
Before estimation, the order of integration of each variable is examined to confirm the suitability of the ARDL 
framework. The ARDL bounds testing procedure is then applied to assess the existence of a long-run cointegration 
relationship among the variables. When cointegration is established, long-run coefficients are obtained from the 
ARDL model, and the associated short-run dynamics are evaluated through an Error Correction Model (ECM). 
The ARDL method offers several advantages that make it particularly suitable for this study. First, it provides 
flexibility with respect to integration orders, as it can accommodate regressors integrated at I(0), I(1), or a 
combination of both, without the need for pre-transforming all variables to the same order of integration. Second, it 
performs efficiently with relatively small sample sizes, which is especially relevant for the annual dataset covering 
the period 1990–2023. Third, the ARDL framework enables the simultaneous estimation of short-run dynamics and 
long-run equilibrium relationships, allowing a comprehensive understanding of both immediate and persistent 
effects. Finally, it supports robust model stability assessment and post-estimation diagnostic testing, ensuring the 
adequacy and reliability of the model specification. 
The baseline long-run relationship can be expressed as: 

𝐿𝑁𝐺𝐷𝑃𝑡 =  𝛼 + 𝛽1𝐿𝑁𝐸𝐶𝑡 + 𝛽2𝐿𝑁𝐸𝐶𝐹𝑡 + 𝛽3𝐿𝑁𝑇𝑂𝑡 + 𝛽4𝐿𝑁𝑅𝐿𝑡 + 𝜀𝑡                             (1) 
Where LNGDP denotes real GDP (income level), LNEC represents energy consumption, LNECF indicates the 

ecological footprint, LNTO measures trade openness, LNRL captures the rule of law, and 𝜀𝑡 is the error term. The 

form of the ARDL (p, q₁, q₂, ..., qₖ) model is specified as follows: 

Δln(GDPₜ) = α + ∑ βᵢ Δln(GDPₜ₋ᵢ) 
p
i=1  + ∑ γⱼ Δln(X₁ₜ₋ⱼ)

q1
j=0  + ... + ∑ φⱼ Δln(Xₖₜ₋ⱼ)

qk
j=0  + λ₁ ln(GDPₜ₋₁) + λ₂ ln(X₁ₜ₋₁) 

+ ... + λₖ₊₁ ln(Xₖₜ₋₁) + εₜ                                (2) 

Where Δ denotes the first-difference operator, Xₖ are the independent variables (such as energy consumption, 

ecological footprint, trade openness, and rule of law), and εₜ is the white-noise error term. 
 
Table 2. Descriptive statistics and correlation matrix. 

Variable  Obs. Mean Std. dev. Min. Max. Skewness Kurtosis 

LNGDP 34 7.145 0.945 5.875 7.288 -1.543 4.645 
LNEC 34 2.245 0.368 2.865 4.455 -0.764 1.643 
LNECF 34 1.863 0.786 0.346 2.765 -0.446 1.344 
LNTO 34 1.536 0.135 1.743 1.245 0.878 1.245 
LNRL 34 1.045 0.468 0.57 2.342 -1.787 3.875 
Variable  LNGDP LNEC LNECF LNTO LNRL 
LNGDP 1.000 

    

LNEC 0.341* 1.000 
   

LNECF 0.432 0.833* 1.000 
  

LNTO 0.233 0.712* 0.422* 1.000 
 

LNRL 0.032 0.788* 0.345* 0.234* 1.000 
Note: * denotes significance at 5% level.     



Economy, 2025, 12(2): 139-145 

142 
© 2025 by the author; licensee Asian Online Journal Publishing Group 

 

 

3. Empirical Findings  
Table 2 summarizes the descriptive statistics and correlation relationships among the variables. Most variables 

show skewed distributions, with real GDP (LNGDP) and rule of law (LNRL) notably left-skewed. The correlation 
matrix indicates that GDP is significantly and positively associated with both energy consumption and ecological 
footprint, suggesting that growth in China has been accompanied by environmental pressure. Trade openness shows 
a positive correlation with GDP. The rule of law is significantly related to all other variables, highlighting its broad 
influence. 

