Search Results (62)

Hyperloop ultra-high-speed transport presents a transformative opportunity for future mobility systems in smart cities. However, assessing its socio-economic impact remains challenging due to Hyperloop’s unique technological, modal, and operational characteristics. As a novel, fifth mode of transportation—distinct from both aviation and rail—Hyperloop requires tailored evaluation tools for policymakers. This study proposes a custom-designed framework to quantify its macroeconomic effects through changes in gross domestic product (GDP) at the city level. Unlike traditional economic models, the proposed approach is specifically adapted to Hyperloop’s multimodality, infrastructure, speed profile, and digital-green footprint. A Poisson pseudo-maximum likelihood (PPML) model is developed and applied at two technology readiness levels (TRL-6 and TRL-9). Case studies of Glasgow, Berlin, and Busan are used to simulate impacts based on geo-spatial features and city-specific trade and accessibility indicators. Results indicate substantial GDP increases driven by factors such as expanded 60 min commute catchment zones, improved trade flows, and connectivity node density. For instance, under TRL-9 conditions, GDP uplift reaches over 260% in certain scenarios. The framework offers a scalable, reproducible tool for policymakers and urban planners to evaluate the economic potential of Hyperloop within the context of sustainable smart city development. Full article

This study aims to evaluate the sustainability performance of EU regions through a comprehensive and data-driven Environmental, Social, Governance (ESG) framework, addressing the increasing demand for regional-level analysis in sustainable finance and policy design. Leveraging Partial Least Squares (PLS) regression and cluster analysis, we construct composite ESG indicators that adjust for economic size using GDP normalization and LOESS smoothing. Drawing on panel data from 2010 to 2023 and over 170 indicators, we model the determinants of ESG performance at both the national and regional levels across the EU-27. Time-based ESG trajectories are assessed using Compound Annual Growth Rates (CAGR), capturing resilience to shocks such as the COVID-19 pandemic and geopolitical instability. Our findings reveal clear spatial disparities in ESG performance, highlighting the structural gaps in governance, environmental quality, and social cohesion. The model captures patterns of convergence and divergence across EU regions and identifies common drivers influencing sustainability outcomes. This paper introduces an integrated framework that combines PLS regression, clustering, and time-based trend analysis to assess ESG performance at the subnational level. The originality of this study lies in its multi-layered approach, offering a replicable and scalable model for evaluating sustainability with direct implications for green finance, policy prioritization, and regional development. This study contributes to the literature by applying advanced data-driven techniques to assess ESG dynamics in complex economic systems. Full article

The Belt and Road Initiative (BRI) prioritizes infrastructure investment to enhance regional connectivity and foster sustainable economic development. Therefore, this empirical study aims to examine the impact of the BRI, specifically through Chinese foreign direct investment (CFDI) on sustainable growth in Bangladesh. The study employs the Mann–Kendall trend analysis and the generalized method of moments (GMM). For the Mann–Kendall trend analysis, sectoral FDI and output data from four major industrial sectors, obtained from Bangladesh Bank and CEIC for the period 1996–2020, are used to analyze trends in industrial development. Additionally, to assess the BRI’s role in sustainable development, this study compares green gross domestic product (GGDP) and gross domestic product (GDP) using a GMM analysis of CFDI inflows across 16 industrial sectors from 2013 to 2022, sourced from various databases. Findings reveal that CFDI significantly contributes to domestic industrial growth, particularly in the manufacturing and construction sectors. Although Bangladesh joined the BRI in 2016, a notable surge in CFDI appears from 2011–2012, partially driven by Bangladesh’s economic liberalization policies, and reflects early strategic investment consistent with China’s expanding economic diplomacy, which was later formalized under the BRI framework. The two-step system GMM results demonstrate that CFDI has a stronger impact on GGDP (0.0350) than on GDP (0.0146), with GGDP showing faster convergence (0.6027 vs. 0.1800), highlighting more robust and rapid sustainable growth outcomes. This underscores the significant Chinese investment in green sectors in Bangladesh. The study also demonstrates that the BRI supports the achievement of Sustainable Development Goals (SDGs) 7 (green energy) and 9 (sustainable infrastructure). These insights offer valuable direction for future research and policy, suggesting that Bangladesh should prioritize attracting green-oriented CFDI in sectors like energy, manufacturing, and construction, while also strengthen. Full article

