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Spatiotemporal differentiation and coupled driving mechanisms of urban ecological resilience in transboundary composite economic belts remain an understudied niche within human–land coupling system research. Taking 29 prefecture-level and county-level units of the Huaihe River Eco-Economic Belt from 2014 to 2023 as research samples, this study constructs an ecological resilience evaluation framework tailored to the pollution disturbance characteristics of the Huaihe River Basin under a three-dimensional theoretical framework encompassing resistance, adaptability, and recoverability. The entropy weight method is adopted to calculate comprehensive ecological resilience values, while the geographically and temporally weighted regression (GTWR) model is applied to identify spatiotemporal heterogeneous correlations among multiple influencing factors. This paper further characterizes the spatiotemporal evolutionary patterns of urban ecological resilience across the study area and unpacks the coupled associative effects of natural, economic, and social driving factors. The empirical results reveal three key findings: (1) Temporally, the overall comprehensive ecological resilience of the study region rose from 0.318 to 0.416, with a total growth rate of 30.91%. Its evolutionary trajectory follows three successive phases: rapid growth, steady improvement, and slow saturation. Adaptability, which is predominantly boosted by anthropogenic environmental governance, constitutes the primary contributor to resilience growth. The range of urban resilience values narrowed by 9.97%, indicating continuous advancement in balanced regional development. (2) Spatially, ecological resilience presents a prominent core-periphery pattern, with high-resilience zones concentrated in mountainous southwestern areas and low-resilience zones distributed across northeastern plains. All low-resilience county-level units were eliminated by 2023. (3) In terms of driving associations, topographic relief and environmental governance investment maintain persistent positive correlations with ecological resilience, while per capita GDP acts as the core economic supportive factor. The proportion of secondary industry and population density exhibit significant negative correlations with resilience. The normalized difference vegetation index (NDVI) shifts from a negative correlation to a weak positive correlation alongside progressive ecological restoration, whereas river network variables exert negligible long-term associative impacts. Collectively, the spatiotemporally heterogeneous coupling of natural endowments, industrial-economic conditions, and social governance factors shapes the evolutionary patterns of regional ecological resilience. This study fills the research gap regarding long-timescale resilience driving mechanisms for transprovincial composite river basins covering five provinces. It identifies novel human–land coupling mechanisms, including the temporal reversal of vegetation’s ecological benefits and the dual stress imposed by industrial agglomeration and dense human settlements in plain regions. The quantitative outputs of this research can provide data-based references for differentiated coordinated ecological governance across the Huaihe Ecological Economic Belt. Full article

Artificial intelligence (AI) may influence economic performance through productivity, innovation, and the diffusion of advanced technologies, but direct country-level measures of AI adoption remain limited for small economies. This study examines the association between AI-related technological capability and economic growth in Cyprus. The descriptive analysis covers 1993–2024, while the econometric analysis uses the common annual sample for 2008–2024 after introducing one-period lags. High-technology exports as a percentage of manufactured exports are treated as an indirect indicator of technological sophistication and absorptive capacity, rather than as a direct measure of AI adoption. A sequential distributed lag ordinary least squares framework introduces current and lagged high-technology exports, merchandise trade, and inflation, with heteroskedasticity-consistent HC3 standard errors. A COVID-19 indicator for 2020–2021 and an observation-exclusion sensitivity analysis are used to assess the influence of exceptional pandemic-era movements. The preferred specification explains a substantial share of annual GDP growth variation, but the small sample requires cautious interpretation. The estimates indicate a positive contemporaneous and a negative lagged association for high-technology exports, a stronger lagged than contemporaneous trade association, and opposite-signed current and lagged inflation coefficients. The COVID-19 indicator is statistically insignificant and does not materially alter the principal coefficient pattern. Complementary DESI, Eurostat enterprise AI use, and ICT employment indicators show that Cyprus has broadly adequate digital inputs while enterprise AI adoption has not yet reached the European Union average, indicating considerable scope for further diffusion. The findings are exploratory statistical associations and do not identify causal effects of AI. The study contributes a country-specific macroeconomic assessment of technological capability, digital readiness, and growth in a small, highly open economy. Full article

