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 infrastructure⁴. However, this triad of infrastructure investment, tourism growth, and economic expansion carries substantial environmental implications, particularly concerning carbon emissions⁵. Understanding the interplay between these factors is paramount for aligning the BRI's economic objectives with global sustainability goals, such as the Paris Agreement and the UN Sustainable Development Goals⁶. This research specifically investigates the consequences of China's infrastructure-led FDI, tourism development, and technological innovation on carbon emissions within the context of BRI participant nations.

There is a significant literature that has entrenched the importance of tourism as an economic development driver, especially in the emerging markets^7,8. The sector is actively encouraged by governments because it brings revenues, contributes to the growth of entrepreneurship, and minimizes unemployment⁹. The industry has shown strong growth globally as international tourist arrivals have been exceeding the projections and bringing in revenues in billions annually, a trend that has been encouraged by the improvement of communication technology, changing visa procedures, and better tourist destinations¹⁰. At the same time, there has been questioning regarding the environmental imprint of this growth¹¹. One line of research points out the adverse effect of tourism, attributing it to the direct correlation of tourism with energy consumption and carbon emissions related to transport¹². Moreover, despite the opportunities that big infrastructure projects under the BRI bring to the tourism sector, they are also known to cause immense environmental stress due to the energy demand and ecological disturbances¹³.

However, existing literature exhibits critical gaps that this study seeks to address. First, while the general link between tourism and emissions is acknowledged, the empirical evidence remains ambiguous and often fails to account for the unique, multi-faceted context of the BRI. The relationship is not monolithic; it is mediated by a host of local factors, including logistical infrastructure, exposure to political instability, environmental standards, and climate conditions¹⁴. Second, a competing narrative in the literature posits that tourism, especially when aligned with eco-tourism principles and green policies, can be a vehicle for environmental sustainability rather than degradation¹⁵. This duality underscores a significant knowledge gap: under what conditions does tourism within the BRI framework exacerbate or mitigate emissions? Third, the role of infrastructure-led FDI is often studied in isolation¹⁶. The synergistic effect of FDI and tourism development, where new airports, roads, and hotels funded by FDI directly enable and stimulate tourist inflows, and its collective impact on the environment is not well understood. This research directly addresses these gaps by conducting a nuanced and comprehensive analysis of the environmental consequences of the BRI's economic drivers. Researchers move beyond broad generalizations to investigate the specific, combined impact of infrastructure-led FDI, tourism development, and technological innovation on carbon emissions. The contributions of this study are threefold. First, it provides novel empirical evidence on the interconnectedness of infrastructure investment, tourism, and carbon emissions within the specific and highly relevant context of the Belt and Road Initiative. By explicitly modeling the interaction between tourism development and FDI, researchers uncover whether their combination creates a synergistic effect that amplifies or mitigates environmental pressure, a dimension largely overlooked in previous studies¹⁷. Second, this research makes a significant methodological contribution by applying a CS-ARDL model and conducting a detailed regional analysis. This enables us to move past one-size-fits-all conclusions and reveal the considerable regional disparities in how these economic activities impact the environment.

Literature review

The tourism industry is widely recognized as a significant catalyst for economic development, particularly in emerging markets¹⁸. Empirical studies consistently demonstrate its capacity to generate income, stimulate entrepreneurial activity, and reduce unemployment, leading governments worldwide to adopt supportive policies for the sector¹⁹. Historical data underscores this dynamic growth, with the industry maintaining an average annual growth rate of about 5%, and international tourist arrivals projected to surpass 1.8 billion²⁰, a projection often exceeded by actual figures fueled by advancements in communication technology, improved tourist infrastructure, and streamlined visa regulations²¹. However, the sector's contribution to economic growth is not uniform, as its success is mediated by a complex array of local factors including logistical infrastructure, political stability, environmental standards, and climate conditions, suggesting that generalized models of tourism-led growth are often inadequate²².

Parallel to the expansion of tourism, the environmental repercussions of economic development have come into sharp focus, revealing a dualistic narrative within the academic discourse²³. One strand of literature firmly associates tourism development with increased energy consumption and transport-related carbon emissions, positioning the industry as a significant contributor to environmental degradation²⁴. This is particularly relevant in the context of large-scale initiatives like China's Belt and Road Initiative (BRI), which, through infrastructure-led foreign direct investment (FDI), enhances connectivity and stimulates tourism and economic activity, but concurrently creates substantial environmental pressures through increased energy demand and ecological disruption²⁵. In contrast, a competing scholarly perspective emphasizes the potential for sustainable tourism, arguing that eco-tourism and green policies can mitigate pollution and ensure long-term ecological sustainability, thus aligning economic growth with environmental stewardship²⁶. This duality extends to the role of FDI, which can be a source of both polluting technologies and advanced, environmentally friendly innovations, creating an empirical ambiguity that is further complicated when FDI is specifically directed towards infrastructure that enables tourism growth²⁷.