In the face of increasingly complex urban challenges, a critical question arises: can urban ecosystems maintain resilience, vitality, and sustainability when confronted with external threats and pressures? Taking Kunming—a plateau-mountainous city in China—as a case study, this research constructs an urban ecosystem resilience (UER) assessment model based on the DPSIR (Driving forces, Pressures, States, Impacts, and Responses) framework. A total of 25 indicators were selected via questionnaire surveys, covering five dimensions: driving forces such as natural population growth, annual GDP growth, urbanization level, urban population density, and resident consumption price growth; pressures including per capita farmland, per capita urban construction land, land reclamation and cultivation rate, proportion of natural disaster-stricken areas, and unit GDP energy consumption; states measured by Evenness Index (EI), Shannon Diversity Index (SHDI), Aggregation Index (AI), Interspersion and Juxtaposition Index (IJI), Landscape Shape Index (LSI), and Normalized Vegetation Index (NDVI); impacts involving per capita GDP, economic density, per capita disposable income growth, per capita green space area, and per capita water resources; and responses including proportion of natural reserve areas, proportion of environmental protection investment to GDP, overall utilization of industrial solid waste, and afforestation area. Based on remote sensing and other data, indicator values were calculated for 2006, 2011, and 2016. The entire-array polygon indicator method was used to visualize indicator interactions and derive composite resilience index values, all of which remained below 0.25—indicating a persistent low-resilience state, marked by sustained economic growth, frequent natural disasters, and declining ecological self-recovery capacity. Forecasting results suggest that, under current development trajectories, Kunming’s UER will remain low over the next decade. This study is the first to integrate the DPSIR framework, entire-array polygon indicator method, and Grey System Forecasting Model into the evaluation and prediction of urban ecosystem resilience in plateau-mountainous cities. The findings highlight the ecosystem’s inherent capacities for self-organization, adaptation, learning, and innovation and reveal its nested, multi-scalar resilience structure. The DPSIR-based framework not only reflects the complex human–nature interactions in urban systems but also identifies key drivers and enables the prediction of future resilience patterns—providing valuable insights for sustainable urban development. Full article

The Henan section of the Sha Ying River Basin, as the core agricultural area of the Central Plains Urban Agglomeration (CPUA), plays a significant role in promoting regional green and sustainable development through the coordinated management of water–energy–carbon (WEC). This study takes the Henan section of the Sha Ying River Basin as a case study to analyze the spatiotemporal evolution characteristics of the region from 2010 to 2022, establish an evaluation system to assess the level of coupled coordination development, and utilize the gray correlation model to identify key influencing factors. The results show a fluctuating downward trend in WEC consumption, with low coupling coordination transitioning from high coordination to moderate imbalance. Key factors influencing coupling coordination include water consumption per 10,000 CNY of GDP, agricultural industry structure, and year-end population. Spatial heterogeneity in WEC coupling coordination factors was observed across cities. This research provides a scientific basis for understanding ecosystem dynamics in agricultural cities and supports differentiated environmental policies for sustainable regional development. Full article