In the context of China’s carbon peaking and carbon neutrality goals, clarifying whether the Yellow River Basin can achieve a decoupling of economic growth from carbon emissions and sustain regional development through deep decarbonization is critical to both ecological protection and high-quality development of the region. This study integrates the Tapio decoupling model, the geographically and temporally weighted regression (GTWR) model, and an author-developed LEAP-YRB v5 macro-sectoral hybrid model to examine 95 prefecture-level cities from 2010 to 2022 and to project energy consumption and carbon emissions for the nine YRB provincial-level regions from 2022 to 2060. The results show that: (1) the urban decoupling status fluctuated among expansive coupling, strong decoupling, and weak decoupling, with weak decoupling becoming dominant and increasing to 62 cities in 2022; (2) per capita GDP and urbanization tended to increase the decoupling index and therefore inhibited decoupling, whereas more intensive construction-land use promoted decoupling, and industrial structure upgrading and green patents showed context-dependent effects; and (3) the basin cannot peak its emissions under the business-as-usual scenario, while the policy-driven scenario peaks at approximately 3.357 billion tons of CO₂ around 2030. Under the carbon-neutrality-oriented scenario, net emissions decline substantially to 825 million tons by 2060, indicating deep decarbonization but not full carbon neutrality. Full neutrality would require additional carbon sinks, cross-regional clean-electricity integration, stronger power-sector decarbonization, or negative-emission technologies beyond the endogenous measures represented in the model. Full article

In the context of global climate issues and the transition towards sustainable development, this study examines the relationship between environmental cost, public expenditure, energy structure, and institutional quality in Saudi Arabia over the period 1994–2023, where environmental cost is proxied by the carbon intensity of GDP. To capture both long-run relationships and short-run dynamics, the study applies the Autoregressive Distributed Lag (ARDL)-Error Correction Model (ECM) framework. The results confirm the existence of a stable long-run equilibrium relationship among the variables. Public expenditure and energy use significantly increase carbon intensity, indicating that fiscal expansion and energy intensity remain important sources of environmental pressure. In contrast, renewable electricity generation and improvements in institutional quality significantly reduce carbon intensity, highlighting the importance of clean energy deployment and effective governance. Economic growth exhibits a small but statistically significant negative effect on carbon intensity, suggesting gradual progress toward relative decoupling between economic activity and environmental cost. The error-correction coefficient indicates rapid adjustment toward the long-run equilibrium following short-run shocks. These findings provide policy-relevant evidence for aligning fiscal policy, energy transition strategies, and institutional reforms with Saudi Arabia’s Vision 2030 and broader sustainability objectives in resource-dependent economies. Full article

This paper examines the concept of green growth within the G7 countries, particularly the connections between energy transition, green innovation, financial inclusion, and the circular economy. The analysis of the data using the CS-ARDL model reveals strong long-term and short-term relationships between these variables and green growth. It is demonstrated that energy transition, measured by the Energy Transition Index (ETI), positively affects green growth, as the coefficient of energy transition in the long run is positive, 0.45, which explains the need to adopt renewable energy sources. Green innovation (based on environmental patents) has a positive contribution as well (coefficient = 0.28), which highlights its contribution towards sustainable economic development. The significance of financial inclusion comes out, and its coefficient is positive and significant, 0.40, in the long run, indicating the importance of financial access in facilitating green investments. Conversely, the negative correlation that was found between carbon intensity and green growth indicates that going low on emissions per unit of GDP is an essential component of a sustainable process (coefficient = −0.20). Green growth is augmented by the interaction of the practice of the circular economy and financial inclusion (long-run 0.12). These results highlight the importance of combined policies that can facilitate energy transformation, innovation, financial inclusion, and the strategy of the circular economy to attain sustainable growth in G7 countries. Full article

This paper examines the asymmetric association between economic diversification and gross domestic product (GDP) growth volatility in the Gulf Cooperation Council (GCC) countries during the period 2000–2022. GDP growth volatility is measured using the rolling five-year standard deviation of real GDP growth. Economic diversification is measured using a Composite Economic Diversification Index (CEDIX), which is constructed through principal component analysis (PCA) and comprises export, fiscal revenue, and sectoral diversification. The index is rescaled to the unit interval and is decomposed into cumulative positive and negative partial sums in order to distinguish between diversification gains and diversification deteriorations. The empirical methodology includes cross-sectional dependence, panel unit-root and cointegration tests, and then the estimation of a pooled mean group nonlinear autoregressive distributed lag (PMG-NARDL) model. The results indicate a long-run relationship between growth volatility and its determinants, with significant long-run asymmetry between diversification gains and diversification deteriorations. Diversification gains are linked to lower volatility of GDP growth, whereas diversification deteriorations are linked to higher volatility. This suggests that deteriorations in diversification may be more strongly associated with macroeconomic instability than diversification gains are associated with stabilization. Short-run diversification effects are statistically insignificant, and the Wald test does not support short-run asymmetry. These results are consistent with the notion that diversification is more strongly associated with long-run resilience than with short-term stabilization. Full article

