The Fairness Evaluation on Achieving Sustainable Development Goals (SDGs) of Ecological Footprint: A Case Study of Guanzhong Plain Urban Agglomeration

Abstract

The sustainable development of the Guanzhong Plain Urban Agglomeration (GPUA), which is a pivotal Belt and Road hub, is critical for achieving the UN’s 17 SDGs. Based on the ecological footprint (EF) model, this study innovatively constructs a three-dimensional framework integrating natural and human-made capital, using the Gini coefficient and spatiotemporal analysis to evaluate resource allocation fairness in the GPUA from 2005 to 2022. Key findings include the following: (1) EF and GDP grew continuously at annual rates of 11.43% and 11.87%, while ecological carrying capacity (EC) stabilized, pushing the GPUA toward its ecological threshold under the Environmental Kuznets Curve (EKC). Moreover, the increasing Ecological Pressure Index (EPI) shows that after 2014, the GPUA has trended toward “extremely unsafe” status. (2) The ecological carrying capacity Gini coefficient (G₁, 0.1710–0.6060) fluctuated significantly, while the economic contribution Gini coefficient (G₂, 0.1039–0.3519) showed a narrow upward trend; since 2015, the comprehensive Gini (G < 0.4) indicates that the EF aligns with its EC and economic contribution. (3) The GPUA shows fair resource allocation. Tongchuan, Baoji, and Xianyang are low economic contribution and high ecological contribution; Xi’an and Yangling Demonstration Zone are high economic contribution and low ecological contribution; Weinan is low ecological contribution and low economic contribution. These findings provide critical insights for hub urban agglomerations to achieve the 17 SDGs through fair ecological resource allocation and sustainable development.

1. Introduction

The sustainable development of urban agglomerations does not pertain to a single city alone, but rather to the coordination among cities in achieving the 17 Sustainable Development Goals (17 SDGs) [1]. Therefore, the evolution of resource allocation fairness across spatiotemporal dimensions has emerged as a pivotal research agenda in sustainability science [2]. Under the dual challenges of institutional collective action dilemmas in urban agglomerations and persistent ecological distribution conflicts [3], addressing the “tragedy of the commons” through precise identification and effective mitigation strategies remains a critical challenge confronting both academic research and practical governance. However, after almost half a century of Reform and Opening-Up, China’s urban agglomerations now confront three critical resource allocation challenges: (1) The illusion of regional economic integration resulting from homogeneous urban development patterns; (2) Large-scale land acquisition practices violating the cultivated land protection Red Line policy; and (3) Prevalent policy-driven rent-seeking behaviors among local governments [4]. These phenomena have collectively triggered profound socioeconomic and environmental repercussions. This reflects the structural dilemmas in urban agglomeration development, characterized by cumulative environmental injustice and ecological “free-riding” [5]. Specifically, core dominant regions engage in unfair and irrational ecological exploitation of vulnerable peripheral regions in terms of natural resource demand and environmental pollution discharge, which lead to imbalances in sustainable development.

The unfair allocation of natural resources can lead to failures in ecological and environmental governance, resulting in the emergence of “pollution havens”. In 2015, the United Nations established the SDGs, which consist of 17 overarching goals and 169 sub-goals. Among these, land use related to SDG15 (Life on Land), carbon emissions related to SDG13 (Climate Action), and water resource utilization related to SDG6 (Clean Water and Sanitation) are crucial for urban agglomerations to achieve the 17 SDGs. However, the Living Planet Report: China 2015 published by the World Wildlife Fund (WWF) revealed significant regional disparities in ecological footprint pressure across China in 2012. Provincial-level analysis highlighted marked variations in per capita ecological footprints, while variability was observed in approximately half of the nation’s total biocapacity. Within the ecological economics framework, when analyzing the trade-offs between fairness and efficiency in optimizing economic services relative to resource consumption, scholars typically employ the concept of absolutely scarce natural capital as a foundational analytical pillar [6]. Natural capital refers to the comprehensive term encompassing both natural resources provided by ecosystems and the ecological services they deliver. The ecological footprint theory is fundamentally rooted in the concept of natural capital, essentially constructing a framework where ecological footprint quantifies the demand for natural capital [7], while ecological carrying capacity evaluates its supply [8]. In alignment with neoclassical economics, ecological economics shares the perspective that natural capital and human-made capital constitute interconnected systems of complementary components [9]. Human-made capital, is generated through the depletion of natural capital while concurrently producing direct economic benefits [10]. These economic benefits are quantified through prevailing market prices of goods and services [11]. Typically, GDP functions as a metric for returns on human-made capital. From a resource management perspective, the fairness and efficiency of regional resource allocation are critically manifested in real-world challenges encompassing the distribution of natural and human-made capital, the effectiveness of their allocation, and the impact of inefficient allocation by inefficiently allocated zones on interregional fairness.