Table 3 summarizes the outcomes of the Augmented Dickey-Fuller (ADF) unit root tests applied to assess the 
stationarity of the variables. While LNGDP, LNTO, and LNRL are found to be stationary at level at the 10% or 5% 
significance levels, LNEC and LNECF are non-stationary in level form. After first differencing, all variables become 
stationary at the 1% level, confirming they are integrated of order one, I(1). These findings justify the use of the 
ARDL bounds testing approach, which is valid when the variables are a mix of I(0) and I(1), but none are I(2). 
 
Table 3. Unit root test. 

Variables Level First Difference 

Constant Constant and trend Constant Constant and trend 

LNGDP -2.399** -2.535** -4.066*** -4.289*** 
LNEC -1.632 -1.309 -3.691*** -3.903*** 
LNECF -0.124 -1.474 -4.344 *** -5.628*** 
LNTO -2.027** -1.397 -4.566*** -6.757*** 
LNRL -2.691* -2.142** -5.105*** -6.225*** 

Note: Stationarity at a significance level of *10%, **5%, and ***1%. 

 
Table 4 displays the outcomes of the ARDL bounds test used to determine the long-run cointegration between 

variables. The calculated F-statistic value of 11.26 exceeds the upper critical bounds at all significance levels. This 
strongly confirms a cointegration relation between GDP and its regressors: energy use, ecological footprint, trade 
openness, and rule of law. 
 
Table 4. ARDL bound test. 

Model F- statistic Lag  Bound test critical value 

 
Base Model* 

 
11.26 

 
3 

1% 3.15 4.43 
5% 2.55 3.68 

10% 2.26 3.34 
Note: *LNGDP=f (LNEC, LNECF, LNTO, LNRL). 

 
Table 5 presents the short-run and long-run estimates from the ARDL model. In the short run, changes in 

ecological footprint (D(LNECF)), trade openness (D(LNTO)), and rule of law (D(LNRL)) are statistically significant 
at the 10% or 5% levels, whereas changes in energy consumption (D(LNEC)) are not significant. The error correction 
term (ECM) is negative and highly significant, confirming the presence of a long-run equilibrium relationship, with 
approximately 70.3% of the short-run disequilibrium adjusted each period. 

In the long run, none of the explanatory variables are statistically significant at the 5% level, although the 
constant term is marginally significant at the 10% level. Energy consumption (LNEC), ecological footprint (LNECF), 
trade openness (LNTO), and rule of law (LNRL) all show positive or negative coefficients, but their high p-values 
indicate that these effects are not statistically robust. This suggests that the long-run relationship between these 
variables and GDP is weak or unstable. The high R2 value indicates strong explanatory power of the model, while 
the adjusted R2 suggests a reasonably good fit after accounting for degrees of freedom. 
 
Table 5. Short and Long-term coefficients. 

Short-term estimation 

Variable Coeffi. Std. error t-Stat. Prob. 

D (LNEC) 1.238 1.032 0.193 0.848 
D (LNECF) 4.422 2.587 1.702 0.099 
D (LNTO) 3.039 1.103 -2.229 0.033 
D (LNRL) 2.027 0.054 2.092 0.021 
ECM (-1) -0.703 0.079 -4.213 0.000 
R2 0.912 Mean dep. var 7.024 
Adjusted R2 0.669 S.D. dep. var 0.993 

Long-term Estimation 

Variable Coeffi. Std. error t-Stat. Prob. 
LNEC 2.183 2.305 0.567 0.577 
LNECF 1.280 1.026 0.921 0.364 
LNTO 2.902 1.366 0.120 0.905 
LNRL 3.037 1.493 -1.393 0.174 
C 14.55 13.32 1.902 0.0002 

Note: Probability values are evaluated based on 1%, 5%, and 10% significance levels.  

