Sustainability

18 pages, 282 KB

Open AccessArticle

Electromagnetic-Exposure Prevention and Mitigation Literacy Among Pre-Service Science Teachers: Behavioral Adaptation, Responsibility Attribution, and Governance Gaps

by Sevgül Çalış

Sustainability 2026, 18(16), 8381; https://doi.org/10.3390/su18168381 (registering DOI) - 17 Aug 2026

Abstract

The expansion of wireless infrastructure has made anthropogenic non-ionizing electromagnetic fields (EMFs)—commonly described in public discourse as “electrosmog” or electromagnetic pollution—a pervasive feature of digitalized environments. This study examines how 81 pre-service science teachers in Türkiye conceptualized EMF-related sources, perceived risks, mitigation measures, [...] Read more.

The expansion of wireless infrastructure has made anthropogenic non-ionizing electromagnetic fields (EMFs)—commonly described in public discourse as “electrosmog” or electromagnetic pollution—a pervasive feature of digitalized environments. This study examines how 81 pre-service science teachers in Türkiye conceptualized EMF-related sources, perceived risks, mitigation measures, and responsibility. A multi-source qualitative design combined inductive content analysis of nine open-ended questionnaire items with a document-informed deductive mapping based on authoritative EMF-exposure and risk-governance documents. The integrated framework distinguished diagnostic and risk-appraisal capacity, receiver–pathway–source intervention levels, governance and collective capacity, and responsibility attribution. Within a questionnaire primarily oriented toward awareness and daily-life action, responses concentrated strongly on receiver-level behavioral adaptation and attributed responsibility mainly to individuals and households. Source-level controls, monitoring, compliance, institutional responsibility, and evidence-based risk communication were not spontaneously articulated. Because these dimensions were not directly prompted, the findings do not establish a governance-literacy deficit; they define the boundaries of the mitigation repertoire elicited and generate a hypothesis for future research using explicit institutional and governance-oriented prompts. The study’s contribution lies in applying an integrated exposure-management and responsibility-attribution framework to an understudied teacher-education population and identifying curriculum needs concerning evidence appraisal, multilevel responsibility, and uncertainty-sensitive risk communication. Full article

(This article belongs to the Section Pollution Prevention, Mitigation and Sustainability)

26 pages, 1657 KB

Open AccessArticle

What Drives CSR for Sustainable Development in a Transition Economy? Evidence from Vietnam

by Chien Thang Nguyen, Thi Nga Bui, Thi Thu Huyen Tran and Thi Hoai Thu Tran

Sustainability 2026, 18(16), 8380; https://doi.org/10.3390/su18168380 (registering DOI) - 16 Aug 2026

Abstract

This study used an institutional perspective to analyze corporate social responsibility (CSR) practices in Vietnam, which is a developing country with institutional voids and competing normative systems. Data were collected using a survey of 463 managers of 463 Vietnamese companies. Then, a PLS-SEM [...] Read more.

This study used an institutional perspective to analyze corporate social responsibility (CSR) practices in Vietnam, which is a developing country with institutional voids and competing normative systems. Data were collected using a survey of 463 managers of 463 Vietnamese companies. Then, a PLS-SEM model was used to assess the roles of individual-level (IND) variables and the organizational context (ORG) in CSR, as well as the influence of the external social context (EXT). The results showed that ORG is the internal factor most strongly associated with CSR (β = 0.258; p < 0.001). IND exhibits a weak negative association with CSR (β = −0.074; p = 0.033), contrary to theoretical expectations. EXT positively moderates the IND–CSR relationship, with governance and tradition being more significant than modernity. The implications suggest that CSR in transition economies may be hybrid and that there is an institutional hierarchy. Full article

(This article belongs to the Section Sustainable Management)

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20 pages, 3624 KB

Open AccessArticle

Spatial Allocation Imbalance of Urban Road Infrastructure Level in Major Chinese Cities

by Jianjin Chen, Dingli Liu, Yanchang Wang and Yao Huang

Sustainability 2026, 18(16), 8379; https://doi.org/10.3390/su18168379 (registering DOI) - 16 Aug 2026

Abstract

The spatial allocation of urban road infrastructure directly affects urban operational efficiency, social equity, and ecological environmental quality. Taking 36 major Chinese cities as the research subjects and drawing on data from municipal statistical yearbooks, this study employs a composite index method, Theil [...] Read more.

