Impact of investment in quality language education on green economic growth: case study of 23 Chinese provinces

The study examines whether and how investment in quality language education promotes green economic growth in China. Against the backdrop of persistent reliance on fossil fuels and escalating climate challenges, the paper positions green growth as a gradual, multidimensional pathway aligning economic activity with environmental protection. China is selected due to its leading share in global emissions and ambitious carbon peaking and neutrality targets, alongside longstanding national efforts in education for sustainable development. The research addresses a gap by focusing specifically on the effect of quality language education on provincial green growth, hypothesizing that higher-quality education builds sustainable literacy, supports energy transition, and fosters behaviors and skills conducive to green economic development.

Two strands are reviewed. First, studies on green growth highlight its multifaceted drivers and outcomes: it enhances human capital and social wellbeing, prioritizes environmental restoration, advances technological progress, and depends on factors such as renewable deployment, ICT diffusion, innovation, and investment (e.g., Pelinescu 2015; Sandberg et al. 2019; Xu et al. 2022; Fabozzi et al. 2022; Maiti 2022; Mahmood et al. 2022; Lehmann et al. 2022; Zhao et al. 2022; Ge et al. 2023; Shang et al. 2023b). Second, literature links education to sustainability by improving sustainable literacy, shaping cultural perceptions of resource use, strengthening human capital, and building skills for green jobs and innovation; educational initiatives (e.g., green campuses) disseminate sustainability in society (e.g., Al-Lian et al. 2019; Garcia-Gonzalez et al. 2020; Greenland et al. 2022; Lee et al. 2022; Ngo et al. 2022; Dong et al. 2023; Sulich et al. 2020; Xie et al. 2020; Ribeiro et al. 2021). The review identifies a gap: prior work has not quantified the effect of quality language education on China’s green growth at the provincial level, which this paper addresses.

Design: Panel data analysis for 23 Chinese provinces over 2010–2021. Dependent variable: Green growth index (GRGROW), constructed using a weighting TOPSIS method following Lin and Zhou (2022). Key explanatory variable: Quality language education (QLE), proxied using provincial education quality data (sourced from China Statistical Yearbook and A Global Data Set on Education Quality; also described as drawn from Chinese General Social Survey). Controls: Renewable power generation (RPG), waste generation (WGE), green investment (GEIN), and green jobs (GRJO). Variable definitions, symbols, units, and sources are summarized in Table 1 of the paper. Pre-estimation diagnostics: Multicollinearity checked via VIF (all VIFs < 5). Cross-sectional dependence assessed using Pesaran’s CD test, indicating significant CD across panel units. Stationarity: Second-generation CIPS unit root tests allowing for cross-sectional dependence show a mix of I(0) and I(1) integration orders across variables (e.g., GRGROW and QLE become stationary after first differencing; RPG and GRJO are stationary at levels). Cointegration: Panel cointegration tests (Pedroni-type statistics reported) indicate long-run cointegrating relationships among variables. Estimation: Autoregressive Distributed Lag model with Pooled Mean Group (ARDL-PMG) estimator to accommodate mixed integration orders and heterogeneous short-run dynamics with homogeneous long-run coefficients. Short-run dynamics modeled in first differences with an error correction term (ECT). Robustness: Re-estimation using an alternative dependent variable (composite green growth index) to validate stability of signs and significance. Data sources: China Statistical Yearbook; CEIC database; A Global Data Set on Education Quality; survey-based education quality measures as described by the authors.

• Quality language education (QLE) significantly promotes green economic growth. Long-run coefficient: 0.013 (p=0.000); short-run coefficient: 0.693 (p=0.083). Interpreted as a 1% increase in QLE associated with about 0.01% long-run and 0.69% short-run increases in green growth.
• Renewable power generation (RPG) positively affects green growth. Long-run: 0.254 (p=0.065); short-run: 0.022 (p=0.014).
• Waste generation (WGE) hampers green growth. Long-run: -0.151 (p=0.000); short-run: -0.573 (p=0.058). The authors highlight that a 1% increase in waste reduces green growth by roughly 0.15% (long-run) and 0.57% (short-run).
• Green investment (GEIN) enhances green growth. Long-run: 0.259 (p=0.029); short-run: 0.114 (p=0.069).
• Green jobs (GRJO) are positively associated with green growth. Long-run: 0.029 (p=0.002); short-run: 0.229 (p=0.033).
• Error correction term: -0.011 (p=0.023), indicating slow but significant adjustment toward long-run equilibrium.
• Diagnostics: Significant cross-sectional dependence detected; CIPS tests confirm mixed I(0)/I(1) integration; panel cointegration present.
• Robustness check using a composite green growth index confirms consistent signs and general significance patterns (e.g., long-run QLE 0.066, p=0.093; RPG positive; WGE negative; GEIN and GRJO positive; short-run effects broadly aligned).

Findings support the hypothesis that improving the quality of language education fosters green economic growth by enhancing sustainable literacy, encouraging participation in renewable energy adoption, and building skills relevant to green jobs. Positive effects from renewable power generation and green investment align with the role of energy transition and capital allocation in decarbonizing economies. Negative effects of waste generation underscore the importance of circular economy practices for sustainability. The positive association of green jobs indicates labor market mechanisms that translate education and investment into greener production and services. Together, the results validate education as a key social driver complementing economic levers (renewables, investment, employment) to achieve provincial green growth in China.

The study contributes by constructing a provincial green growth index via TOPSIS and assembling a novel proxy for quality language education to quantify its impact on green growth across 23 Chinese provinces (2010–2021). Using ARDL-PMG, the paper shows that quality language education significantly boosts green growth in both the short and long run, while renewable power generation, green investment, and green jobs further enhance sustainability; waste generation hinders it. Policy recommendations include expanding education for sustainable development (ESD), integrating sustainability into curricula, greening campuses and facilities, providing specialized training for decision-makers, promoting public literacy and participation in renewable power generation, incentivizing circular economy practices, and supporting SMEs to create green jobs and drive sustainable investment. Future research should examine province-specific effects and explore the role of universities’ renewable energy use in China’s economic growth to tailor policies more precisely.

The authors note challenges in constructing the green growth index and measuring the quality of language education at the provincial level due to limited data access. The scope is restricted to 23 provinces over 2010–2021, which may limit generalizability. Measures of education quality rely on available survey/statistical sources that may not capture all dimensions of educational quality. The ARDL-PMG framework assumes common long-run coefficients, which may mask province-specific heterogeneity.