Quantitative assessment of the ability of the ecosystem service (ES) and its driving forces is of great significance for achieving regional SDGs. In view of the scarcity of existing research that evaluates the sustainability of multiple ES types over a long time series at the township scale in a typical Watertown Region, this study aims to address two key scientific questions: (1) what are the spatiotemporal changes in the ecosystem service supply–demand index (ESSDI) and ecosystem service sustainability index (ESSI) of a typical Watertown Region? and (2) what are the key factors driving the changes in ESSI? To answer the above two questions, this study takes the Yangtze River Delta Integrated Demonstration Zone (YRDIDZ) as the study area, utilizing multi-source remote sensing and other spatiotemporal geographical datasets to calculate the supply–demand levels and sustainable development ability of different ES in the YRDIDZ from 2000 to 2020. The main findings were as follows: (1) From 2000 to 2020, the mean ESSDI values for habitat quality, carbon storage, crop production, water yield, and soil retention all showed a declining trend. (2) During the same period, the mean ESSI exhibited a fluctuating downward trend, decreasing from 0.31 in 2000 to 0.17 in 2020, with low-value areas expanding as built-up areas grew, while high-value areas were mainly distributed around Dianshan Lake, Yuandang, and parts of ecological land. (3) The primary driving factors within the YRDIDZ were human activity factors, including POP and GDP, with their five-period average explanatory powers being 0.44 and 0.26, whereas the explanatory power of natural factors was lower. However, the interaction of POP and soil showed higher explanatory power. The results of this study could provide actionable ways for regional sustainable governance: (1) prioritizing wetland protection and soil retention in high-population-density areas based on targeted land use quotas; (2) integrating ESSI coldspots (built-up expansion zones) into ecological redline adjustments, maintaining high green infrastructure coverage in new urban areas; and (3) establishing a population–soil co-management framework in agricultural–urban transition zones. Full article

The evaluation of water resource carrying capacity (WRCC) is crucial for guiding regional water management. This study established a WRCC evaluation index system and standards for the middle and lower Yangtze River, covering four subsystems: water resources, and social, economic, and ecological dimensions. The study improved the matter–element extension model by introducing triangular fuzzy numbers. The enhanced model was then used to assess the WRCC of seven provinces in the middle and lower Yangtze (2015–2023). Furthermore, GIS was used to examine the spatiotemporal variations and driving factors of WRCC. The main conclusions are as follows: (1) from 2015 to 2023, the evaluated level of WRCC in the Yangtze River’s middle and lower reaches remained stable and improved overall. Among them, the WRCC of Shanghai rose most significantly, from level III to level I. Zhejiang’s WRCC remained stable at level II, while Hubei and Hunan remained stable at level III, but with a trend toward improvement. Jiangsu’s WRCC fluctuated significantly. (2) The evaluation values of the subsystems in each region show a certain level of volatility. The water resource subsystem remained relatively stable in most regions, the social subsystem showed some variability, and both the economic and ecological subsystems developed well, showing positive effects in economic development and ecological protection in various regions. (3) The water resource subsystem had the greatest influence on WRCC. Per capita water resources, the urbanization rate, the greening coverage rate in built-up areas, and per capita GDP have the most significant impact on the WRCC in the Yangtze River’s middle and lower reaches. Full article