The rapid expansion of information and communication technology (ICT) alongside sustained economic growth poses a critical policy challenge for hydrocarbon-based economies seeking to reconcile digital development with environmental sustainability. This study examines both the short- and long-term relationships between per capita CO₂ emissions, GDP per capita, and the adoption of ICT in Saudi Arabia, using annual time-series data for 1995–2024. Methodologically, we employ the ARDL bounds test, an error correction model (ECM), and Granger causality analysis to test the EKC hypothesis. Additionally, we use the Zivot–Andrews test to detect structural breaks, accounting for these through dummy variables. Long-run cointegration among the three variables is validated by the bounds test results. GDP is the dominant long-run driver of internet-based ICT adoption, while CO₂ emissions and GDP are strongly cointegrated through the 2010 structural break and the EKC mechanism. No direct causal link is found between ICT and CO₂ emissions in either direction. The long-run validity of the EKC hypothesis is confirmed, with an estimated income turning point of approximately USD 26,021 per capita, a level reached during the latter part of the sample period. These findings suggest that Saudi Arabia’s internet adoption has yet to generate an independent decarbonization dividend, underscoring the need for policies that couple ICT investment with clean energy deployment under Vision 2030. Full article

Hainan Tropical Rainforest National Park is a prime example of a “continental island” tropical rainforest and holds significant value for biodiversity conservation. However, human activities have led to frequent changes in land use and increased habitat fragmentation within the park; a precise analysis of the underlying mechanisms is necessary for ecological restoration. Consequently, drawing upon land-use data from 2000 to 2020, this study coupled multi-dimensional fragmentation metrics (CFI, AFI, and SFI) with the XGBoost-SHAP framework to systematically unravel the spatiotemporal dynamics and underlying driving mechanisms of landscape fragmentation in Hainan Tropical Rainforest National Park. Our findings revealed that the spatial configuration of fragmentation predominantly propagated along river networks and transport corridors, accompanied by a fluctuating ‘decline–rise–decline’ temporal trajectory. Notably, the XGBoost-SHAP attribution highlighted a distinct temporal shift in the dominant drivers: fragmentation was primarily mitigated (negatively driven) by NDVI between 2000 and 2010 but was subsequently exacerbated (positively driven) by GDP growth from 2010 to 2020. The findings of this study provide data support and a scientific basis for ecosystem restoration and land use planning in Hainan Tropical Rainforest National Park. Full article

Environmental protection has become a key priority for the Chinese government. This study examined the symmetric complementarity of spatial layouts between inter-city high-speed rail and urban subway systems. Their co-location formed a structurally symmetric low-carbon travel chain that bridged long-distance and short-distance trips. The study evaluated their joint effect on urban carbon productivity within a difference-in-differences framework. The baseline two-way fixed-effects DID estimate showed that the simultaneous operation of HSR and subway systems significantly improved urban carbon productivity, with an average increase of approximately 10.51% in treated cities relative to non-treated ones. This core finding remained qualitatively robust across a series of tests, including conditional coarsened exact matching (CEM), placebo simulations, sample exclusions, and decomposition of joint effects relative to individual rail impacts. Complementary nonparametric causal forest estimates yielded smaller but still significant effect sizes, providing supportive evidence for causal validity under weaker functional form assumptions. Heterogeneity analysis based on dynamic GDP grouping revealed that high-GDP cities experienced stronger emission reductions, while low-GDP cities benefited more from the economic growth dimension of carbon productivity. Empirical patterns aligned with the theoretical prediction of the transportation substitution mechanism. The positive effect of dual rail availability on carbon productivity was stronger in cities with higher private car stock, as shown by double machine learning interaction models. This finding only provided indirect evidence and did not constitute direct proof of actual modal shift behavior. The results exhibited magnitude asymmetry between parametric and nonparametric methods, yet all estimates consistently pointed to a positive qualitative direction. This consistency revealed directional symmetry in the causal conclusion. The study contributed by (1) examining the combined carbon productivity effect of co-located HSR and subway systems, (2) identifying the moderating role of private car stock in the transportation substitution mechanism, (3) revealing development-stage-based heterogeneity, (4) and combining machine learning methods with parametric causal inference as complementary robustness evidence. Policy implications suggested that high-GDP cities should prioritize expanding dual rail coverage and optimizing connections to amplify emission reductions, while low-GDP cities should focus on improving HSR connectivity with existing public transit systems and fostering low-carbon industrial development in line with local conditions. Full article