 
The diagnostic tests confirm the adequacy of the ARDL specification. The Ramsey RESET test indicates no 

functional form misspecification. The Breusch–Godfrey LM test and the Breusch–Pagan–Godfrey heteroskedasticity 
test fail to reject the null hypotheses, suggesting the absence of serial correlation and heteroskedasticity. The Jarque–
Bera statistic confirms normally distributed residuals. Moreover, the VIF value is well below the critical threshold, 
implying no multicollinearity concerns. Overall, the model satisfies the key econometric assumptions, supporting the 
reliability of the estimated coefficients for inference. 
 



Economy, 2025, 12(2): 139-145 

143 
© 2025 by the author; licensee Asian Online Journal Publishing Group 

 

 

Table 6. Diagnostic tests. 

Tests LM-test prob. Chi-squared- test prob. t-test / F-test prob. 

Ramsey RESET   0.1154 
Breusch-Godfrey serial correlation LM 0.1882   

Breusch-Pagan-Godfrey Heteroskedasticity 0.4421   

Jarque-Bera normality  0.5282  
Variance inflation factor (VIF)   3.22 

 
The stability of the estimated ARDL parameters is assessed using the CUSUM and CUSUM of Squares 

(CUSUMSQ) tests. As illustrated in Figure 1, both test statistics remain well within the 5% significance bounds 
throughout the sample period (1990–2023). This indicates the absence of structural instabilities or parameter drift 
over time, thereby confirming the robustness of the model specification. The stability of the coefficients further 
supports the reliability of the long-run relationships and reinforces the suitability of the ARDL framework for 
drawing policy-relevant conclusions in the context of China’s growth–environment–institution nexus. 
 
 

 
 Figure 1. CUSUM and CUSUMQ Test. 

 

4. Discussion 
The empirical findings of this study provide important insights into the interaction between environmental, 

institutional, and trade-related factors and income levels in China, and they align with several strands of the existing 
literature while also revealing notable divergences. 

In the short run, the significant effects of ecological footprint, trade openness, and rule of law on GDP are 
consistent with prior research emphasizing the immediate economic implications of environmental pressures and 
institutional interventions. For instance, Dam, Kaya, and Bekun (2024) similarly found that changes in ecological 
footprint exert short-term impacts on growth, reflecting the sensitivity of production structures to environmental 
constraints. Likewise, the positive short-run effect of trade openness mirrors the findings of Kim and Lin (2022), who 
reported that trade liberalization can boost economic performance in the short term, though its environmental effects 
remain mixed depending on the regulatory framework. 

The short-run significance of the rule of law aligns with Mtiraoui, Lazreg, and Chemli (2024) and Shah, Raghoo, 
and Surroop (2021), who argue that institutional enforcement capacity can produce rapid economic effects by 
improving policy credibility and investor confidence. However, this study’s results suggest that such effects do not 

persist in the long run, which resonates with  Udemba and Yalçıntaş (2021), who noted that institutional quality 
alone is insufficient to sustain growth without complementary structural reforms. 

In the long run, the absence of significant relationships between energy consumption, ecological footprint, trade 
openness, rule of law, and income level diverges from several studies that reported persistent environmental effects 
on GDP. For instance, Xia (2022) found a long-run link between fossil fuel consumption and growth, while Wang, 
Wang, and Li (2024) highlighted the enduring influence of trade openness under China’s industrial upgrading. The 
divergence observed here may be attributable to the inclusion of multiple structural factors in a single model, which 
could dilute individual long-run effects, or to structural changes in China’s economy over the 1990–2023 period that 
altered the persistence of these relationships. 

The insignificance of the long-run institutional variable also contrasts with studies such as North (1990) and 
Jiang et al. (2022) which emphasizes the foundational role of institutions in sustaining growth. One possible 
explanation is that the formal institutional framework in China has advanced more rapidly than its enforcement and 
implementation, resulting in weaker measurable long-term impacts on GDP. 

Overall, these findings suggest that the short-run responsiveness of growth to environmental and institutional 
factors is consistent with the broader literature, while the lack of long-run significance underscores the need to 
examine threshold effects, sectoral differences, and the role of complementary policies. This study adds to the debate 
by providing evidence that short-term environmental and institutional changes can generate rapid economic effects 
in China, but sustained impacts may require deeper structural transformations. 
 