Druckman proposed the initial application of the Gini coefficient to estimate inequalities between neighborhoods in the consumption of specific consumer goods, assess interregional fairness in resource consumption and pollution emissions, and subsequently formalized the Area-Based Gini coefficient (AR-Gini), presenting pilot Lorenz curves and estimates of the AR-Gini coefficients for selected commodities [12]. Sun and Chen et al. utilized the Gini coefficient with calibrated fairness metrics to assess fairness in wastewater discharge and freshwater resource allocation [13,14]. However, their work lacks implementable evaluation matrices, multidimensional spatial analysis across administrative boundaries, and integration of natural and human-made capital frameworks. In other fields of sustainable development, there are numerous existing studies on fairness evaluation. Samani et al. developed a Public Participation GIS (PPGIS) with fuzzy Best-Worst Method (BWM) to evaluate healthcare spatial fairness [15]. Arezoumand and Smadi proposed a transportation asset management framework balancing economic, environmental, and social fairness [16]. Shruti et al. designed the Smart City Environmental Sustainability Index (SCESI) for Indian cities, emphasizing waste, water, and air quality metrics [17]. Within China, studies on the fairness of ecological resource allocation predominantly focus on interprovincial and intraprovincial administrative boundaries. For instance, Zhong et al. formulated a Resource-Environment Gini coefficient from the perspective of ecological carrying capacity, revealing significant regional disparities in environmental resource distribution within Guangdong Province [18]. Tian et al. employed the Gini coefficient to analyze evolving patterns of cropland carbon sink surplus across China from 2000 to 2012, revealing widening disparities among surplus-abundant regions [19]. Dong et al., in their fairness assessment of water allocation, identified GDP, population size, cultivated land area, and water availability as key determinants influencing water use patterns. Their Gini coefficient-based analysis revealed that the majority of Chinese regions exhibit moderate to high levels of unfair water use [20]. Lu et al. developed economic contribution coefficients and EC coefficients to evaluate interprovincial fairness and heterogeneity in energy consumption-related carbon emissions across China, ultimately categorizing provincial emission profiles into distinct typologies [21]. Tian et al. evaluated water footprint sustainability for five major crops, integrating ecological economics to emphasize interprovincial fairness as a key constraint [22]. Song et al. proposed a weighted ZSG-DEA model for provincial energy and emission quota allocation, balancing fairness and efficiency through optimized Pareto solutions [23]. Wang et al. applied the symbiosis theory to urban–rural systems, identifying spatial imbalances in resource allocation and advocating for coordinated development mechanisms [24]. Pang et al. introduced Gini coefficients to assess ecological–economic fairness in the Beibu Gulf urban agglomeration, highlighting disparities in resource utilization efficiency [25]. Li et al. developed the RSECI index for ecological quality monitoring in the Lijiang Basin, linking urbanization pressures to ecosystem degradation [26]. Overall, previous studies often use methods like the Gini coefficient to evaluate regional ecological resource allocation fairness. However, few integrate analytical frameworks for natural and human-made capital from the ecological footprint model’s perspective, thus failing to form a replicable assessment framework for this fairness. Focusing on research in China, while significant disparities in ecological resource allocation have been identified, there is limited attention to ecological resource fairness issues caused by the structural dilemmas of generalizable hub urban agglomerations. Additionally, no general solutions have been proposed for data-scarce inland hub urban agglomerations, limiting the systematic assessment of ecological resource allocation fairness in geographical hub regions.

This study pioneers an integrated framework through the GPUA case, employing the Ecological Resource Allocation Gini Index (ERA-Gini) for spatiotemporal trends analysis and category classification (see Figure 1). Our marginal contributions are as follows: (1) Within the ecological–economic framework, we innovatively construct a three-dimensional analytical framework. This framework integrates the ecological footprint (EF), the ecological carrying capacity (EC), and GDP, which, respectively, represent natural capital demand, natural capital gain, and human-made capital gain. It provides a new tool for evaluating the fairness of ecological resource allocation; (2) Previous research has paid limited attention to the GPUA. As a core hub of the Belt and Road Initiative, the GPUA’s economic development heavily relies on natural capital despite its acute ecological vulnerability. The coordination mechanism between its natural capital demand and human-made capital gain has not been fully explored. This study fills this gap and provides actionable insights for similar hub urban agglomerations; (3) We use a multi-dimensional data conversion to overcome bottlenecks in energy consumption accounting. This approach accurately quantifies energy consumption to calculate ecological footprints under data scarcity, offering a replicable solution for ecological assessment in data-scarce regions.

2. Materials and Methods

2.1. Study Area

The Guanzhong plain (see Figure 2), situated at the geographic core of China’s interior, constitutes a pivotal hub along the New Eurasian Land Bridge (NELB) corridor while serving as the principal gateway orienting Western China toward socioeconomic exchanges with Central-Eastern regions. Situated in the Qinling Mountains’ northern foothills and bordering the Yellow River to the east, the Guanzhong plain, renowned as both a seminal birthplace of Chinese civilization and the historic starting point of the Silk Road, possesses distinctive cultural and geographical characteristics. At its core lies Xi’an, the imperial capital of 13 dynasties including the Zhou, Qin, Han, and Tang eras, making it the only surviving capital city among the Four Great Ancient Civilizations that continues to thrive as a metropolitan center. Emerging from this unique and strategic heartland, primordial ancestor worship traditions and the imperial cultures of Han and Tang dynasties developed and thrived here, constituting fundamental elements of Chinese cultural identity that have profoundly influenced East Asian civilization. The GPUA plays a pivotal role as a strategic hub in western China, combining economic and geopolitical influence. In 2018, the National Development and Reform Commission (NDRC) and the Ministry of Housing and Urban–Rural Development (MoHURD) of China formally ratified the Development Plan for the GPUA. This integrated network encompasses Xi’an (the provincial capital), Xianyang, Baoji, Tongchuan, Weinan, and the Yangling Agricultural High-Tech Industries Demonstration Zone (hereafter Yangling Demonstration Zone), among other administrative divisions. In 2022, the GPUA achieved a regional GDP of $280 billion, comprising 60.78%, slightly surpassing the World Bank’s Upper-Middle Income threshold (lower limit: $4256 in 2022) [27]. These metrics collectively establish the area as Northwestern China’s most economically robust region. The GPUA is characterized by diverse ecosystems including forests, grasslands, and wetlands, with a strategic geographical advantage as a vital transportation hub in Western China. It hosts a robust industrial framework encompassing aerospace, defense technology, scientific education, and tourism, with a 3.2% R and D investment intensity surpassing the national average. Additionally, six UNESCO World Heritage sites (e.g., “Silk Roads: the Routes Network of Chang’an-Tianshan Corridor” [28] and “Mausoleum of the First Qin Emperor” [29] are located within this urban agglomeration, spanning two cultural and historical categories. Notably, Xi’an, designated as a National Central City under China’s 14th Five-Year Plan for National Economic and Social Development [30], anchors the region’s significance as a globally renowned historical and cultural metropolis.