The spatial allocation of urban road infrastructure directly affects urban operational efficiency, social equity, and ecological environmental quality. Taking 36 major Chinese cities as the research subjects and drawing on data from municipal statistical yearbooks, this study employs a composite index method, Theil index decomposition, and correlation analysis to reveal spatial differentiation patterns, imbalances, and driving factors of urban road infrastructure levels, based on both aggregate and average indicators. The results indicate that the aggregate road infrastructure level exhibits a “high in the southeast, low in the northwest” pattern along the Hu Huanyong Line, while the average road infrastructure level reveals relatively lower performance in some first-tier cities. Imbalances exist in both aggregate and average dimensions, with Theil indices of 0.231 and 0.059, respectively; intra-regional disparities contribute more to total inequality than inter-regional disparities. Urban permanent population (ridge regression coefficient: 0.1991) and fiscal revenue (ridge regression coefficient: −0.1087) are the core driving factors among the four influencing factors of aggregate road infrastructure level spatial differentiation, whereas GDP (−0.0318) and built-up area (0.0703) exert only marginal effects. This suggests that current aggregate urban road infrastructure levels are shaped by the interplay of urbanization stage, economic development level, fiscal system, and spatial planning policies, all operating within the constraints imposed by the city’s natural geographical conditions, and have not yet adequately addressed residents’ demand for spatial equity. The study recommends establishing differentiated investment mechanisms, optimizing road network density in developed cities, and constructing a spatial matching early-warning system to promote people-oriented new urbanization and coordinated regional sustainable development. Full article

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19 pages, 5264 KB

Open AccessArticle

Optimal Node Degree and Contingent Topology of Industry–University–Research Knowledge Sharing Networks: A Simulation Analysis Considering Relational Maintenance Cost

by Houxing Tang, Ziyi Kuang, Changping Chai, Songqin Zhao, Qifan Hu and Zhenzhong Ma

Sustainability 2026, 18(16), 8377; https://doi.org/10.3390/su18168377 (registering DOI) - 16 Aug 2026

Abstract

Industry–University–Research (IUR) networks are vital for knowledge sharing and collaborative innovation, yet existing network research largely ignores the maintenance cost of inter-organizational ties, which creates persistent theoretical tension between social capital theory (advocating dense connections) and structural hole theory (advocating sparse non-redundant ties). [...] Read more.

Industry–University–Research (IUR) networks are vital for knowledge sharing and collaborative innovation, yet existing network research largely ignores the maintenance cost of inter-organizational ties, which creates persistent theoretical tension between social capital theory (advocating dense connections) and structural hole theory (advocating sparse non-redundant ties). This study constructs a simulation model integrating barter knowledge exchange and multi-dimensional relational maintenance cost loss and systematically simulates the evolution of average knowledge stock (AKS) under regular, small-world and random network structure. The simulation results show that there exists a stable optimal node degree range of 20–40 for IUR actors, which is robust against changes in network scale, initial knowledge endowment and relational cost coefficients. Under moderate technological complexity, small-world networks realize the highest efficiency of knowledge accumulation; when technological complexity rises to a high level, regular networks with local agglomeration advantages become more efficient. This study supplements a cost-based analytical perspective to reconcile the contradiction between two core network theories and provides preliminary simulation evidence for the contingent design of IUR collaborative networks. From a practical perspective, the findings offer reference for adaptive governance of IUR alliances to balance relational costs and knowledge gains and further respond to the United Nations Sustainable Development Goal 9 (Industry, Innovation, and Infrastructure). Limitations of this simulation-based analysis are clearly acknowledged in the discussion section. Full article

(This article belongs to the Special Issue The Role of Knowledge Management in Designing and Achieving Business Sustainability)

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41 pages, 11016 KB

Open AccessArticle

Design of Resilient Renewable-Fed Microgrid Using ANFIS- Based MPPT Control and Adaptive Power Management with Voltage Stability Enhancement

by Mohammad Kamruzzaman Khan Prince, Md. Rimon Hossain, Md. Rashedul Islam, Saeed Ahamed Mridha, Md. Salah Uddin, Md. Feroz Ali, Md. Shafiul Alam, Shama Islam and Mohammad Taufiqul Arif

Sustainability 2026, 18(16), 8378; https://doi.org/10.3390/su18168378 (registering DOI) - 15 Aug 2026

Abstract

This paper presents the design, control, and validation of a solar photovoltaic (PV)-powered DC microgrid (MG) integrated with a battery energy storage system (BESS), which was studied at laboratory scale as a step towards remote electrification in resource-constrained regions. An Adaptive Neuro-Fuzzy Inference [...] Read more.