The complex interlinkages between tourism, investment, and the environment necessitate a deeper investigation into moderating factors and theoretical frameworks²⁸. Technology innovation is often cited as a critical mechanism for decoupling economic growth from emissions, through the adoption of renewable energy and green technologies²⁹. Furthermore, the theoretical foundation of the Environmental Kuznets Curve (EKC) hypothesis provides a framework for testing the relationship between economic development and environmental degradation, suggesting that emissions may initially rise with income before eventually declining³⁰. The transformative potential of the BRI, with projections suggesting its economies may account for half of global GDP by 2030, makes it a critical case study for examining these dynamics³¹. Yet, a significant gap remains in the literature: a lack of nuanced, empirical studies that simultaneously model the combined impact of infrastructure-led FDI, tourism development, and technological progress on carbon emissions across the heterogeneous regions of the BRI (Figure 1)³². Existing research often treats these drivers in isolation, failing to capture their synergistic effects and regional variations, a gap this research aims to fill by employing a methodological approach that accounts for both common correlated effects and cross-sectional disparities³³.

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

This research analyzes EKC in the nations along with BRI and examines the correlation between Chinese outward FDI stocks and carbon emissions. Generally, the effects of economic activity on the environment may be broken down into three categories: encompasses what authors labeled as the Technology Innovation (TI) effect, the industrial composition effect, and the economic scale effect. Therefore, apart from the main features of the study, authors are depicting these critical features. The following is a perception of this correlation:

   (1)

In this model, several variables are used to represent various economic indicators. These include:

CO2: carbon emissions, TD: Chinese outward foreign direct investment stocks in Belt and Road Initiative countries, TI: TD indices, GDP: gross domestic product, SQGDP: squares of GDP (used to denote the Environmental Kuznets Curve), and IVA: industrial value-added.

The model uses a subscript to indicate the number of countries and a subscript ε_{it} to indicate time. The parameters in the model are represented by β_{1} + β_{2}  + β_{3}  + β_{4} + β_{5} + β_{6} and β_{7} . The term μ_{i} refers to a cross-sectional specific factor, ε_{it} and represents the mistaken expression.

The BRI has considerably enhanced communication between China and other contributing nations, importantly impacting power usage, fiscal growth, and environmental conditions. Steps taken by the Chinese government to develop the green BRI in line with the Paris Agreement (2015) and the SDGs 2030 include the spread of technological advantages and the growth of efficient and environmentally friendly energy infrastructure³⁴. Gu and Zhou³⁵ examined recently built clean power projects in BRI respective nations with 16 GW of installed capacity and helped reduce greenhouse gas emissions by 49 metric tons, reaffirming these approaches. Additionally, Li et al.³⁶ suggested that the Belt and Road Initiative boosts the hosting nation's power efficacy and economic development by constructing reliable energy and logistical facilities. However, China's FDI worsens ecological problems in hosting countries because of growing demands for power and natural resources. Depending on this rationale, it is projected that China's foreign direct investment abroad may have an impact on carbon emissions in various places that is both beneficial and harmful:

In the latest study, Bekun et al.³⁷ investigated the hypothesis that tourism revenues contribute to lower emissions, but visitor arrivals have the opposite impact. In addition, the synthesis of the scientific studies (see Table 1) emphasized that tourist variables caused specific environmental consequences. To comply, this research conducts an accumulated tourist engagement indicator that aids in removing subjective bias. The overall tourism business indicator might have a beneficial or detrimental effect on CO2.

Comparable to how increased connectivity, fewer barriers to competition (including both people and goods), faster shipments, and infrastructure improvement between BRI member nations encourage the expansion of the tourist industry, which could have a pivotal effect on environmental quality, given the inherent characteristics of the tourism economy, it might be advantageous.

Given the fundamental nature of economic activity, more energy and fuel consumption, which results in increased CO2, are necessary. Nevertheless, technical advancement to create ecologically friendly products and procedures, as well as innovation-led changes in energy supply, can significantly impact environmental sustainability. TI decreases carbon dioxide emissions in this way.

The EKC theory is supported by the economic growth being linked to increasing energy consumption³⁸, which raises emissions levels, but the square of development in the economy lowers carbon dioxide emissions.