Under the current consensus of ecological fairness, the key challenges for achieving sustainable development in the GPUA are as follows:

First, intensifying land-use conflicts. As a net population inflow region, the GPUA faces rapidly growing demands for urban construction and infrastructure development. The ratio of production-living land to ecological land has approached 1:1. Early stage urban development has left numerous old urban districts, where preserving historical and cultural landscapes requires substantial land allocation.

Second, imbalanced energy structure and carbon emission challenges. Fossil fuels dominate the energy mix, with coal accounting for over 70% of total energy consumption in the urban agglomeration. Industries exhibit “high-carbon” characteristics. Energy-intensive industries in Xianyang/Weinan emit 40% of regional CO₂ but occupy only 25% green space, and while clean energy adoption remains limited, it creates significant pressure for improving energy efficiency. Severe water pollution persists in certain sections of the Wei River Basin (the region’s primary water system), coupled with critical groundwater over-extraction issues. Furthermore, air pollution remains acute, with frequent heavy pollution episodes pushing environmental carrying capacity to its limits.

Third, inadequate innovation systems in core industries. The localized conversion rate of scientific achievements remains below 30%, resulting in insufficient momentum for regional economic development. The vulnerability of the “nature–economy–ecology” system continues to escalate. Consequently, the region faces persistent ecological challenges including excessive energy dependence, severe vegetation degradation [31], and suboptimal environmental quality [32]. These issues will remain critical concerns within the 17 SDGs-aligned development paradigms over the coming decades.

2.2. Data Sources

This study selects six cities/districts—Xi’an, Tongchuan, Baoji, Xianyang, Weinan, and Yangling Demonstration Zone—as the data sample regions. Data for biological resource accounts, energy resource accounts, and environmental emission accounts are sourced from the Shaanxi Statistical Yearbook (2005–2015). Notably, for the energy resource account, we proposed an innovative multi-dimensional data conversion approach to overcome data bottlenecks and enable accurate accounting with scarce data. We also overcame issues like inconsistent data calibers in the biological resource account and limited availability of land data. For details, see Appendix A.

2.3. Methodology

2.3.1. Gini Coefficient

The Gini coefficient, internationally recognized as a statistical measure of income distribution inequality across national or regional populations, quantifies disparity on a normalized scale bounded within [0,1]. Values approaching 0 indicate near-perfect egalitarian distribution of economic resources, while values approaching 1 denote maximal wealth concentration. While existing studies often adopt a Gini coefficient threshold of 0.4 as a heuristic benchmark for inequality assessment [33], comprehensive queries of official statistical terminologies from authoritative bodies—including the United Nations Statistics Division (UNSD), World Bank, Eurostat, and the Organization for Economic Co-operation and Development (OECD)—using both “Gini” and “Gini coefficient” as search parameters revealed no institutionally endorsed classification intervals or empirical justification for such stratification thresholds. Consequently, the 0.4 threshold was retained as a provisional benchmark Gini coefficient without granular stratification of evaluation gradients.

The fairness of EF allocation in the GPUA can be evaluated through the calculation of EF and their corresponding Gini coefficients of fairness metrics. Given the current methodological limitations in delineating resource appropriation sources between intra-agglomeration (GPUA) and extra-regional origins, this investigation specifically quantifies urban contributions to ecological resource disparity within the agglomeration system. We conceptualize the GPUA as a closed system where resource flows are confined to intra-regional circulation, thereby precluding the expression of resource utilization from external regions. The Gini coefficient was determined by employing a geometric approach to calculate the trapezoidal area under the Lorenz curve [13], with the computational formula expressed as:

$$Gini coefficient=1-{\displaystyle \sum _{n-1}^i(X_i-X_{i-1})}(Y_i-Y_{i-1})$$

(1)

In the formula, X_i represents the cumulative percentage of the fairness evaluation index for the city/district ranked i-th, while Y_i denotes the cumulative percentage of the total EF for the city/district ranked i-th. When i = 1, (X_i−1, Y_i−1) is set to (0, 0). To comprehensively assess the influence of EC and economic contribution on the fairness of EF distribution, the comprehensive Gini coefficient is calculated as follows:

$$G={\displaystyle \sum _{\mathrm{k}=1}^{\mathrm{m}}{\lambda }_k}Gini\hspace{0.17em}coefficien t_k\hspace{1em}\left(k=1,2\right)$$