As an important ecological barrier and economic belt in China, the sustainable development of the Yellow River Basin (YRB) is of great significance to national ecological security and regional economic balance. Based on the coupled and coordinated development analysis of the water–soil–energy–carbon (W-L-E-C) system in the provinces of the Yellow River Basin from 2002 to 2022, this study systematically analyzed the interaction relationship among the various factors through WLECNI index assessment, factor identification, and driving factor exploration. Thus, it fully reveals the spatiotemporal evolution law of regional coordinated development and its internal driving mechanism. It is found that the coordinated development of the W-L-E-C system in different provinces of the Yellow River Basin presents significant spatiotemporal differentiation, and its evolution process is influenced by multiple factors. It is found that the coordination of the YRB presents a significant spatial difference, and Inner Mongolia and Shaanxi, as high coordination areas, have achieved significant improvement in coordination, through ecological restoration and clean energy replacement, arable land intensification, and industrial water-saving technology, respectively. Shandong, Henan, and Shanxi in the middle coordination zone have made some achievements in industrial greening and water-saving technology promotion, but they are still restricted by industrial carbon emissions and land resource pressure. The Ningxia and Gansu regions with low coordination are slow to improve their coordination due to water resource overload and inefficient energy utilization. Barrier factor analysis shows that the water resources utilization rate (W4), impervious area (L4), energy consumption per unit GDP (E1), and carbon emissions from energy consumption (C3) are the core factors restricting coordination. Among them, the water quality compliance rate (W5) of Shanxi and Henan is very low, and the impervious area (L4) of Shandong is a prominent problem. The interaction analysis of the driving factors showed that there were significant interactions between water resource use and ecological protection (W-E), land resource and energy use (L-E), and carbon emissions and ecosystem (C-E). Inner Mongolia, Shaanxi, and Shandong achieved coordinated improvement through “scenic energy + ecological restoration”, cultivated land protection, and industrial greening. Shanxi, Henan, and Ningxia are constrained by the “W-L-E-C” complex obstacles. In the future, the Yellow River Basin should implement the following zoning control strategy: for the areas with high coordination, it should focus on consolidating the synergistic advantages of ecological protection and energy development; water-saving technology and energy consumption reduction measures should be promoted in the middle coordination area. In the low coordination area, efforts should be made to solve the problem of resource overload, and the current situation of low resource utilization efficiency should be improved by improving the utilization rate of recycled water and applying photovoltaic sand control technology. This differentiated governance plan will effectively enhance the level of coordinated development across the basin. The research results provide a decision-making framework of “zoning regulation, system optimization and dynamic monitoring” for the sustainable development of the YRB, and provide a scientific basis for achieving high-quality development of the basin. Full article

This study assesses the impact of climate change and human activities on vegetation dynamics (kNDVI) on the Qinghai-Tibet Plateau (QTP) between 2000 and 2022, considering both lag and cumulative effects. Given the QTP’s high sensitivity to climate change and human activities, it is imperative to understand their effects on vegetation for the sustainable development of regional and national terrestrial ecosystems. Using MOD13Q1 NDVI and climate and human activity data, we applied methods such as Sen-MK, lag and cumulative effect analysis, improved residual analysis, and geographical detector analysis. The outcomes were as follows. (1) The vegetation kNDVI on the QTP showed an overall fluctuating growth trend between 2000 and 2022; improved regions were more significant than degraded regions, with improved regions primarily distributed in humid and semi-humid areas with favorable climate conditions, and degraded regions primarily in arid and semi-arid areas; this implies that climate conditions have a significant impact on vegetation changes on the QTP. (2) The analysis of lag and cumulative effects revealed that temperature and precipitation have a substantial cumulative effect on vegetation kNDVI on the QTP. The vegetation kNDVI showed a lag effect of 0 months and a cumulative effect of 1 month for temperature, and a lag effect of 0 months and a cumulative effect of 2 months for precipitation, respectively. (3) Improved residual analysis based on lag and cumulative effects revealed that human activities positively contributed 66% to the changes in vegetation kNDVI on the QTP, suggesting a notable positive impact of human activities. Geographical detector analysis indicated that, among different human activity factors affecting vegetation kNDVI changes, the explanatory power in 2005 and 2015 ranked as X3 (livestock density) > X1 (population density) > X2 (per capita GDP) > X4 (artificial afforestation density) > X5 (land use type), and in 2020, as X3 > X4 > X1 > X5 > X2. The explanatory power of afforestation density and land use type has relatively increased, indicating that recent efforts in ecological protection and restoration on the QTP, including developing artificial forest areas and afforestation programs, have considerably contributed to vegetation greening. Full article