This paper presents the design, control, and validation of a solar photovoltaic (PV)-powered DC microgrid (MG) integrated with a battery energy storage system (BESS), which was studied at laboratory scale as a step towards remote electrification in resource-constrained regions. An Adaptive Neuro-Fuzzy Inference System (ANFIS)-based maximum power point tracking (MPPT) algorithm is implemented to maximise solar energy extraction under varying irradiance. An Adaptive Power Management (APM) framework is proposed to maintain DC bus stability when the BESS is unavailable to support the bus—a condition that may arise from battery degradation, sensor or communication failures, converter malfunctions, protection trips, or physical damage. In this work, BESS unavailability is represented at the system level as the withdrawal of BESS support; the individual fault mechanisms that may cause it are not separately modelled. The APM operates across three hierarchical layers—monitoring, decision, and control—and reuses only the voltage and current measurements already present in the MG, requiring no additional sensing. The system is evaluated under three operating scenarios: (i) intermittent renewable generation; (ii) varying load demand; (iii) stochastic fluctuations in both irradiance and load. During BESS unavailability, the APM activates prioritised adaptive load shedding or PV generation curtailment as appropriate, preserving critical loads and preventing DC bus overvoltage. In the scenarios studied, the APM reduces worst-case voltage sag from 35.9% to 2.4% and worst-case swell from 53.51% to 0.14%, while maintaining BESS State of Charge (SOC) within 20%–80% during normal operation. Compared with the conventional Perturb and Observe (P&O) and Incremental Conductance (INC) methods, the ANFIS-based MPPT achieves a mean point-wise tracking and conversion efficiency of 99.46%, a 1.78% improvement and a 0.86% improvement, respectively, which were corroborated by independent energy-based assessments (1.76% and 0.92%), with voltage deviations of 2.34% and oscillations of only 0.57 V peak-to-peak. Lyapunov-based analysis establishes asymptotic stability of the DC bus voltage in the BESS-regulated operating modes under stated assumptions. The proposed control strategies are validated through MATLAB/Simulink (R2025b) simulations and laboratory-scale experimental results, with the latter demonstrating coordinated PV–BESS–converter operation and bus voltage regulation. Full article

(This article belongs to the Special Issue Advances in Renewable and Sustainable Energy Technologies)

29 pages, 434 KB

Open AccessArticle

Supply Chain Digitalisation and Corporate ESG Performance: Structural and Resource Empowerment Mechanisms

by Hongyu Li, Jiale Zhao, Jianing Chen, Qi Meng, Yuwei Zhao and Baojian Zhang

Sustainability 2026, 18(16), 8376; https://doi.org/10.3390/su18168376 (registering DOI) - 15 Aug 2026

Abstract

Enhancing corporate ESG performance through digital technologies has become a central issue as digital transformation and green development advance in tandem. Using data on Chinese A-share listed companies from 2010 to 2024, a total of 13,917 observations, this study develops a dual-path framework [...] Read more.

Enhancing corporate ESG performance through digital technologies has become a central issue as digital transformation and green development advance in tandem. Using data on Chinese A-share listed companies from 2010 to 2024, a total of 13,917 observations, this study develops a dual-path framework of structural and resource empowerment from a supply chain network perspective, to systematically uncover the mechanisms through which supply chain digitalisation affects corporate ESG performance. The results show that supply chain digitalisation significantly improves ESG performance (β = 4.4127, p < 0.01), a finding that remains robust across a series of robustness checks. Mechanism analysis further shows that digitalisation generates structural empowerment by increasing firms’ network centrality (β = 0.4036, p < 0.01) and structural-hole positions (β = −0.1668, p < 0.05), and also generates resource empowerment by alleviating financing constraints (β = −0.8366, p < 0.01) and promoting substantive green innovation (β = 0.6263, p < 0.05). Heterogeneity analysis reveals that the positive effect is more pronounced at the planning, production, and logistics stages, in capital- and labour-intensive industries, among upstream firms in the industrial chain, under low environmental uncertainty, and under intense market competition. From a meso-level network perspective, this study extends the literature on the mechanisms underlying corporate ESG performance and provides empirical evidence for firms seeking to align digital transformation with green development. Full article

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23 pages, 9806 KB

Open AccessReview

Aegilops geniculata Roth: Biology, Ecology and Potential Applications in Crop Breeding and Nature-Based Solutions

by Micol Orengo, Filippo Guzzon, Anna Corli and Graziano Rossi

Sustainability 2026, 18(16), 8375; https://doi.org/10.3390/su18168375 (registering DOI) - 15 Aug 2026

Abstract

Aegilops geniculata Roth is a tetraploid wild relative of wheat distributed throughout the Mediterranean Basin, where it grows in a wide range of habitats. Due to its genetic diversity, ecological plasticity and adaptation to Mediterranean ruderal environments, the species has attracted increasing interest [...] Read more.