(2)

In the equation, Gini_K represents the Gini coefficient for the k-th fairness evaluation indicator, while λ_k denotes its corresponding weighting coefficient, reflecting the degree of influence exerted by the k-th indicator on EF fairness, with λ₁ + λ₂ = 1. The determination of λ_k values follows the methodology outlined in [34], where cultivated land area and GDP were selected to characterize EC (G₁) and economic contribution (G₂), respectively. The standardized PLS regression coefficients (0.013 for cultivated land and 0.114 for GDP) yielded final weights of λ₁ = 0.102 and λ₂ = 0.898.

2.3.2. Spatiotemporal Analysis Method

The Ecological Pressure Index (EPI) serves as a quantitative metric for assessing anthropogenic disturbances on ecosystems, employing the carrying capacity of coupled natural–economic–social systems as its reference benchmark. The computational formula is expressed as:

$$EPI={\displaystyle \frac{ef}{ec}}$$

(3)

In the EPI, ef (per capita ecological footprint, hm²/person) and ec (per capita ecological carrying capacity, hm²/person) serve as core metrics quantifying human–environment interactions. EPI < 1 indicates that anthropogenic disturbances remain below the self-regulation threshold of regional ecosystems under given conditions. Conversely, values exceeding this threshold may disrupt the ecological equilibrium. Sustained high EPI levels (EPI ≥ 1) risk surpassing environmental capacity thresholds, potentially triggering irreversible ecosystem collapse [35]. The classification standard of the EPI is shown in

Table 1. The classification standard of the EPI.

Ecological Security Level	Ecological Pressure Index (EPI)	Ecological Security Status
1	EPI < 0.50	Very safe
2	0.50 ≤ EPI < 0.80	Safer
3	0.80 ≤ EPI < 1.00	Slightly unsafe
4	1.00 ≤ EPI < 1.50	Less safe
5	1.50 ≤ EPI < 2.00	Not safe
6	EPI > 2.00	Extremely unsafe

[28,36].

The GDP-Normalized Ecological Footprint Intensity (EF_GDP) quantifies the biologically productive land area required per unit of economic output (¥10,000 yuan), serving as a critical indicator for assessing resource use efficiency in regional production systems. The metric is calculated as:

$$E{F}_{GDP}={\displaystyle \frac{EF}{GDP}}$$

(4)

Among them, EF represents the total ecological footprint. This inverse metric reflects regional resource utilization efficiency: a higher EF_GDP value indicates lower systemic resource-use efficiency, whereas reduced values demonstrate improved utilization patterns [37].

Elasticity coefficients quantify the relative growth rates between interdependent variables over defined periods. We propose two critical indices:

$$E={\displaystyle \frac{\Delta e{f}_{(i,i-1)}/e{f}_{(i-1)}}{\mathsf{\Delta }e{c}_{(i,i-1)}/e{c}_{(i-1)}}}$$

(5)

$$G={\displaystyle \frac{\mathsf{\Delta }e{f}_{(i,i-1)}/e{f}_{(i-1)}}{\mathsf{\Delta }GD{P}_{(i,i-1)}/GD{P}_{(i-1)}}}$$

(6)

The E in the equation represents the ecological pressure elasticity coefficient, while G represents the GDP-normalized EF elasticity coefficient. The definitions of these parameters are as follows:

∆ef_(i,i−1): Change in ef (hm²/person) from year i − 1 to i,

ef_(i−1): Baseline EF (hm²/person) at year i − 1,

∆ec_(i,i−1): Change in ec (hm²/person) from year i − 1 to i,

ec_(i,i−1): Baseline EC (hm²/person) at year i − 1,

∆GDP_(i,i−1): Change in per capita GDP (CNY/person) from year i − 1 to i,

GDP_(i,i−1): Per capita GDP (CNY/person) at year i − 1.

To address data scale distortion while preserving directional trends:

$$X^{\prime }=\{\begin{array}{cc}\hfill ln(X)\hfill & ,X>0\hfill \\ \hfill 0\hfill & ,X=0\hfill \\ \hfill -ln(-X)\hfill & ,X<0\hfill \end{array}$$

(7)

where X′ represents E′ or G′.

2.3.3. Ecological Support Coefficient

The cumulative percentage of the total EF for each city/district within the GPUA is plotted on the vertical axis (OY), while the cumulative percentage of EC is represented on the horizontal axis (OX). Cities/districts are sorted in ascending order based on their EF/EC ratios. This framework aims to illustrate that occupying a certain proportion of ecological resources requires contributing a corresponding proportion of EC, thereby describing the matching degree between EF and EC. The Lorenz curve is used to depict the actual distribution curve, while a fixed proportion of EF represents the absolute equal distribution curve.

