Analyzing the Environmental Footprint and Its Determinants in China: An Integrated Assessment of the EKC and Pollution Haven Hypotheses

Summary

This research confirms that energy use and open trade degrade the environment, supporting the pollution haven hypothesis. It advises policymakers to pursue sustainable tourism, diversify energy sources, and attract foreign investment into service sectors for better ecological outcomes.

Abstract

This study provides a comprehensive empirical investigation into the determinants of China's environmental footprint from 1990 to 2023, with a specific focus on testing the Pollution Haven Hypothesis (PHH) and the Environmental Kuznets Curve (EKC) in the context of travel and tourism. Employing the EKC framework, we analyze the dynamic impacts of eco-tourism, economic growth, energy consumption, trade openness, and foreign direct investment on ecological degradation. The methodology involves advanced statistical tests to establish the existence of a long-run equilibrium relationship among the variables. The findings confirm an inverted U-shaped EKC relationship between travel and tourism development and the environmental footprint, indicating that the sector initially exacerbates environmental degradation but, beyond a certain threshold of economic development, contributes to its amelioration. Further results indicate that energy utilization and trade openness significantly intensify the environmental footprint. Crucially, foreign direct investment is found to escalate environmental degradation, thereby providing robust empirical support for the Pollution Haven Hypothesis in the Chinese context. Consequently, this study recommends that policymakers prioritize the transition towards sustainable tourism models, accelerate the diversification of the energy mix towards cleaner sources, and strategically channel foreign direct investment into less polluting service sectors.

Introduction

Global climatic changes resulting from human actions are making a low-carbon economy an unavoidable necessity for the local authorities of the global edge¹. Unusual weather occurrences can disrupt stream flow, the dynamics of the organisms within an ecosystem, instabilities in agricultural production², damage associated with infrastructure, or increased morbidity and mortality, thereby indicating that the socio-economic, biological and geographical processes have induced variability and forced scholars to proceed to the driving of this change³. Ecotourism is associated with ecological costs becaus....

Protocol

Theoretical framework and variable selection
The empirical model and variable selection for this study are explicitly derived from a synthesis of foundational theories that explain the dynamic relationship between economic development and environmental sustainability. The investigation is principally guided by the Environmental Kuznets Curve (EKC) hypothesis⁴⁰, which posits an inverted U-shaped trajectory where environmental degradation initially increases with economic growth but eventually declines after a certain income threshold is reached. To test this core proposition, Gross Domestic Product (GDP) is included as the....

Results

Preliminary Analysis
A thorough preliminary analysis of the data is a fundamental prerequisite for robust econometric modeling, as it establishes the essential characteristics and behavior of the underlying variables. This stage involves examining descriptive statistics to understand central tendencies, dispersion, and normality, while graphical inspections such as box plots and growth rate trends provide critical visual insights into the distribution, variability, and temporal evolution of the ser.......

Discussion

The findings of this study confirm the complex relationship between economic activities and environmental degradation in China, with critical implications for policy. Our application of the Toda-Yamamoto causality test and the ARDL cointegration method provided a robust framework for disentangling these dynamics. The success of this analytical protocol hinged on two critical steps: first, the establishment of a stable long-run cointegrating relationship among the variables, which validated the existence of a fundamental .......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Data Sources
Environmental Footprint (EF) Data	Global Footprint Network (2018)		Serves as the dependent variable capturing the ecological pressure from economic activities; replaces traditional pollution indicators.
Tourism & Travel (T&T) as % of GDP	World Data Atlas; China Statistical Yearbook; China Bureau of Statistics		Measures the tourism sector’s contribution to economic activity and its environmental implications; used to test tourism-led EKC by including T&T2.
GDP (Economic Development Indicator)	World Development Indicators (WDI)		Core variable for testing the EKC hypothesis and modeling the scale effect of economic growth.
Energy Use (EU)	World Development Indicators (WDI)		Captures the pollution intensity of energy consumption, aligned with the IPAT framework.
Joint Venture (JV) / FDI Proxy	Ministry of Commerce of China, Chinese Investment & JV Records		Used as a proxy for FDI to test the Pollution Haven and Pollution Halo hypotheses within China.
Open Trade (OT)	World Development Indicators (WDI)		Represents trade openness to evaluate scale, composition, and technique effects on EF.
Constructed Variables
Tourism Non-linear Term (T&T²)	Computed squared term of T&T as % of GDP		Allows testing for non-linear EKC-type behavior within the tourism sector.
Log-Transformed Variables	Natural logarithm applied to EF, T&T, GDP, EU, OT		Ensures elasticity-based interpretation, reduces heteroscedasticity, and improves model fit.
Software & Tools
Econometric Software	EViews / Stata / R (not specified but required)		Used for time-series transformation, econometric estimation (ARDL, cointegration tests), diagnostics, and causality tests.
Econometric Methods & Tests
Unit Root Testing	ADF, PP (implied), stationarity diagnostics for time-series		Determines whether variables are I(0) or I(1) prior to ARDL estimation.
ARDL Bounds Testing	Pesaran et al. (2001) ARDL methodology		Evaluates long-run cointegration among EF and its determinants; handles mixed orders of integration.
Fisher-Johansen Combined Cointegration Test	Engle–Granger, Johansen, Boswijk, Banerjee-Dolado-Mestre (BDM)		Validates cointegrating relationships using multiple integrated cointegration statistics.
Long-run and Short-run ARDL Estimation	ARDL-based ECM framework		Estimates short-run elasticities and long-run equilibrium relationships affecting EF.
Error Correction Term (ECT)	Derived from ARDL long-run equation		Measures the speed at which EF returns to equilibrium after short-run shocks.
Structural & Stability Diagnostics	CUSUM and CUSUMSQ tests		Tests for structural stability and absence of parameter shifts across time.
Causality Analysis	Granger causality (within ARDL framework)		Determines directional causality between EF, tourism, GDP, EU, OT, and JV.