Aegilops geniculata Roth is a tetraploid wild relative of wheat distributed throughout the Mediterranean Basin, where it grows in a wide range of habitats. Due to its genetic diversity, ecological plasticity and adaptation to Mediterranean ruderal environments, the species has attracted increasing interest as a genetic resource for wheat improvement and as a potential component of sustainable agroecosystems. Beyond use in agriculture, its ecological characteristics also suggest applications in Nature-based Solutions, including revegetation of degraded, low-input, semi-arid ecosystems. This review aims to provide a comprehensive synthesis of current knowledge on the taxonomy, genetics, morphology, distribution, ecology, reproductive biology and conservation of Ae. geniculata. This work is complemented by original experimental data on agronomic traits and an assessment of ex situ conservation efforts. Evidence highlights the species’ value as a source of drought- and stress-adaptive traits for wheat improvement, plus promising cover crop traits (rapid establishment, stable biomass production and flexible germination behavior). Remaining knowledge gaps include population genomics, conservation of undercollected populations and field-scale agronomic performance to better clarify the potential of this species as a cover crop. This work supports further investigation of Ae. geniculata as both a valuable crop wild relative and a promising cover crop for Mediterranean agroecosystems. Full article

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34 pages, 2609 KB

Open AccessArticle

Biosorption of Cadmium from Aqueous Solutions Using Natural and Treated Pinus halepensis Needles: Batch and Fixed-Bed Studies

by Víctor-Francisco Meseguer, Mercedes Lloréns, María-Isabel Aguilar, Javier Sánchez-Pina, Juan-Francisco Ortuño and Ana-Belén Pérez-Marín

Sustainability 2026, 18(16), 8374; https://doi.org/10.3390/su18168374 (registering DOI) - 15 Aug 2026

Abstract

This study investigated the adsorption capabilities of three solids derived from dead Pinus halepensis needles, a renewable and low-cost natural material, for the removal of cadmium ions from wastewater: the raw material (P-H₂O) and materials chemically modified with NaOH (P-NaOH) and [...] Read more.

This study investigated the adsorption capabilities of three solids derived from dead Pinus halepensis needles, a renewable and low-cost natural material, for the removal of cadmium ions from wastewater: the raw material (P-H₂O) and materials chemically modified with NaOH (P-NaOH) and HCl (P-HCl) solutions. The Fourier transform infrared spectra of the three materials revealed the presence of active functional groups, such as hydroxyl and carbonyl groups, which may be involved in the adsorption process. The effects of pH, adsorption kinetics, adsorption isotherms, and the presence of Na⁺, K⁺, Ca²⁺, and Mg²⁺ ions were studied. Continuous adsorption tests in a fixed-bed column were carried out using the P-H₂O biosorbent. The amount of Cd(II) adsorbed increased with increasing pH and followed the order P-NaOH > P-H₂O > P-HCl. Cadmium adsorption kinetics were very rapid (less than 30 min in all cases), and the pseudo-second-order kinetic model adequately described the adsorption process. The Sips isotherm model accurately described the adsorption equilibrium and predicted maximum adsorption capacities of 30.50 mg·g⁻¹, 70.11 mg·g⁻¹, and 30.46 mg·g⁻¹ for the P-H₂O, P-NaOH, and P-HCl solids, respectively. It was also verified that the amount of cadmium adsorbed decreased substantially in the presence of Na⁺, K⁺, Ca²⁺, and Mg²⁺ ions. These results highlight that dead pine needles may be a promising, inexpensive, and effective adsorbent for the removal of Cd(II) ions from aqueous solutions. Full article

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25 pages, 8831 KB

Open AccessArticle

Spatiotemporal Distribution and Health Risk of PM_2.5 in the Urban Belt Around the Tarim Basin, China

by Liang Guo, Qian Sun, Guizhen Gao, Yueqi Zhao and Lunan Chen

Sustainability 2026, 18(16), 8373; https://doi.org/10.3390/su18168373 (registering DOI) - 15 Aug 2026

Abstract

The Taklamakan Desert, the primary source of sand and dust in China, has a significant impact on PM_2.5 concentrations in the urban belt around the Tarim Basin. In this study, moderate-resolution imaging spectroradiometer (MODIS) images and machine learning algorithms were used to [...] Read more.