The Ecological Support Coefficient (ESC) is used to assess the fairness of the EF allocation among various cities/districts [38]. The calculation formula is as follows:

$$ESC={\displaystyle \frac{E{F}_i}{EF}}/{\displaystyle \frac{E{C}_i}{EC}}$$

(8)

The ESC serves as a critical metric to evaluate the fairness of ecological resource allocation among cities/districts within the GPUA. Here, EF_i and EC_i represent the EF and EC of individual cities/districts, respectively, while EF and EC denote the aggregate footprint and carrying capacity of the entire urban agglomeration. An ESC value of less than 1 (ESC < 1) indicates that a city/district contributes a higher proportion of EC relative to its EF, demonstrating a positive externality through its role as a net biocapacity provider that supports regional sustainability. Conversely, an ESC value exceeding 1 (ESC > 1) reflects a scenario where a city/district’s EF disproportionately outweighs its EC contribution, revealing a negative externality characterized by resource overconsumption and heightened pressure on the regional ecosystem. This framework quantifies the spatial mismatch between resource demand and biocapacity supply, offering actionable insights for balancing interregional ecological fairness.

2.3.4. Economic Contribution Coefficient

The vertical axis (OY) represents the cumulative percentage of the total EF of each city/district relative to the total EF of the GPUA. The horizontal axis (OX) denotes the cumulative percentage of the GDP of each city/district. Cities/districts are sorted in ascending order based on the ratio of their total EF to GDP. The purpose of this approach is to use the GDP of each city/district as a reference, reflecting the idea that occupying a certain proportion of ecological resources should correspond to contributing a proportionate share of GDP. This method describes the degree of match between EF and economic contribution. We think it could be changed to: The actual distribution curve is depicted by plotting the Lorenz curve. The Economy Contributive Coefficient (ECC) is an enhanced metric integrating pollutant emissions, resource appropriation, and economic output for comprehensive fairness assessment [25]. The formula is defined as:

$$ECC={\displaystyle \frac{E{F}_i}{EF}}/{\displaystyle \frac{G_i}{G}}$$

(9)

where EF_i and EF represent the ecological footprint of Guanzhong plain cities/districts and the GPUA, respectively; G_i and G denote the GDP of the Guanzhong plain cities/districts and the GPUA, respectively. If ECC < 1, it indicates that a city/district’s economic contribution rate exceeds its EF proportion, reflecting a higher economic efficiency and characteristics of sustainable development. Conversely, if ECC > 1, it suggests a lower economic contribution relative to a higher EF.

3. Results and Discussion

3.1. Trends of the Ecological Footprint in the GPUA

3.1.1. Trends Analysis of the Ecological Footprint

From 2005 to 2022, the ef of the GPUA exhibited an upward trend, except for a decline in 2014, with an annual growth rate of 11.43%. In contrast, the ec remained nearly unchanged during the same period. Concurrently, the per capita GDP continued to grow at a higher annual growth rate of 11.87%, slightly outpacing the increase in ef (see Figure 3). It is noteworthy that since 2014, China has gradually shifted its economic development pattern from a scale-and speed-driven approach to a quality- and efficiency-oriented one, with economic growth transitioning from high-speed to medium-high speed. The GPUA is no exception. Specifically, the growth rate of per capita GDP has slowed down, while the ef has experienced leapfrog growth during the same period. Over the study period, if 2014 is taken as the demarcation point, the cumulative ef from 2015 to 2022 was twice that of 2005 to 2014. On the other hand, ec primarily depends on the area of corresponding land types. However, since land use areas remain relatively stable in the short term, no significant fluctuations are observed. Meanwhile, a regression analysis of the inverted U-shaped relationship between the ef and per capita GDP of the GPUA was conducted based on the panel data of six cities within the GPUA (see Appendix B). The results indicate that there is indeed an inverted U-shaped relationship between the ef and per capita GDP of the GPUA. Therefore, the increasingly prominent ecological pressure may have put the GPUA in a process of acceleration towards the ecological threshold of its inverted U-shaped Environmental Kuznets Curve (EKC) [39].

3.1.2. Dynamic Relationship Analysis of the Ecological Footprint

Based on Formulas (3)–(6), the spatiotemporal analysis indicators were calculated. According to Figure 4, the EPI increased from 0.6618 in 2005 to 4.7760 in 2022, representing a 7.22-fold growth with an annual growth rate of 12.33%. According to the classification standard in Table 1, since 2008, the EPI has exceeded 1, indicating a “slightly unsafe” state. In 2013, it surpassed 2, entering an “extremely unsafe” state. Although it slightly decreased to below 2 in 2014, it then rebounded to exceeded 2, remaining in the “extremely unsafe” state thereafter. This indicates prominent ecological pressure in the GPUA. Concurrently, the EF_GDP fluctuated and decreased from 2.6304 in 2005 to 2.4589 in 2022, with a cumulative reduction of 6.52% and an annual growth rate of −0.4%. These trends suggest that under the increasing pressure of economic slowdown, improvements in economic contribution have decelerated, while the accelerated spillover of nonlinearly growing stock externalities (e.g., accumulated environmental pollution) has gradually revealed latent ecological risks.

According to Figure 5, the ecological pressure elasticity coefficient (E′) is negative in all years except 2015 and 2019, indicating an inverse relationship between EF and EC changes. Similarly, the GDP-normalized EF elasticity coefficient (G′) shows cyclical fluctuations, with predominantly negative values during 2007–2011 and post-2021, suggesting a decoupling between economic growth and ecological pressure. This reflects a weaker economic contribution and rising environmental pressure in the GPUA. From 2012 to 2020, G′ remained predominantly greater than 0, indicating that the EF positively correlated with economic development. Furthermore, G′ fluctuated within a reasonable range, reflecting a relatively strong economic contribution from the GPUA. The economic scale effect drove improvements in structural and technological efficiency [40], thereby alleviating environmental pressure. Overall, the temporal variation in G′ partially reflects the U-shaped EKC. This suggests the need for further analysis of the spatiotemporal characteristics of ecological footprints to evaluate the fairness of EF allocation in the GPUA.