This article employs exploratory data-driven modeling to examine the relationships between selected macroeconomic indicators, residential property prices, and housing affordability pressures in Poland between 2020 and 2024. This turbulent period was selected for analysis because of the unprecedented volatility triggered by the COVID-19 pandemic and the geopolitical shocks associated with the war in Ukraine, both of which severely disrupted macroeconomic stability and construction supply chains. The study examines how key economic variables—including inflation, gross domestic product (GDP), unemployment, and average and minimum wage dynamics—are associated with residential property price dynamics within the framework of construction economics. Using statistical modeling techniques, including linear regression and Pearson correlation analysis, the study quantifies the strength, direction, and dynamics of these relationships across primary and secondary housing sectors. Our findings reveal a distinct comparative pattern of associations: average wage growth and inflation emerge as the macroeconomic indicators most strongly associated with property valuations, while macroeconomic growth and unemployment dynamics exhibit asymmetric associations across market segments. Notably, the findings suggest that the primary sector may be more sensitive to credit-related demand shocks and policy interventions, whereas the secondary sector appears to respond more directly to broader consumer trends and household purchasing capacity. By integrating macroeconomic data into a sectoral analysis, this study provides an exploratory empirical basis for discussing sustainable housing strategies. The results underscore the necessity of aligning investment and production cycles in the construction sector with macroeconomic stability to maintain long-term residential purchasing capacity and support resilient urban development. Full article

This study examines the short-run effect of carbon taxation on the growth rate of GDP per capita, the annual first difference in log GDP per capita, using a panel of 16 carbon-pricing economies spanning Europe, the Americas, Asia and Africa over 2020–2024. Country fixed-effects estimation with country-clustered robust standard errors follows formal model selection (F-test, Hausman test), checked for cross-sectional dependence. Three baseline specifications are estimated, Model 3 excluding the COVID-19 dummy as a robustness check; a fourth adds carbon tax interaction terms with inflation, investment, energy intensity and political stability to test whether these factors condition the relationship. A higher carbon tax rate has a small but statistically significant negative effect on growth across all three baseline models (a USD 10 increase implies roughly a 1.2-percentage-point reduction in annual growth, preferred specification); none of the interaction terms is significant, indicating no detectable conditioning effect. Investment shows a robust positive association with growth; inflation, a robust negative one. Energy intensity and the COVID-19 dummy enter with signs contrary to expectations once year fixed effects are excluded, and the carbon tax coefficient loses significance under a lagged specification, cautioning against a strictly causal reading. Findings support pairing carbon tax design with investment and price-stability policies. Full article

Decoupling economic growth from carbon emissions is central to the sustainable development of rapidly urbanizing economies, and urban agglomerations are the pivotal spatial units for delivering this transition under China’s dual-carbon goals, yet systematic cross-agglomeration comparisons that could inform differentiated sustainability policy remain scarce. Using panel data for 107 prefecture-level cities in five agglomerations—the Yangtze River Delta (YRD), Beijing–Tianjin–Hebei (BTH), Pearl River Delta (PRD), Chengdu–Chongqing (CY), and the middle reaches of the Yangtze River (MRYR)—across five benchmark years spanning 2005–2023, we combined a two-way fixed-effects environmental Kuznets curve (EKC) model, the Tapio decoupling model, and cross-sectional quadrant analysis to examine the growth–emission relationship in shape, decoupling dynamics, and spatial structure. All five agglomerations traced an inverted-U trajectory, with turning-point per capita gross domestic product (GDP) rising in the order CY < PRD < BTH < MRYR < YRD. Once fixed effects and structural controls were added, most quadratic terms became insignificant and reversed sign after the secondary-industry share and carbon intensity entered; only the PRD and BTH retained a significant nonlinear form. The net income effect is therefore largely monotonic, with the inverted U carried by industrial upgrading and energy-efficiency gains. Tapio decoupling followed a non-monotonic “improve-then-regress” path, with expansive negative decoupling re-emerging across all agglomerations during 2020–2023. Spatially, high-value clustering persisted in the YRD, weakened in the BTH after 2020, and concentrated on single cores in Chengdu and Wuhan. We accordingly propose sustainability-oriented low-carbon pathways differentiated jointly by agglomeration and quadrant. By showing that decoupling is stage-dependent and reversible rather than an automatic by-product of income growth, our findings indicate that durable progress toward regional sustainability hinges on structural transformation and coordinated governance tailored to each agglomeration’s stage of development. Full article

