Spatial Heterogeneity in Carbon Footprint from Belt and Road Investments Assessed Through a Cross-Sectional Autoregressive Distributed Lag Model

Summary

This study analyzes the environmental impact of Chinese foreign direct investment (FDI), tourism, and innovation on carbon emissions across Belt and Road Initiative regions using spatial econometric models.

Abstract

This study aims to empirically investigate the multifaceted impact of China's infrastructure-led foreign direct investment (FDI), tourism development, and technological innovation on carbon emissions within Belt and Road Initiative (BRI) participant countries. Moving beyond aggregate analysis, the research specifically examines the often-overlooked synergistic effect between FDI and tourism and explores the significant regional heterogeneities in these environmental relationships. Methodologically, the study employs second-generation panel data techniques to ensure robust estimations in the presence of cross-sectional dependence and slope heterogeneity. The analysis utilizes the Cross-Sectional Autoregressive Distributed Lag (CS-ARDL) and Common Correlated Effects Mean Group (CCEMG) estimators on data from 2000 to 2020 to discern both short-run and long-run dynamics. The key findings reveal a complex and dualistic role for FDI. While it is associated with a reduction in carbon emissions in full sample and in regions like Southeast Asia and Europe, it significantly increases emissions in South Asia and MENA countries. Critically, the interaction between FDI and tourism development is found to exacerbate carbon emissions, indicating that infrastructure investments amplify the environmental footprint of tourism. Furthermore, technological innovation consistently mitigates emissions, and the study validates the Environmental Kuznets Curve (EKC) hypothesis across all samples. The implications of these findings are profound for policymakers, underscoring the necessity of moving beyond one-size-fits-all approaches. The study's primary novelty lies in its explicit focus on infrastructure-led FDI, its quantification of the FDI-tourism interaction effect, and its groundbreaking regional disaggregation, which collectively reveal the contested and context-dependent nature of the BRI's environmental legacy. This granular analysis provides a critical evidence base for formulating targeted, region-specific policies to steer the BRI towards its stated goal of sustainable development.

Introduction

The Belt and Road Initiative (BRI), a monumental global development strategy launched by China¹, has profoundly reshaped the economic and infrastructural landscape across Asia, Africa, and Europe². By channeling unprecedented levels of infrastructure-led foreign direct investment (FDI) into transportation, energy, and port facilities, the BRI aims to enhance regional connectivity and stimulate economic growth. A critical, yet complex, outcome of this enhanced connectivity has been the significant boost to tourism development in participating countries³, facilitated by improved accessibility and in....

Protocol

This study utilized exclusively publicly available, secondary macroeconomic data from official sources, including the World Bank's World Development Indicators (WDI) and the Ministry of Commerce of the People's Republic of China (MOFCOM). The underlying raw data are publicly available from the World Bank (https://databank.worldbank.org/source/world-development-indicators) and the Chinese Ministry of Commerce (MOFCOM). No human or animal subjects were involved in the research. As such, this study did not require review or approval by an institutional review board (IRB) or ethics committee.

Data and variable construction

Results

The presented results effectively demonstrate the utility of the Cross-sectionally Augmented Autoregressive Distributed Lag (CS-ARDL) technique in capturing complex, real-world economic relationships (Figure 2). For instance, the divergent long-run impact of Foreign Direct Investment (FDI) on carbon emissions is negative in some regions, like Southeast Asia, but positive in others, like South Asia, exemplifying the slope heterogeneity that the CS-ARDL model is specifically designed to handle.......

Discussion

The results of this empirical work provide a rich and nuanced story regarding the possible environmental impact of economic integration within the framework of the Belt and Road Initiative and transcend the simplistic dichotomies that tend to define the literature. Testing slope heterogeneity and cross-sectional dependence must not be introduced as a methodological nicety at the very outset, but rather as a basic point; it must be recognized that the relations between FDI, tourism, technology, and carbon emissions are no.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Causality Analysis	Heterogeneous Panel Granger Causality Test by Dumitrescu and Hurlin (2012)		To determine the direction of causal relationships between the variables (e.g., does FDI Granger-cause CO2 emissions?).
Cross-Sectional Dependence (CSD) Test	Pesaran (2015) Cross-Sectional Dependence (CD) test		To diagnostically test the null hypothesis of cross-sectional independence among the error terms of the panel units.
FDI-TD Interaction Term	Computed as the product of the FDI and Tourism Development (TD) index series.		To capture the synergistic or moderating effect that infrastructure-led FDI has on the relationship between tourism development and carbon emissions.
Foreign Direct Investment (FDI) Data	Ministry of Commerce of the People's Republic of China (MOFCOM)		Source for data on Chinese outward FDI stocks to Belt and Road Initiative (BRI) countries.
Long-run and Short-run Estimation	Cross-Sectional Autoregressive Distributed Lag (CS-ARDL) model		The primary estimator to derive both short-run and long-run coefficients for the impact of independent variables on CO2 emissions, while accounting for CSD and heterogeneity.
Macroeconomic Panel Data	World Bank, World Development Indicators (WDI)		Primary source for data on CO2 emissions, GDP, Industrial Value-Added (IVA), and tourism metrics (arrivals, receipts).
Panel Cointegration Tests	Westerlund and Edgerton (2008) cointegration test with structural breaks		To test for the existence of a long-run cointegrating relationship among the variables, accounting for CSD and structural breaks.
Robustness Check Estimator	Common Correlated Effects Mean Group (CCEMG)		An alternative estimator used to check the robustness of the long-run coefficients obtained from the CS-ARDL model.
Second-Generation Unit Root Tests	Cross-sectional Augmented Dickey-Fuller (CADF) and Cross-sectional Augmented IPS (CIPS)		To determine the order of integration of the variables (CO2, FDI, TD, TI, GDP, IVA) in the presence of cross-sectional dependence.
Slope Heterogeneity (SH) Test	Pesaran and Yamagata (2008) Delta and Delta-modified tests		To diagnostically test the null hypothesis of homogeneous slopes across cross-sections in the panel.
Squared GDP (SQGDP)	Computed as the square of the GDP per capita (or total GDP) series.		To empirically test for the presence of the Environmental Kuznets Curve (EKC) hypothesis in the model.
Statistical Software Package	Not specified in the protocol, but essential for analysis (e.g., Stata, R, EViews)		Platform for data management, variable construction, and execution of all econometric tests and estimations (CSD, SH, unit root, cointegration, CS-ARDL, CCEMG, causality).
Technology Innovation Data	World Bank, World Development Indicators (WDI)		Source for patent data (resident and non-resident) used to construct the Technology Innovation (TI) index.
Tourism Development (TD) Index	Principal Component Analysis (PCA) applied to tourism arrivals, receipts, and expenditures.		To create a single, composite index that captures overall tourism activity and avoids multicollinearity from using individual tourism metrics.