3.2. Fairness Evaluation of the Ecological Footprint in the GPUA

3.2.1. Temporal Dimension Evaluation

Based on

Table 2. Ecological carrying capacity Gini coefficient (G₁), economic contribution Gini coefficient (G₂), and comprehensive Gini coefficients (G) in the GPUA from 2005 to 2022.

Year	Ecological Carrying Capacity Gini Coefficient (G1)	Economic Contribution Gini Coefficient (G2)	Comprehensive Gini Coefficient (G)
2005	0.3694	0.1039	0.1310
2006	0.4860	0.1474	0.3709
2007	0.4970	0.1610	0.3827
2008	0.4982	0.1661	0.3853
2009	0.5023	0.1569	0.3848
2010	0.5040	0.1624	0.3879
2011	0.5247	0.1932	0.4120
2012	0.5757	0.2558	0.4669
2013	0.6060	0.2984	0.5014
2014	0.5898	0.2701	0.4811
2015	0.2293	0.2751	0.2449
2016	0.2296	0.2807	0.2469
2017	0.2298	0.3041	0.2550
2018	0.2300	0.3180	0.2599
2019	0.1710	0.3417	0.2290
2020	0.1712	0.3519	0.2327
2021	0.1714	0.3392	0.2284
2022	0.1716	0.3396	0.2287
Average value	0.3754	0.2481	0.3239
Coefficient of variation	0.4513	0.3287	0.3310

, Figure 6 and Figure 7, the ecological carrying capacity Gini coefficient (G₁) ranged between 0.1710 and 0.6060, and the coefficient of variation was 0.4513, exhibiting significant fluctuations. It reached a peak value of 0.6060 in 2013, followed by a gradual decline and stabilized near 0.1710 after 2019. This indicates that the GPUA has been dynamically fluctuating toward a fairer state, and the coordination between resource consumption and ecological capacity has transitioned from weak to strong. This implies that resource consumption and pollution emissions are increasingly aligned with the region’s ecological capacity. The economic contribution Gini coefficient (G₂) ranged between 0.1039 and 0.3519, and the coefficient of variation was 0.3287, exhibiting a year-on-year increasing trend with narrow fluctuations. It reached a peak value of 0.3519 in 2020, followed by a slight decline thereafter. Overall, the alignment between EF and economic contribution has been continuously optimized, and ecological demands are relatively evenly distributed among different cities/districts.

The comprehensive Gini coefficient (G) ranged between 0.1310 and 0.5014, and the coefficient of variation was 0.3287, with narrow fluctuations. G reached a peak value of 0.5014 in 2013, subsequently declining year-on-year. After 2015, G consistently remained below the warning threshold of 0.4. This indicates that the alignment between EF and economic contribution in the GPUA is well-matched. Specifically, development strategies balance both environmental capacity utilization and differentiated ecological resource demands (e.g., consumption patterns and resource allocation) across regions with varying economic development levels.

3.2.2. Spatial Dimension Evaluation

According to Formulas (8) and (9), the ECC and ESC of the GPUA from 2007 to 2020 are shown in

Table 3. Economy Contributive Coefficient (ECC) and Ecological Support Coefficient (ESC) in the GPUA from 2005 to 2022.

Year	Xi’an		Tongchuan		Baoji		Xianyang		Weinan		Yangling Demonstration Zone
	ECC	ESC	ECC	ESC	ECC	ESC	ECC	ESC	ECC	ESC	ECC	ESC
2005	1.0195	2.6443	0.9860	0.4790	0.7573	0.4335	0.8476	0.7472	1.4658	0.6875	0.3129	0.7659
2006	1.1168	2.9202	0.8439	0.4244	0.6145	0.3582	0.7118	0.6043	1.4708	0.6787	0.2558	0.6441
2007	1.2171	3.1879	0.7910	0.3930	0.5343	0.3052	0.6054	0.5190	1.3064	0.6060	0.2584	0.6944
2008	1.2245	3.1963	0.8134	0.4075	0.5379	0.3026	0.5777	0.5136	1.3239	0.6040	0.2362	0.6398
2009	1.2280	3.2541	0.7881	0.4055	0.5596	0.3071	0.5739	0.5034	1.2667	0.5648	0.2433	0.6877
2010	1.2513	3.2522	0.7914	0.4080	0.5487	0.3004	0.5549	0.5047	1.2365	0.5717	0.2487	0.6737
2011	1.3211	3.3659	0.7678	0.4036	0.5041	0.2735	0.5045	0.4678	1.1153	0.5447	0.2195	0.6295
2012	1.4621	3.6912	0.6218	0.3366	0.4042	0.2247	0.4074	0.3828	0.9061	0.4350	0.1923	0.5355
2013	1.5680	3.8828	0.5164	0.2888	0.3558	0.1951	0.3464	0.3374	0.7524	0.3703	0.1432	0.4393
2014	1.4910	3.7692	0.6315	0.3239	0.3866	0.2045	0.3739	0.3705	0.8698	0.4101	0.1912	0.6104
2015	0.6045	1.5417	1.8991	0.8785	1.0113	0.5560	0.9925	0.9701	2.4494	1.1085	0.4430	1.4728
2016	0.6041	1.5403	2.0463	0.8901	1.0053	0.5537	0.9628	0.9687	2.5434	1.1102	0.4312	1.4962
2017	0.5700	1.5390	2.0875	0.9007	0.9951	0.5515	1.1304	0.9673	2.5893	1.1118	0.4291	1.5178
2018	0.5510	1.5378	2.4246	0.9105	1.0375	0.5496	1.1778	0.9661	2.6182	1.1133	0.4280	1.5377
2019	0.5208	1.5367	2.3909	0.9196	1.1122	0.5477	1.3448	0.9650	2.6762	1.1147	0.4126	1.5562
2020	0.5086	1.5357	2.3552	0.9281	1.1374	0.5460	1.4050	0.9640	2.7574	1.1159	0.4818	1.5734
2021	0.5216	1.5347	2.2590	0.9360	1.1091	0.5444	1.3124	0.9630	2.7019	1.1171	0.5123	1.5894
2022	0.5222	1.5338	2.1306	0.9434	1.1065	0.5429	1.2933	0.9621	2.7611	1.1182	0.5272	1.6044

