Impact of China's financial development on the sustainable development goals of the Belt and Road Initiative participating countries

China launched the Belt and Road Initiative (BRI) in 2013 to enhance regional economic and policy cooperation, aiming to align with the UN 2030 Agenda for Sustainable Development. Although prior work links finance to economic growth, energy, and environmental outcomes in BRI countries, few studies assess how China’s financial development—across scale, structure, and efficiency—affects BRI countries’ progress on the Sustainable Development Goals (SDGs). This study asks: (1) Has China’s financial development promoted SDGs in BRI countries? (2) How do effects vary across regions (Asia vs. Europe), income groups, and BRI corridors (Land vs. Maritime)? (3) How do these effects evolve over time? Addressing critical funding gaps for SDG implementation in developing BRI countries, the paper quantifies financial development’s impacts using panel models and explores spatio-temporal heterogeneity via GTWR.

Existing studies commonly examine single dimensions of financial development (e.g., depth, stability, institutional environment) and often focus on isolated sustainability outcomes such as CO2 emissions, deforestation, or health. Few works evaluate multiple aspects of financial development simultaneously or assess comprehensive SDG outcomes. Some research suggests finance can promote aspects of economic, social, or environmental sustainability, and studies in other regions (e.g., EU) link sustainable finance models with better SDG performance. This paper advances the literature by jointly measuring China’s financial development scale, structure, and efficiency and relating them to a broader SDG index, alongside heterogeneity analyses across regions, income groups, and BRI corridors.

Data: Panel dataset for 61 BRI participating countries from 2005–2018, primarily from the World Bank (integrating FAO, UNESCO, and others). Countries are categorized into Maritime Silk Road (33) and Land Silk Road (28) economies. Missing annual observations are interpolated. SDG measurement: Focus on SDGs 2, 3, 5, 8, 9, 11, and 15, selected for relevance to national-level impacts and BRI priorities and data availability. An SDG index is computed via the entropy method: (1) normalize indicators (positive/negative), (2) compute proportions P_ij, (3) entropy e_j, (4) variability a_j, (5) weights w_j, (6) weighted aggregation s_i. Explanatory variables (China’s financial development): - Scale (FIR): ratio of RMB deposit and loan balances of financial institutions to China’s GDP. - Structure (FS): ratio of China’s stock market value to bank credit to the private sector. - Efficiency (FE): loan-to-deposit ratio of China’s financial institutions. Robustness proxies: Alternative measures include FIR as financial industry value-added/GDP; FS as direct financing/total social financing; FE as stock turnover/market capitalization; and absolute-amount measures capturing fund circulation to test sensitivity. Controls: Population growth (PGR), technology level (TECH, R&D expenditure/GDP), foreign trade (FT, imports of goods and services/GDP), education (EDU, government education expenditure share), and human capital (RD, R&D personnel per million people). Econometric models: - Panel regression: SDG_it = β0 + β1 FIR_it + β2 FS_it + β3 FE_it + β4 EDU_it + β5 RD_it + β6 PGR_it + β7 FT_it + β8 TECH_it + ε_it, assessing average effects across countries and time. - Geographically and Temporally Weighted Regression (GTWR): y_i = β0(u_i,v_i,t_i) + Σ_k β_k(u_i,v_i,t_i) x_ki + ε_i, estimating local spatio-temporal coefficients using a Gaussian kernel and Euclidean distance; bandwidth chosen via AIC. - Model comparison: GTWR vs. GWR (spatial-only). GTWR shows smaller residual sums, lower AIC/AICC, and higher R^2/adjusted R^2, indicating better fit. Heterogeneity analyses: Effects compared across Asia vs. Europe, Land vs. Maritime corridors, and World Bank income groups (low, middle, high).