The increasingly rapid process of de-dollarization in the context of growing geopolitical fragmentation has significantly altered the monetary dynamics of the global economy, thereby posing important questions about economic resilience and sustainable development in the emerging world. This research investigates de-dollarization-related macro-financial conditions and their association with economic resilience in the BRICS+ countries during the time span of 2000–2024, considering macroeconomic, financial, and environmental aspects. In the empirical analysis, economic resilience is operationalized through annual GDP growth, which is used as an indicator of the macroeconomic performance dimension of resilience. Due to the absence of a consistent direct de-dollarization index for all BRICS+ economies over 2000–2024, de-dollarization is captured through exchange-rate dynamics and related external monetary-financial indicators, including the official exchange rate, current account balance, total reserves, and trade openness. By employing annual panel data and the Cross-Sectionally Augmented Autoregressive Distributed Lag (CS-ARDL) method to address cross-sectional dependence and heterogeneity, the study identifies the structural changes linked to major global shocks and currency shifts. The results show that de-dollarization-related macro-financial conditions have differential long-run associations with resilience, depending on the macroeconomic stability and financial development of the countries. The findings indicate that exchange-rate dynamics and external financial conditions play an important role in shaping the macroeconomic resilience of BRICS+ economies. Environmental outcomes are found to be important determinants of long-run resilience, suggesting that sustainability-driven structural changes improve resilience during the process of global monetary shifts. The results indicate that resilience during global realignment goes beyond financial diversification and is linked to the sustainability frameworks. Full article

Emerging economies face crucial challenges around fiscal stability, particularly servicing foreign debt and securing long-term financing arrangements. In this context, we investigated the relationship between the share of banking facilities allocated to various economic sectors, the growth of public revenues and the proportion of long-term loans relative to total foreign debt in Jordan. Using data from 2008: Q1 to 2022: Q4 and employing two regression models along with the Vector Error Correction model, key findings reveal that the share of banking facilities allocated to total financing positively impacts public income and reduces long-term liabilities (LRL). Additionally, the positive effect of direct credit from financial institutions on real GDP and public income is associated with a negative impact on LRL. Conversely, direct credit from financial corporations negatively influences real GDP and public income while positively affecting LRL. Direct credit from public sector financing exhibits an inverse relationship with economic growth and public income, leading to a positive association with LRL. The statistically significant error correction coefficients indicate that short-run deviations are corrected toward the long-run equilibrium, with the first model showing a faster but oscillatory adjustment process and the second model exhibiting a slower and more gradual return to equilibrium. These findings suggest that developing countries like Jordan must prioritize banking facilitation for sectors such as industry, tourism, and agriculture to facilitate debt repayment. Full article

Human settlement quality is a key dimension of sustainable urban development, yet its spatiotemporal evolution and associated mechanisms remain insufficiently understood, particularly under rapid urbanization and regional inequality. This study aims to evaluate the Human Settlement Quality Index (HSQI) across 31 Chinese provinces from 2012 to 2021 and to examine its nonlinear predictive patterns, average conditional associations, and region-specific pathways. A composite HSQI was constructed using an entropy-weighted multi-criteria decision-making framework based on 25 indicators covering natural, human, social, residential, and supporting systems. XGBoost-SHAP was used to identify global feature importance and nonlinear predictive patterns, while a two-way fixed effects panel model and regional group regressions were employed to estimate average conditional associations and regional heterogeneity. The results show that China’s HSQI increased by 12.22% from 2012 to 2021, with an initial decline followed by sustained improvement and narrowing regional disparities. Per capita GDP was the most important predictive factor, while human and supporting systems jointly accounted for more than 60% of the total feature importance. Several core indicators exhibited nonlinear threshold-like response patterns, and regional association patterns differed substantially. Eastern China showed signs of a weaker association between economic growth and HSQI improvement, central China showed stronger associations with digital logistics and infrastructure, and western China remained closely linked to ecological foundation protection. These findings demonstrate the complementarity of interpretable machine learning and panel econometrics and provide evidence for differentiated, sustainability-oriented strategies to improve human settlement quality. Full article