5. Conclusion 
This study examined the relationship between income level and key environmental and institutional variables in 

China, including energy use, ecological footprint, trade openness, and rule of law, over the period 1990–2023 using 
the ARDL bounds testing approach. Motivated by the ongoing debate on the compatibility of economic expansion 
with environmental constraints, the analysis considered both short-run dynamics and long-run equilibrium 
relationships. 

The short-run results indicate that changes in ecological footprint, trade openness, and rule of law exert 
statistically significant effects on GDP, while the effect of energy consumption is positive but not statistically 
significant. These findings suggest that environmental pressure and institutional dynamics have immediate 



Economy, 2025, 12(2): 139-145 

144 
© 2025 by the author; licensee Asian Online Journal Publishing Group 

 

 

implications for economic performance, with trade openness also playing a short-term role in shaping growth 
outcomes. The error correction term (–0.703) is negative and highly significant, confirming rapid adjustment toward 
the long-run equilibrium following short-run deviations. 

In the long run, none of the explanatory variables are statistically significant at conventional levels, implying 
that the sustained effects of energy consumption, ecological footprint, trade openness, and institutional quality on 
GDP are weak or unstable over the study period. This result highlights the possibility that China’s long-term growth 
path is influenced by factors beyond the environmental and institutional variables included in this model, or that 
their effects are conditional on structural and policy changes not captured in the current specification. 

The results carry several important policy implications. The significant short-term effect of the ecological 
footprint suggests that China should accelerate the adoption of clean production processes and enhance 
environmental efficiency in key industrial sectors. Policies under SDG 12 (Responsible Consumption and Production) 
and SDG 13 (Climate Action) could be operationalized through stricter ecological footprint reduction targets, 
technology subsidies for green manufacturing, and more rigorous environmental monitoring. The short-term 
influence of trade openness on growth highlights the need to integrate environmental standards into trade policy. 
China can leverage trade agreements to promote green technology transfer, establish environmental quality 
benchmarks for exports and imports, and avoid pollution haven dynamics. The short-term significance of the rule of 
law underscores the importance of timely governance interventions. Enhancing enforcement mechanisms, increasing 
institutional transparency, and ensuring uniform application of environmental laws across provinces will be critical 
for achieving policy credibility. 

The long-term insignificance of environmental and institutional variables points to the necessity of sustained 
structural reforms. These include reducing coal dependency, expanding the share of renewables (SDG 7), promoting 
energy efficiency, and embedding environmental sustainability into industrial planning (SDG 8). A gradual shift 
toward a green growth model will require harmonized energy, industrial, and legal policies. Moreover, the high 
speed of adjustment toward equilibrium highlights the economy’s sensitivity to shocks, supporting the adoption of 
agile, evidence-based policymaking that responds quickly to environmental and trade fluctuations to maintain 
macroeconomic stability. 

While this study contributes to understanding the growth–environment–institution nexus in China, several 
avenues remain for future research. Incorporating additional institutional indicators, such as government 
effectiveness, regulatory quality, and environmental policy stringency, could provide a more nuanced picture of 
institutional capacity. Expanding the environmental dimension to include renewable energy share, carbon pricing 
mechanisms, or sector-specific emissions may capture a broader scope of environmental pressures. Future studies 
could also employ nonlinear or threshold models (e.g., Threshold ARDL, Smooth Transition models) to detect 
regime shifts that might explain the insignificance of long-run effects. Finally, comparative studies between China 
and other emerging economies could offer valuable insights into the generalizability of these findings, particularly 
within the framework of the Sustainable Development Goals. 
 