Climate change poses a significant threat to the sustainable development of all countries. The transition to low-carbon energy sources is a crucial strategy for reducing carbon emissions and mitigating climate change. We investigate the mediating role of clean energy consumption (EC) and fossil energy supply (ES) on the promoting of carbon emission intensity per unit of GDP (CO₂/GDP) reduction by green bonds (GBs). We develop a mediating model to analyze how GB influences CO₂/GDP reduction through EC and ES, utilizing panel data from 13 prefecture-level cities in Jiangsu province spanning the years 2007 to 2021. Additionally, we assess the model’s reliability through endogeneity and robustness tests. We find that GBs contribute to reducing CO₂/GDP by facilitating the structural transition of energy supply and consumption. Furthermore, the development of GBs enhance the consumption of clean energy and plays a direct role in advancing the transition in structure of both energy supply and energy consumption. Notably, we observe heterogeneity in the effectiveness of GBs on CO₂/GDP reduction across different regions. Therefore, it is imperative for the government to actively promote the development of GBs to achieve sustainable economic growth. Furthermore, both financial and energy policies should be tailored to align with the specific energy structures of various regions. Full article

This research uses advanced statistical methods to examine climate change mitigation policies’ economic and environmental impacts. The primary objective is to assess the effectiveness of carbon pricing, renewable energy subsidies, emission trading schemes, and regulatory standards in reducing CO₂ emissions, fostering economic growth, and promoting employment. A mixed-methods approach was employed, combining regression analysis, cost–benefit analysis (CBA), and computable general equilibrium (CGE) models. Data were collected from national and global databases, and sensitivity analyses were conducted to ensure the robustness of the findings. Key findings revealed a statistically significant reduction in CO₂ emissions by 0.45% for each unit increase in carbon pricing (p < 0.01). Renewable energy subsidies were positively correlated with a 3.5% increase in employment in the green sector (p < 0.05). Emission trading schemes were projected to increase GDP by 1.2% over a decade (p < 0.05). However, chi-square tests indicated that carbon pricing disproportionately affects low-income households (p < 0.05), highlighting the need for compensatory policies. The study concluded that a balanced policy mix, tailored to national contexts, can optimise economic and environmental outcomes while addressing social equity concerns. Error margins in GDP projections remained below ±0.3%, confirming the models’ reliability. Full article

In recent years, the socio-economic development in the Tibet region of China has experienced substantial growth. However, transportation increasingly strains the region’s fragile ecological environment. Most studies overlook the accurate measurement and analysis of factors influencing traffic carbon emissions in Tibet due to data scarcity. To address this, this paper applies an improved traffic carbon emissions model, using transportation turnover data to estimate emissions in Tibet from 2008 to 2020. Simultaneously, the estimated traffic carbon emissions in Tibet served as the predicted variable, and various machine learning algorithms, including Radial Basis Function Support Vector Machine (RBF-SVM), eXtreme Gradient Boosting (XGBoost), Random Forest, and Gradient Boosting Decision Tree (GBDT) are employed to conduct an initial comparison of the constructed prediction models using three-fold cross-validation and multiple evaluation metrics. The best-performing model undergoes further optimization using Grid Search (GS) and Real-coded Genetic Algorithm (RGA). Finally, the central difference method and Local Interpretable Model-Agnostic Explanation (LIME) algorithm are used for local sensitivity and interpretability analyses on twelve core variables. The results assess each variable’s contribution to the model’s output, enabling a comprehensive analysis of their impact on Tibet’s traffic carbon emissions. The findings demonstrate a significant upward trend in Tibet’s traffic carbon emissions, with road transportation and civil aviation being the main contributors. The RBF-SVM algorithm is most suitable for predicting traffic carbon emissions in this region. After GS optimization, the model’s R² value exceeded 0.99, indicating high predictive accuracy and stability. Key factors influencing traffic carbon emissions in Tibet include civilian vehicle numbers, transportation land-use area, transportation output value, urban green coverage areas, per capita GDP, and built-up area. This paper provides a systematic framework and empirical support for measuring, predicting, and analyzing factors influencing traffic carbon emissions in Tibet. It employs innovative measurement methods, optimized machine learning models, and detailed sensitivity and interpretability analyses. The results can guide regional carbon reduction targets and promote green sustainable development. Full article