The Taklamakan Desert, the primary source of sand and dust in China, has a significant impact on PM_2.5 concentrations in the urban belt around the Tarim Basin. In this study, moderate-resolution imaging spectroradiometer (MODIS) images and machine learning algorithms were used to simulate PM_2.5 concentrations and analyze deaths from ischemic heart disease (IHD), chronic obstructive pulmonary disease (COPD), lung cancer (LC), and stroke (STK) attributed to PM_2.5. From 2015 to 2024, the aerosol optical depth (AOD) showed a decreasing trend, and the spatial distribution pattern gradually increased from west to east. The counties and cities with higher average PM_2.5 concentrations during the 2024 dust-prone season were Awati County, Alar City, Moyu County, and Aksu City, with average concentrations of 89.7 μg/m³, 88.8 μg/m³, 86.0 μg/m³, and 84.1 μg/m³, respectively. PM_2.5 concentrations during the non-dust season were significantly lower (approximately 50%) than those during the dust-prone season. Among the four major fatal diseases, STK and IHD accounted for the majority of attributable deaths, with proportions exceeding 80% of the total. The results show that PM_2.5 concentrations can be accurately monitored over a large scale, addressing the shortage of fixed monitoring stations, and providing a theoretical basis for sustainable development of ecological environment, health-risk management and control in the urban belt around the Tarim Basin. Full article

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25 pages, 10834 KB

Open AccessArticle

Remote Sensing Inversion Model of Cultivated Land Salinity Based on Attention Mechanism and Lightweight CNN: Construction, Validation, and Multi-Model Comparative Analysis

by Xingchen Dong, Shiqian Guo, Zichen Guo, Xu Jiang, Senyu Mao, Ruihong Jia and Ji Wang

Sustainability 2026, 18(16), 8372; https://doi.org/10.3390/su18168372 (registering DOI) - 15 Aug 2026

Abstract

Soil salinization threatens agriculture in arid regions, and remote sensing retrieval still faces challenges of unclear mechanisms and poor generalization. Based on Sentinel-2 data, this study compares the retrieval performance of various machine learning and deep learning models for farmland soil salt content, [...] Read more.

Soil salinization threatens agriculture in arid regions, and remote sensing retrieval still faces challenges of unclear mechanisms and poor generalization. Based on Sentinel-2 data, this study compares the retrieval performance of various machine learning and deep learning models for farmland soil salt content, and introduces an attention mechanism for optimization. The main conclusions are as follows: (1) Random Forest achieved the highest accuracy among classical machine learning models (R² = 0.334), while CNN performed better among deep learning models (R² = 0.70), making it suitable for modeling scenarios with a single dominant salt type, well-defined spatial structures, and samples covering major environmental gradients. (2) In the problem of multispectral salinity retrieval, the complementarity of errors among base models was poor, preventing the ensemble model from fully leveraging its advantages. (3) Specific indices derived from near-infrared, red-edge, and blue–green bands performed well for sulfate-type salinity retrieval. (4) In the seed maize production area of Gansu, approximately 75.47% of farmland is non-saline, with severely saline land accounting for 1.42%; over the past decade, 74.84% of the area experienced a decrease in salt content, among which areas with a significant decline (accounting for 9.31%) corresponded consistently with regions where continuous engineering salt removal and microbial fertilizer management had been implemented for ten years. This demonstrates that, under conditions of limited ground samples, combining Sentinel-2 spectral information with moderate local spatial context can enhance the ability to detect salinity changes in relatively uniform irrigated areas. Full article

(This article belongs to the Special Issue Application of Remote Sensing and Machine Learning in Sustainable Agriculture)

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25 pages, 27810 KB

Open AccessArticle

Predicting Dynamic Landslide Susceptibility Under Changing Land-Use Scenarios with Generalized Additive Model

by Hong Xie, Hongwei Deng, Peng Wang and Mengfei Lei

Sustainability 2026, 18(16), 8371; https://doi.org/10.3390/su18168371 (registering DOI) - 15 Aug 2026

Abstract

Conventional landslide susceptibility assessment (LSA) generally relies on static land-use/land-cover (LULC) data, limiting its ability to capture the influence of future land-use evolution on landslide susceptibility. To address this limitation, this study proposes a dynamic LSA framework by integrating the Patch-generating Land Use [...] Read more.