References 
Adebayo, G., Alola, A. A., Ullah, S., & Abbas, S. (2023). The growth impacts of agriculture value-added, energy utilization, and CO₂ emissions 

in emerging economies. Environmental Science and Pollution Research, 30(12), 34567–34580.  
Cai, J. (2023). China's economic development from the ages of comprehensive reforms and opening-up-a literature review. Multidiszciplináris 

Kihívások, Sokszínű Válaszok-Gazdálkodás-és Szervezéstudományi Folyóirat, (1), 3-36. https://doi.org/10.33565/MKSV.2023.01.01 
China Statistical Yearbook (CSY). (2018). China statistical yearbook 2018. Beijing, China: National Bureau of Statistics of China. 
Dam, M. M., Kaya, F., & Bekun, F. V. (2024). How does technological innovation affect the ecological footprint? Evidence from E-7 countries 

in the background of the SDGs. Journal of Cleaner Production, 443, 141020. https://doi.org/10.1016/j.jclepro.2024.141020 
Dong, K., Sun, R., & Dong, X. (2018). CO2 emissions, natural gas and renewables, economic growth: Assessing the evidence from China. Science 

of the Total Environment, 640-641, 293-302. https://doi.org/10.1016/j.scitotenv.2018.05.322 
Eweade, B. S., Uzuner, G., Akadiri, A. C., & Lasisi, T. T. (2024). Japan energy mix and economic growth nexus: Focus on natural gas 

consumption. Energy & Environment, 35(2), 692-724. https://doi.org/10.1177/0958305X221130460 
Furtuna, K. O., & Atis, S. (2024). Does foreign direct investment affect environmental degradation: Evidence from largest carbon intense 

countries. PLoS One, 19(11), e0314232. https://doi.org/10.1371/journal.pone.0314232 
Grossman, G. M., & Krueger, A. B. (1991). Environmental impacts of a North American free trade agreement. NBER Working Paper No. 3914. 

Cambridge, MA: National Bureau of Economic Research. https://doi.org/10.3386/w3914 
Gu, X., Shen, X., Zhong, X., Wu, T., & Rahim, S. (2023). Natural resources and undesired productions of environmental outputs as green 

growth: EKC in the perspective of green finance and green growth in the G7 region. Resources Policy, 82, 103552. 
https://doi.org/10.1016/j.resourpol.2023.103552 

Idroes, G. M., Rahman, H., Uddin, I., Hardi, I., & Falcone, P. M. (2024). Towards sustainable environment in North African countries: The 
role of military expenditure, renewable energy, tourism, manufacture, and globalization on environmental degradation. Journal of 
Environmental Management, 368, 122077. https://doi.org/10.1016/j.jenvman.2024.122077 

Ivanovski, K., Hailemariam, A., & Smyth, R. (2021). The effect of renewable and non-renewable energy consumption on economic growth: 
Non-parametric evidence. Journal of Cleaner Production, 286, 124956. https://doi.org/10.1016/j.jclepro.2020.124956 

Jiang, J., Zhu, S., & Wang, W. (2022). Carbon emissions, economic growth, urbanization, and foreign trade in China: Empirical evidence from 
ARDL models. Sustainability, 14(15), 9396. https://doi.org/10.3390/su14159396 

Kim, D.-H., & Lin, S.-C. (2022). Trade openness and environmental policy stringency: Quantile evidence. Sustainability, 14(6), 3590. 
https://doi.org/10.3390/su14063590 

Kirikkaleli, D., & Adebayo, T. S. (2021). Do renewable energy consumption and financial development matter for environmental sustainability? 
New global evidence. Sustainable Development, 29(4), 583-594. https://doi.org/10.1002/sd.2159 

Kostakis, I., Armaos, S., Abeliotis, K., & Theodoropoulou, E. (2023). The investigation of EKC within CO2 emissions framework: Empirical 
evidence from selected cross-correlated countries. Sustainability Analytics and Modeling, 3, 100015. 
https://doi.org/10.1016/j.samod.2023.100015 

Liu, X. (2023). The impact of China’s high-quality development of energy on carbon neutrality. Energy Reports, 9, 2665-2675. 
https://doi.org/10.1016/j.egyr.2023.01.089 

Mtiraoui, A., Lazreg, M., & Chemli, L. (2024). Institutional quality and economic growth in the energy transition of MENA countries. 
International Journal of Energy Economics and Policy, 14(6), 605-613.  