One key element influencing the host nations' carbon dioxide emissions is the commercial makeup of those nations (Table 2). IVA may have a beneficial effect on CO2 emissions since increasing industry significance in BRI neighboring countries will result in increased energy consumption and associated emissions³⁹.

Data source and sample selection

The importance of BRI nations is predicated on the assumption that they represent 30% of the world's GDP, 64% of its inhabitants, 39% of its territory, 35% of its commerce, 50% of its power generation use, and 54% of its emissions of CO₂. In addition to these data, the BRI area has experienced enormous growth in Chinese FDI, making it the most effective collaboration platform in the globe⁴⁰. Consequently, authors investigate the ecological effects of China's outbound foreign direct investment, TD, and TI in the chosen 2 (54) Belting and Roads node nations from 2000 to 2020. To do this, the article makes use of yearly data on CO₂ emissions (metric tons), Chinese outward foreign direct investment stocks (constant 2010 US$), the TD indicator (for more information, see Table 3), and the total index (TI), which is calculated as the total amount of patents (for inhabitants and immigrants), the GDP (continual 2011 US$), and the manufacturing values (% of GDP). It is crucial to provide a consistent test statistic when resolving the problem of differential attributes since these factors display diverse measurement units. Considering previous research, the authors transformed all the aspects into the regression model, which produces results in the form of elasticity that facilitate data interpretation. Apart from Chinese external foreign direct investment capital, which is taken from China's Department of Economy and Trade, information for these factors is taken from the World Development Index (WDI).

TD index

Numerous metrics are employed in tourism studies to describe tourist activities. Tourism revenues⁴¹, tourist spending Pata and Balsalobre-Lorente⁴², and admissions⁴³ data are used to quantify comprehensive tourism demand. These tourist models have several drawbacks since they only account for a portion of the link between pollutants. These three tourist variables are very collinear, and if they are utilized concurrently in a unified framework, it might result in the multicollinearity issue, according to Meng et al.⁴⁴, PCA combines common fluctuations from all tourism variables into a unique accumulated score. These tourist metrics are drawn from the WDI dataset, which aids in creating a distinctive composite indicator by fusing the very critical data.

TD index through PCA

The Tourism Development (TD) index was constructed using Principal Component Analysis (PCA) to consolidate the three highly correlated tourism variables (international tourism receipts, expenditures, and arrivals) into a single composite score. The suitability of the data for PCA was first validated using two standard tests. The Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy yielded a value of 0.723, exceeding the recommended threshold of 0.6, indicating that the patterns of correlation were compact and thus suitable for factor analysis. Furthermore, Bartlett's Test of Sphericity was statistically significant (p < 0.001), confirming that the correlation matrix was not an identity matrix and that the variables were sufficiently correlated for PCA. Based on the Kaiser criterion (eigenvalue > 1), only the first principal component was retained. This component satisfactorily explained more than 81% of the total variance across all three tourism variables. The factor loadings for all three variables were positive and high, supporting a balanced weighting in the computation of the final index. The resulting TD index scores ranged from -0.681 to 5.157 for the sample, where a lower value indicates a lower level of tourism development, and a higher value indicates a greater level of tourism development among the BRI nations.

Analytical framework

Researchers began the analysis by testing two essential panel data properties: cross-sectional dependence (CSD), which indicates interconnectedness between countries, and slope heterogeneity (SH), which signifies that the variable relationships differ across countries. Confirming these properties was a critical first step, as it necessitated the use of specialized second-generation econometric tests for accurate results.

Cross-sectional dependence (CSD) and slope heterogeneity (SH)

The premise of sloping variability and cross-sectional dependence maintained by second-generation (SGN) unit root (UR; including CS supplement IPS UR testing) and cointegration tests makes it necessary to verify before advancing to the factor's integration order. Because of the rapid fluctuations in financial and monetary projects succeeded by changes in the economy, the banking collapse, globalization, the interconnectedness of royalties, and missing visual and undetected common elements, data series inevitably pose the CSD problem. Prior to using UR and cointegrating tests, CSD enables us to select the most effective method to solve the problems that have been revealed. The research utilized a more sophisticated approach based on Tang et al.⁴⁵, CD testing to validate CSD. Additionally, the article adopts the Zhuang et al.⁴⁶, SH testing, which is better than conventional heterogeneity analyses like SURE that do not permit CSD in panels⁴⁷. The slope heterogeneity analysis formulas are as follows:

   (2)

   (3)

Unit roots test (URT)

A further phase is to use the proper unit root experiments to ensure the integration order once CSD and slope heterogeneity have been verified. The order of integration for all factors is determined by the cross-Sectionally augmented Dickey-Fuller (CADF) and cross-Sectionally enhanced IPS (CIPS) models. First-generation testing is the initial two URT, while SGN analyses are the third URT Since it permits CSD while computing variable unit roots, CIPS is better. The conventional panel unit root tests give false findings when CSD is present. Despite this, the current research employs basic and sophisticated unit root tests for objective and consistent estimations. The following is the CIPS equation:

   (4)

The Equation below uses the symbol to indicate the average across different sections.