GDP is a common and essential indicator for evaluating a country’s overall economy. However, environmental issues may be overlooked in the pursuit of GDP growth for some countries. It may be beneficial to adopt more sustainable criteria for assessing economic health. In this study, green GDP (GGDP) is discussed using mathematical approaches. Multiple dataset indicators were selected for the evaluation of GGDP and its impact on climate mitigation. The k-means clustering algorithm was utilized to classify 16 countries into three distinct categories for specific analysis. The potential impact of transitioning to GGDP was investigated through changes in a quantitative parameter, the climate impact factor. Ridge regression was applied to predict the impact of switching to GGDP for the three country categories. The consequences of transitioning to GGDP on the quantified improvement of climate indicators were graphically demonstrated over time on a global scale. The entropy weight method (EWM) and TOPSIS were used to obtain the value. Countries in category 2, as divided by k-means clustering, were predicted to show a greater improvement in scores as one of the world’s largest carbon emitters, China, which belongs to category 2 countries, plays a significant role in global climate governance. A specific analysis of China was performed after obtaining the EWM-TOPSIS results. Gray relational analysis and Pearson correlation were carried out to analyze the relationships between specific indicators, followed by a prediction of CO₂ emissions based on the analyzed critical indicators. Full article

The frequency of global floods has increased, posing significant threats to economic development and human safety. Existing flood risk zoning studies in disaster prevention lack integration of the natural–economic–social chain and urban resilience factors. This study addresses this gap by constructing flood disaster risk and intensity indices using data from 31 provinces and 295 prefectural-level cities in China from 2011 to 2022. These indices incorporate natural (rainfall), economic (GDP), and social (population, built-up area) indicators to assess the flood likelihood and loss degree, providing comprehensive risk and intensity ratings. The study also examines the impact of resilience factors—environmental (green space), infrastructural (rainwater pipeline density), and natural resource (watershed areas)—on flood intensity. Findings reveal that high-risk regions are mainly in the Yangtze River Basin and southern regions, while high-intensity regions are primarily in the middle and lower Yangtze River and certain northwestern cities. Increasing rainwater pipeline density mitigates flood impacts in high-risk, high-intensity areas, while expanding green spaces and pipelines are effective in high-risk, low-intensity regions. This paper proposes a comprehensive flood hazard zoning mechanism integrating natural, economic, and social factors with urban resilience, offering insights and a scientific basis for urban flood management. Full article

Phycocyanin (PC) is a naturally occurring green pigment in Spirulina. It was extracted by ultrasonic extraction using green technology, and its structure was studied using IR- and NMR-spectroscopy. Spectral data confirmed the PC structure. This study also involves an in silico assessment of the diverse applications of green pigment PC. Utilizing QSAR, PreADME/T, SwissADME, and Pro-Tox, this study explores the safety profile, pharmacokinetics, and potential targets of PC. QSAR analysis reveals a favorable safety profile, with the parent structure and most metabolites showing no binding to DNA or proteins. PreADME/T indicates low skin permeability, excellent intestinal absorption, and medium permeability, supporting oral administration. Distribution analysis suggests moderate plasma protein binding and cautious blood–brain barrier permeability, guiding formulation strategies. Metabolism assessments highlight interactions with key cytochrome P450 enzymes, influencing drug interactions. Target prediction analysis unveils potential targets, suggesting diverse therapeutic effects, including cardiovascular benefits, anti-inflammatory activities, neuroprotection, and immune modulation. Based on the in silico analysis, PC holds promise for various applications due to its safety, bioavailability, and potential therapeutic benefits. Experimental validation is crucial to elucidate precise molecular mechanisms, ensuring safe and effective utilization in therapeutic and dietary contexts. DFT calculations, including geometry optimization, MEP analysis, HOMO-LUMO energy surface, and quantum reactivity parameters of the PC compound, were obtained using the B3LYP/6–311G(d,p) level. This integrated approach contributes to a comprehensive understanding of PC’s pharmacological profile and informs future research directions. Full article