- Overall effects: China’s financial development significantly promotes SDGs in BRI countries. Panel estimates show: - Scale (FIR): β1 = 0.0454, p < 0.01. - Structure (FS): β2 = 0.0643, p < 0.01. - Efficiency (FE): β3 = 0.1444, p < 0.05, the largest positive effect. Robustness with alternative proxies confirms positive significance: FIR β1 = 0.0544 (p < 0.01), FS β2 = 0.0802 (p < 0.01), FE β3 = 0.5809 (p < 0.01); results also hold using social financing scale as a proxy. - Heterogeneity: - Region: Scale and efficiency effects are stronger for Asian than European BRI countries; structure effects more strongly promote SDGs in European countries. - Income: Scale effects are larger for low- and middle-income countries; efficiency and structure effects more strongly promote SDGs in high- and middle-income countries. - Corridor: Structure and efficiency effects are stronger for Land Silk Road countries; scale effects are stronger for Maritime Silk Road countries. - Spatio-temporal patterns (GTWR): - Scale (FIR): Positive impacts weaken over time in many BRI countries, concentrating in Russia, Mongolia, Central and Eastern Europe; several countries (Kazakhstan, India, Egypt, Saudi Arabia, Iran) exhibit inverted U-shaped temporal patterns. - Structure (FS): Mixed patterns—declining positives in Mongolia and Southeast Asia; increasing positives in Egypt, Saudi Arabia, Yemen, Iran, Iraq; inverted U in Ukraine, Romania, Bulgaria, Greece; U-shaped in Russia, Afghanistan, Kazakhstan. - Efficiency (FE): Broadly increasing positive impact over time; Mongolia, India, and many Southeast Asian countries show rising positives; some countries (Russia, Singapore, Vietnam) shift from negative to positive, while others (Egypt, Saudi Arabia, Yemen, Turkey) shift from positive to negative; strongest positive effects in Central and Eastern Europe. - Model performance: GTWR outperforms GWR on residual metrics and fit, indicating the importance of accounting for spatio-temporal heterogeneity.

The study demonstrates that China’s financial development—particularly efficiency—supports SDG progress across BRI countries by mobilizing and allocating capital, strengthening partnerships, and facilitating infrastructure and innovation. Heterogeneous effects reflect differences in development stage, industrial structure, trade/transport advantages, and energy demand. In several countries, scale and structure show inverted U-shaped effects consistent with environmental Kuznets curve dynamics: early-stage financial expansion supports growth and SDGs, while later stages may encounter constraints (e.g., energy use, emissions, governance). Efficiency enhancements, by contrast, more consistently improve SDGs by channeling funds to energy-efficient and environmentally friendly projects. Since the BRI’s launch, rising Chinese investment and expanding financial cooperation (e.g., Silk Road Fund, AIIB) likely amplified positive impacts over time. Policymakers should recognize spatial and temporal heterogeneity and prioritize improving financial efficiency and optimizing structures rather than pursuing unbounded scale growth.

Using panel data for 61 BRI countries (2005–2018), the study finds that China’s financial development positively impacts overall SDG performance, with efficiency having the strongest effect. The effects vary by region, income, and corridor: stronger promotion in Asian, low- and middle-income, and Land Silk Road countries (for efficiency and structure), while scale shows stronger effects in Maritime countries and for lower-income groups. Temporally, scale’s positive impact tends to diminish, structure’s impact varies by country, and efficiency’s impact generally increases. These insights highlight the need to align financial development—especially efficiency and structural optimization—with SDG priorities across diverse BRI contexts. Future work should expand SDG coverage, analyze country-specific mechanisms, and develop tailored policies to better match financial initiatives with heterogeneous national SDG needs.

- Scope of SDGs: Only a subset of SDGs (2, 3, 5, 8, 9, 11, 15) was evaluated due to national-scale relevance and data availability; broader inclusion could change results. - Heterogeneity: Aggregation by region, income, and corridor may mask country-specific institutional, political, and developmental differences (e.g., instability, governance quality) that condition financial impacts. - Measurement: Financial development proxies, even with robustness checks, may not fully capture complex financial dynamics; interpolation for missing data may introduce noise. - Alignment issues: Potential mismatch between donor and recipient priorities can limit effectiveness; more granular alignment analysis is needed.