Conventional landslide susceptibility assessment (LSA) generally relies on static land-use/land-cover (LULC) data, limiting its ability to capture the influence of future land-use evolution on landslide susceptibility. To address this limitation, this study proposes a dynamic LSA framework by integrating the Patch-generating Land Use Simulation (PLUS) model with a slope unit-based Generalized Additive Model (GAM). Using Wushan County in the Three Gorges Reservoir Area (TGRA), China, as a case study, historical LULC data were first analyzed, and future LULC scenarios for 2026 and 2030 were simulated using the PLUS model. Landslide susceptibility under different LULC scenarios was then evaluated using the interpretable GAM, while the statistical association between LULC categories and the spatial distribution of LSI was quantified using the GeoDetector model. The results show that the PLUS model accurately reproduced LULC evolution with a Kappa coefficient of 0.963. The GAM exhibited robust predictive performance, with 100 repeated spatial cross-validations (SCVs) yielding a mean AUC value exceeding 0.75. Although dynamic LULC had only a limited influence on the overall spatial pattern of landslide susceptibility, the proportion of very high-susceptibility areas gradually increased from 4.54% in 2022 to 4.62% in 2030. The interpretable model further revealed distinct nonlinear responses of environmental variables and demonstrated that different LULC categories contributed differently to the spatial distribution of landslide susceptibility. The proposed framework provides a practical approach for incorporating future land-use dynamics into landslide susceptibility assessment and offers valuable support for long-term landslide risk management and sustainable land-use planning. Full article

(This article belongs to the Special Issue Geological Engineering and Sustainable Environment)

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43 pages, 34833 KB

Open AccessArticle

Use of the Spatial Economic Benefit Analysis (SEBA) for Marine Spatial Planning: A Case Including Offshore Wind Energy and Fisheries in France

by Lokesh Pawar, Bertrand Le Gallic and Jorge Ramos

Sustainability 2026, 18(16), 8370; https://doi.org/10.3390/su18168370 (registering DOI) - 15 Aug 2026

Abstract

Marine Spatial Planning (MSP) is a key instrument for sustainable ocean governance in Europe, yet the spatial distribution of socioeconomic benefits and trade-offs remains poorly documented. This study applies and extends the Spatial Economic Benefit Analysis (SEBA) framework to offshore wind energy and [...] Read more.

Marine Spatial Planning (MSP) is a key instrument for sustainable ocean governance in Europe, yet the spatial distribution of socioeconomic benefits and trade-offs remains poorly documented. This study applies and extends the Spatial Economic Benefit Analysis (SEBA) framework to offshore wind energy and capture fisheries in France, with a focus on the North Atlantic–Western Channel (NAMO) façade. Three extensions are introduced relative to earlier single-sector, single-scale SEBA applications: simultaneous dual-sector comparison, nesting of project-level firm mapping within regional analysis, and delivery through an open, updatable visualization platform that allows planners to re-run the analysis as registries are updated. Twelve indicator families were selected against four explicit criteria (relevance to a SEBA step, spatial attributability, traceability to a planning instrument, and public availability) and cross-validated against independent registries. Offshore wind employment is more unevenly distributed across regions than fishery revenue (Gini 0.59 versus 0.42), and firm presence is a weak proxy for employment: Brittany hosts 26.6% of mapped offshore wind firms but only 6.5% of sectoral full-time equivalents (2.1 FTE per firm), whereas Île-de-France hosts 4.8% of firms and 19.6% of employment (34.6 FTE per firm). Fishery revenue and offshore wind firm counts are positively rank-correlated across coastal regions (Spearman ρ = 0.90, n = 5, p = 0.037), indicating co-location rather than causation: offshore wind constitutes an additional spatial pressure on a sector already contracting (−9.5% employment 2012–2022; −29% vessels 2000–2023). Uncertainty was addressed through a three-tier data confidence classification and sensitivity testing; concentration rankings were robust to extreme reassignments of multi-role firms (ΔGini ≤ 0.06). The analysis is deliberately restricted to the distribution of economic benefits and does not assess ecological impacts, which must be integrated before SEBA outputs inform zoning. Outputs are directly interpretable for façade plan revision, tender local content scoring, and compensation design, and the workflow is transferable to any jurisdiction with public corporate registries and landings statistics. Full article