North, D. C. (1990). Institutions, institutional change and economic performance. Cambridge, UK: Cambridge University Press. 
Organisation for Economic Co-operation and Development (OECD). (2011). Towards green growth: Monitoring progress – OECD indicators. 

Paris, France: OECD Publishing. 

https://doi.org/10.33565/MKSV.2023.01.01
https://doi.org/10.1016/j.jclepro.2024.141020
https://doi.org/10.1016/j.scitotenv.2018.05.322
https://doi.org/10.1177/0958305X221130460
https://doi.org/10.1371/journal.pone.0314232
https://doi.org/10.3386/w3914
https://doi.org/10.1016/j.resourpol.2023.103552
https://doi.org/10.1016/j.jenvman.2024.122077
https://doi.org/10.1016/j.jclepro.2020.124956
https://doi.org/10.3390/su14159396
https://doi.org/10.3390/su14063590
https://doi.org/10.1002/sd.2159
https://doi.org/10.1016/j.samod.2023.100015
https://doi.org/10.1016/j.egyr.2023.01.089


Economy, 2025, 12(2): 139-145 

145 
© 2025 by the author; licensee Asian Online Journal Publishing Group 

 

 

Ozturk, I., Farooq, S., Majeed, M. T., & Skare, M. (2024). An empirical investigation of financial development and ecological footprint in South 
Asia: Bridging the EKC and pollution haven hypotheses. Geoscience Frontiers, 15(4), 101588. 
https://doi.org/10.1016/j.gsf.2023.101588 

Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics, 
16(3), 289–326. https://doi.org/10.1002/jae.616 

Shah, K. U., Raghoo, P., & Surroop, D. (2021). An institutional-based governance framework for energy efficiency promotion in small island 
developing states. Climate, 9(6), 95. https://doi.org/10.3390/cli9060095 

Udemba, E. N., Tosun, J., & Matasane, C. (2021). Renewable energy transition and economic development in the BRICS economies: 
Implications for sustainable development. Renewable Energy, 180, 1170–1182.  

Udemba, E. N., & Yalçıntaş, S. (2021). Interacting force of foreign direct invest (FDI), natural resource and economic growth in determining 
environmental performance: A nonlinear autoregressive distributed lag (NARDL) approach. Resources Policy, 73, 102168. 
https://doi.org/10.1016/j.resourpol.2021.102168 

Wang, Q., Wang, L., & Li, R. (2024). Trade openness helps move towards carbon neutrality—insight from 114 countries. Sustainable 
Development, 32(1), 1081-1095. https://doi.org/10.1002/sd.2720 

Wang, X., Zhang, T., Nathwani, J., Yang, F., & Shao, Q. (2022). Environmental regulation, technology innovation, and low carbon 
development: Revisiting the EKC Hypothesis, Porter Hypothesis, and Jevons’ Paradox in China's iron & steel industry. Technological 
Forecasting and Social Change, 176, 121471. https://doi.org/10.1016/j.techfore.2022.121471 

Xia, S. (2022). The link and spillovers between clean energy and fossil fuels market: A systematic literature review. Journal of Accounting 
Literature, 44(2/3), 177-191. https://doi.org/10.1108/JAL-08-2022-0087 

Zhao, J., Taghizadeh-Hesary, F., Dong, K., & Dong, X. (2023). How green growth affects carbon emissions in China: The role of green finance. 
Economic Research, 36(1), 2090-2111. https://doi.org/10.1080/1331677X.2022.2095522 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 
 
 

Asian Online Journal Publishing Group is not responsible or answerable for any loss, damage or liability, etc. caused in relation to/arising out of the use of the content. 
Any queries should be directed to the corresponding author of the article. 

 

https://doi.org/10.1016/j.gsf.2023.101588
https://doi.org/10.1002/jae.616
https://doi.org/10.3390/cli9060095
https://doi.org/10.1016/j.resourpol.2021.102168
https://doi.org/10.1002/sd.2720
https://doi.org/10.1016/j.techfore.2022.121471
https://doi.org/10.1108/JAL-08-2022-0087
https://doi.org/10.1080/1331677X.2022.2095522