. Over the 17-year period, the ECC values of Tongchuan, Baoji, Xianyang, Weinan, and Yangling Demonstration Zone presented an upward trend, and the ECC values of cities other than Yangling Demonstration Zone exceeded 1. In contrast, Xi’an witnessed a fluctuating downward trend in its ECC, with the value dropping to less than 1 which reflects that the city’s economic contribution rate exceeds its EF proportion, suggesting a higher economic efficiency characteristic. The ESC values of Tongchuan, Baoji, Xianyang, Weinan, and Yangling Demonstration Zone also presented an upward trend, and the ECC values of cities other than Weinan and Yangling Demonstration Zone exceeded 1. However, Xi’an witnessed a fluctuating downward trend in its ECC. This suggests that while Xi’an may have imposed negative external effects on the GPUA’s ecological capacity, it has achieved a relatively high economic contribution. Evidently, Xi’an is capable of achieving a balance between resource allocation and economic development, thereby highlighting its sustainable development attributes within the GPUA.

Viewed statically, from the two dimensions of ECC and ESC, the six cities can be divided into four categories, as shown in Figure 8. In 2005, Category I only included Xi’an, which had low economic contribution and low ecological contribution (ECC > 1, ESC > 1). Category II included Weinan, which had low economic contribution and high ecological contribution (ECC > 1, ESC < 1). Category III included four cities, i.e., Tongchuan, Baoji, Xianyang, and Yangling Demonstration Zone, which were characterized by “dual-high” features: high economic contribution and high ecological contribution (ECC < 1, ESC < 1). There were no cities in Category IV.

Viewed dynamically from 2005 to 2022, cities experienced categorical shifts. Weinan shifted from Category II to Category I. Tongchuan, Baoji, and Xianyang shifted from Category III to Category II. Xi’an and Yangling Demonstration Zone shifted to Category IV from Category I and III, respectively. More specifically:

(1)

Xi’an shifted from low economic contribution and low ecological contribution to high economic contribution and low ecological contribution, reflecting its status as the National central city and core city of the GPUA. The innovation-driven economic development model of Xi’an contributes to a relatively high economic growth efficiency, and its economic contribution is significantly higher than that of the other five cities. According to the EKC, in the future, with its development, the structural effect and technological effect will drive industrial transformation, which will shift from factor-driven heavy industry to innovation-driven technology-intensive industries and high-end service industries. This will gradually improve the environmental quality and enhance its ecological contribution.

(2)

Tongchuan, Baoji, and Xianyang shifted from high economic contribution and high ecological contribution to low economic contribution and high ecological contribution, indicating delayed industrial transformation that reduced economic efficiency. Consequently, these cities have become suppliers of natural capital within the agglomeration, generating positive externalities for neighboring areas.

(3)

Weinan transitioned from low economic contribution and high ecological contribution to ”dual-low”: low economic and low ecological contribution, demonstrating lagging performance in both economic and ecological dimensions. This dual decline implies weaker contributions relative to resource consumption, sacrificing the agglomeration’s well-being and ecological fairness, and while positioning the city as a demander of both natural and human-made capital, creating negative externalities.

(4)

Yangling Demonstration Zone shifted from high economic and high ecological contribution to high economic and low ecological contribution, revealing its failure to fulfill environmental responsibilities (e.g., energy conservation) compared to its peers, and thereby undermining regional ecological fairness. According to the EKC, urban development need not sacrifice neighboring well-being; in the future, the development of the city, surpassing the inflection point of the EKC, will gradually enhance its ecological contribution.