(This article belongs to the Section Sustainable Oceans)

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16 pages, 599 KB

Open AccessArticle

Assessing the Effects of Root Preparation Methods and Irrigation Strategies for Urban Tree Growth and Establishment

by Teagan H. Young, Ryan W. Klein, Gail Hansen, Sandra B. Wilson, Laura Warner and Andrew K. Koeser

Sustainability 2026, 18(16), 8369; https://doi.org/10.3390/su18168369 - 14 Aug 2026

Abstract

Drought and constrained municipal budgets are increasing demand for establishment practices that conserve water and labor. This study quantified trade-offs between inputs (water, labor, cost) and tree outcomes across irrigation methods and root-ball correction practices during establishment of American sycamore (Platanus occidentalis [...] Read more.

Drought and constrained municipal budgets are increasing demand for establishment practices that conserve water and labor. This study quantified trade-offs between inputs (water, labor, cost) and tree outcomes across irrigation methods and root-ball correction practices during establishment of American sycamore (Platanus occidentalis L.). In December 2022, forty-five 45-gal trees were planted in Gainesville, Florida, in a completely randomized design combining three root treatments (control, shaved, sliced) with three irrigation methods (hand watering, hydrogel bag, conventional slow-release bag). Over 23 months, trunk caliper, height, midday stem water potential, and anchorage (bending stress at 1° inclination) were analyzed using linear mixed-effects models; survival, labor, water, and cost inputs were compared descriptively. Labor differed by two orders of magnitude, but total cost ranked nearly opposite. All trees survived; irrigation method affected no measured response: trees receiving no scheduled irrigation after an initial hydrogel charge were indistinguishable from those hand-watered 48 times (differences within 10–12%). Shaving increased the bending stress required to tilt the trunk 81% over controls, indicating firmer anchorage, without altering growth or water status; slicing had no effect. Under the dormant-season, average-rainfall conditions tested, shaving improved anchorage at negligible cost, and irrigation method had no measurable effect on tree performance. Full article

(This article belongs to the Section Sustainable Forestry)

31 pages, 28995 KB

Open AccessArticle

Optimization of Park Green-Space Site Selection in Changsha Based on Accessibility and Machine Learning

by Zhihao Luo, Weimin Zheng, Sheng Li, Zeyu Zhang and Kangkang Zhao

Sustainability 2026, 18(16), 8368; https://doi.org/10.3390/su18168368 - 14 Aug 2026

Abstract

Fine-scale site selection of urban parks serves as a core measure to guarantee spatial equity of public service spaces for residents and advance the sustainable development of urban ecological spaces. Among relevant tasks, balancing the supply and demand of green spaces stands out [...] Read more.

Fine-scale site selection of urban parks serves as a core measure to guarantee spatial equity of public service spaces for residents and advance the sustainable development of urban ecological spaces. Among relevant tasks, balancing the supply and demand of green spaces stands out as an essential foundation for maintaining long-term stability of urban human well-being and ecosystems. Green-space supply is defined as the stock of various existing urban parks within the city, while green-space demand is quantified via grids generated based on residential communities in Changsha. Existing research on urban park site selection lacks a full-process coupled framework, fails to accommodate differentiated layout demands for multi-level parks, and struggles to reconcile the sustainable operation and long-term ecological empowerment of urban green-space systems. Taking the main urban area of Changsha as the research scope, this study divides the study area into grid units to analyze the spatial differentiation of green-space accessibility and identify service blind zones. The XGBoost model is adopted to predict areas suitable for green-space construction, and the NSGA-III algorithm is applied to realize collaborative multi-objective optimization covering service efficiency, ecological benefits, and land development costs. The results reveal that the 15-min walking coverage of community parks in central Changsha only reaches 57.29%. Respectively, 34.52% and 41.04% of residential communities record accessibility levels below the municipal average of urban parks and forest parks, with prominent shortages of green-space supply in peripheral urban areas. This study optimizes and screens twenty-eight candidate sites for community parks, twelve candidate sites for urban parks, and eight candidate sites for forest parks. The proposed scheme effectively narrows the gap in green-space accessibility across the whole city and coordinates ecological conservation with land development costs. Compared with research relying on a single model or two-stage coupling frameworks, this paper constructs a systematic workflow spanning supply–demand status assessment to multi-objective layout decision-making, enabling differentiated optimized layout of multi-tiered parks. The integrated framework effectively enhances the spatial resilience and resource utilization efficiency of urban green-space systems, facilitates high-quality and sustainable upgrading of urban living environments, and provides a referable innovative approach for multi-level urban park arrangement and refined multi-objective planning. Full article