   (5)

The CIPS statistical tests are as follows:

   (6)

Panel cointegration tests

Authors then estimate the cointegration connection between carbon dioxide emissions and their causes, including Chinese outbound FDI, TD, TI, and economic expansion, following confirming CDS, slope heterogeneity, and variation stationarity level. The research does this by utilizing the panel cointegration approach, which yields realistic predictions when erroneous factors are cross-sectionally dependent, and panels are diverse. Since this test does not impose typical element constraints while considering CSD and structural breakdowns, it is preferable to first-generation cointegration tests. Additionally, traditional panel estimation methods that use unobserved heterogeneity, fixed effect models, and instrumental variables do not consider the CSD of constant variance, resulting in inaccurate estimations. In contrast to the second-generation cointegration test, the panel cointegration technique addresses structural breaks at various locations for each cross-section in addition to CSD, SH, and serial correlation of mistaken expressions. The paper includes the cointegration test. The structural breakdown and paradigm changes affect genuine values. This analysis provides cointegration predictions without pollution, level break, or paradigm change (Table 4).

Cross-sectionally augmented autoregressive distributed lags

Authors may now consider experimental assessments of the long-term connection between greenhouse gases and their drivers. The traditional prediction model presupposes cross-sectional stability, which is not the case in the initial investigations. Given that the amount of carbon pollution is controlled by the unseen relevant variables that induce reliance on cross-sectional error terms, this is an implausible hypothesis. Several factors, including the unequal and sudden rise of Chinese external FDI and the change in financial and political frameworks, lend conceptual credibility to cross-section reliance on the Belt and Road program. This is because the natural characteristics may be distorted by the presence of unseen relevant variables that are linked with the regression analysis. The CS-ARDL estimation provides the best solution to the issue of cross-section dependence and slope heterogeneity. Propose a dynamic common correlated effects strategy that the CS-ARDL method uses to address these concerns. This leads us to the following derivation of the CS-ARDL formula:

   (7)

Simple autoregressive distributed lags (ARDL) models have the linear function shown above in Equation (7); if the authors continue with this formula, they will get mixed results depending on whether cross-sectional dependencies occur. Equation (7) is generalized to provide Equation (8) by averaging over cross-sections. The cutoff point impact is only present if cross-sectional dependency is considered, and this procedure will help eliminate that potentially misleading assumption.

   (8)

Where X_t-1 = (CO_2i,t–₁, Z_i,t–₁) may be thought of as the mean of the dependent and independent variables, P_w,P_z,P_x Identify all delays within every parameter. CO_2i,t, Carbon dioxide emissions (tCO2) is an example of a dependent variable, and Z_i,t is a vector that includes all the different factors. Cross-sectional averages (including time dummy or pattern variables) are shown by X demonstrating how they may be used to avoid CSD caused by ripple effects. The CS-ARDL uses short-run coefficients as inputs to calculate long-run coefficients. For the average number estimator and the long-run coefficient.

   (9)

The mean group is given as:

   (10)

Short-run coefficients are estimated as:

   (11)

Where,

    (12)

   (13)

    (14)

    (15)

In the event of a disturbance or the timeframe needed to attain constant balance, the mistake correcting procedures for CS-ARDLs are comparable to an organized average panel, which sets the pace of modification towards prolonged balance.

Robustness estimators

Traditional estimation procedures are incredibly opposed to this committee's acknowledged existence of slope heterogeneity and variance decomposition. Moreover, the research uses the Ordinary Connected Effect Average Panel measure established by Pesaran to confirm similar things. Even with CSD, heterogeneity, endogeneity, and non-stationarity, CCEMG can be used effectively (Table 5). It also has correlation problems, especially with the cross-sectional divisions. Following is the CCEMG formula:

   (16)

Granger causality

Granger causality is used to analyze the article's components and their possible contributing relationship. Owing to its core assumptions, this method works better with diverse displays. Against the hypothesis test of causality between variables, the null hypothesis of the DH multiple regression analysis is that there is no causation. Following is a description of the practical structure of the DH Granger causality test:

   (17)

For an autoregressive model, the estimator is denoted by j and the autoregressive variables are represented by βj(j)

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.