From a practical perspective, since the Chinese Government halted illegal villa construction in the northern foothills of the Qinling Mountains (Xi’an section) in 2018, GPUA has established the Qinling Nature Reserve, launched the “Green Shield” initiative, restored its largest Heyang Wetland and urban surrounding wetlands, and achieved progress in ecological restoration through urban afforestation and green space development. For instance, urban green coverage reached 69,561 hm², accounting for 84.54% of Shaanxi Province’s total, aligning with the principles of SDG 15 (Life on Land). Regarding SDG 13 (Climate Action), GPUA’s “high-carbon” energy structure conflicts with “low-carbon” development goals. Urgent measures are required to optimize productivity distribution and develop green industries to enhance carrying capacity, ensuring public access to “affordable, reliable, and sustainable energy”. Regarding SDG 6 (Clean Water and Sanitation), GPUA achieved full coverage of safe drinking water services by 2018. Water resource utilization efficiency and wastewater treatment rates increased from 64.54% and 52.98% in 2007 to 73.26% and 95.89% in 2017, with 100% compliance in centralized urban drinking water sources. However, per capita water availability remains at 304.59 m³/person, indicating persistent water scarcity. In alignment with SDG 3 (Good Health and Well-being), GPUA’s ongoing implementation of a universal medical insurance system is making equitable healthcare services accessible to all residents.

4. Key Findings, Research Limitations, and Future Directions

4.1. Key Findings

Ensuring fair resource allocation within urban agglomerations is crucial for achieving the 17 SDGs, as it directly addresses the challenges posed by intensified intercity competition and unfair ecological occupancy. More importantly, it will help reduce cross-regional environmental impacts while safeguarding the rights and interests of urban agglomeration stakeholders, thereby promoting ecologically fair actions among cities. This study, based on the ecological–economic framework, innovatively constructs a three-dimensional analytical framework, integrates the EF, EC, and GDP, and uses the Gini coefficient and spatiotemporal analysis indicators to evaluate the 2005–2022 fairness of ecological resource allocation of the GPUA, as a core hub of the Belt and Road, which has not been fully focused in previous research. The key findings are as follows:

(1) EF and GDP grew continuously at annual rates of 11.43% and 11.87%, while EC stabilized, pushing the GPUA toward its ecological threshold under the Environmental Kuznets Curve (EKC). Moreover, the increasing Ecological Pressure Index (EPI) shows that after 2014, the GPUA has trended toward “extremely unsafe” status.

(2) The ecological carrying capacity Gini coefficient (G₁, 0.1710–0.6060) fluctuated significantly, while the economic contribution Gini coefficient (G₂, 0.1039–0.3519) showed a narrow upward trend; since 2015, the comprehensive Gini (G < 0.4) indicates that the EF aligns with its EC and economic contribution.

(3) The GPUA shows fair resource allocation. Tongchuan, Baoji, and Xianyang are low economic contribution and high ecological contribution; Xi’an and Yangling Demonstration Zone are high economic contribution and low ecological contribution; Weinan is low ecological contribution and low economic contribution.

4.2. Research Limitations

Regrettably, three data limitations persist: (1) Biophysical data limitations: Dependence on planning documents—constrained by issues of authoritativeness and accessibility—fails to accurately capture actual land use conditions, thereby reducing the precision of EC calculations, failing to fully reflect the true ecological impacts generated by consumption; (2) Scale mismatches: City/district-level biocapacity accounts suffer from inconsistent calibration, and it is also difficult to unify the data scopes of the biological resource accounts and energy consumption accounts; (3) Temporal constraints: Limited time-series restrict threshold estimation for Gini coefficient based on EC and economic contribution.

To address the three data limitations, we propose three workable measures for the future: (1) Introduce remote sensing data for land change monitoring. Integrate deep-learning algorithms to identify land-use changes, and verify the accuracy through randomly sampled field investigations, such as drone aerial surveys; (2) Establish a data-sharing mechanism with energy trading platforms. Use AI agents to automatically clean the real-time data, thereby achieving accurate tracking of energy data; (3) Apply deep-learning algorithms like Prophet and LSTM for predicting missing data (e.g., Zhang et al. [41]).

4.3. Future Directions

Within the 17 SDGs, exploring ecological resource allocation fairness and its impacts is one of the key future research directions, especially in the development of urban agglomerations. Based on our study, in-depth investigations can be pursued in the following three areas:

(1) Ecological Spillover Effects Analysis: From the perspective of urban agglomeration fairness, analyze the ecological spillover effects of individual cities and their ecological compensation. For example, Li et al. use ecological spillover and energy analysis to determine the amount of eco-compensation that the city of Xuchang should pay to the upstream city of Xinzheng, and they find that the eco-compensation increased from ¥990 million in 2010 to ¥509 billion in 2014 [42].

(2) Policy Effectiveness Evaluation: Analyze the effectiveness of ecological governance policies under different scenarios outlined in the 17 SDGs, and investigate the heterogeneous policy effects across cities with varying economic, social, and ecological contexts. For example, Dong et al. constructed a comprehensive evaluation index system for grain system resilience, based on its core components of resistance, recovery, and transformation. They find that both the direct and spatial spillover effects of digital infrastructure on grain system resilience are significantly positive, but considerable regional heterogeneity is observed [43].

(3) Stakeholder Behavior Research: Explore the attitudes of ecological stakeholders—including governments, enterprises, and the public—towards ecological allocation fairness. Investigate issues such as the satisfaction level of policies and the welfare gains/losses associated. For example, Rodrigues et al. use a mixed-methods approach to assess 217 stakeholders’ (including representatives of public institutions, private entities, associations, and consumer groups) perceptions of the NIMCP. Findings indicate that stakeholders perceive satisfactory NIMCP-objective compliance, especially in animal health and risk control. Also, all stakeholder groups prioritize risk control and consumer protection over plant and animal health [44].