(This article belongs to the Section Health, Well-Being and Sustainability)

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19 pages, 604 KB

Open AccessArticle

The Effect of Socio-Economic and Energy-Related Factors on Environmental Degradation in South Africa: An Autoregressive Distributed Lag Model Approach

by Lehlohonolo Godfrey Mafeta, Amahle Madiba and Robert Nicky Tjano

Sustainability 2026, 18(16), 8367; https://doi.org/10.3390/su18168367 - 14 Aug 2026

Abstract

Over the past two decades, the world has experienced a significant and relentless increase in environmental degradation, measured through carbon emissions (CO₂). These emissions have been one of the persistent global concerns. South Africa boosts abundance of natural resources and some [...] Read more.

Over the past two decades, the world has experienced a significant and relentless increase in environmental degradation, measured through carbon emissions (CO₂). These emissions have been one of the persistent global concerns. South Africa boosts abundance of natural resources and some of the world’s most substantial mineral deposits, endowments in the form of precious metals, diamonds and gold. The paper aims to examine the impact of socio-economic and energy-related factors on environmental degradation from a South African perspective. Using multivariate annual data spanning from 1991 to 2022, the Autoregressive Distributed Lag Model (ARDL) was employed to determine both short-run and long-run impact of financial development (FD), renewable energy (RE), non-renewable energy (NRE), unemployment rate (UNE), economic growth (GDPPC), and population growth (PoPG) on CO₂ emission. The results show that NRE remains a dominant driver for environmental degradation, while RE is positively associated with emissions under current system conditions. FD exhibits short-run emission-intensity but long-run mitigation effects. The results suggest a need for relevant policymakers to prioritize coal displacement, stimulate economic growth and promote access to green financing, and related technologies and consumption, to enhance and promote environmental quality in South Africa. The conclusion is that South Africa’s energy-economy nexus is still at a transitional stage, where targeted policy intervention and structural reform are essential to accelerate the shift towards a low-emission economy. Future research can extend the analytical depth by exploring asymmetries, disaggregating fossil fuels, and incorporating broader environmental indicators. Full article

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40 pages, 779 KB

Open AccessArticle

Applying the UNECE PIERS Evaluation Methodology for the SDGs to Support the Implementation of Green PPP Projects and Infrastructure: Lessons from Slovenia

by Petra Ferk

Sustainability 2026, 18(16), 8366; https://doi.org/10.3390/su18168366 - 14 Aug 2026

Abstract

Robust evaluation methods are essential for assessing the sustainability of public–private partnerships (PPPs) and advancing the Sustainable Development Goals (SDGs). This study examines the United Nations Economic Commission for Europe (UNECE) People-first PPP and Infrastructure Evaluation and Rating System (PIERS) through three green [...] Read more.

Robust evaluation methods are essential for assessing the sustainability of public–private partnerships (PPPs) and advancing the Sustainable Development Goals (SDGs). This study examines the United Nations Economic Commission for Europe (UNECE) People-first PPP and Infrastructure Evaluation and Rating System (PIERS) through three green PPP initiatives in Slovenia, focusing on energy efficiency, renewable energy and sustainable mobility. Using structured scoring across five dimensions—access and equity, economic effectiveness, environmental sustainability, replicability and stakeholder engagement—the analysis considers project-level performance, national SDG alignment and methodological applicability. The results indicate strong overall sustainability performance, but identify stakeholder engagement as a significant weakness, largely due to institutional constraints. Comparison with Slovenia’s national SDG progress in 2023–2025 shows broad alignment between project outcomes and wider sustainability achievements. The methodological review also identifies scope for improving the applicability and adaptability of PIERS indicators. Although primarily designed as a self-assessment tool, PIERS provides a systematic framework for integrating SDG principles into PPP management, especially in relation to SDGs 7, 9, 11 and 13. The study contributes empirical evidence and methodological insights for more sustainable public investment governance. Full article

(This article belongs to the Special Issue State of the Art of Assessment for Sustainable Development Goals—3rd Edition)

25 pages, 5916 KB

Open AccessArticle

Can Green Bond Financing Improve Corporate Green Technology Efficiency? Evidence from Chinese Listed Firms

by Hongjun Jing and Xiuli Li

Sustainability 2026, 18(16), 8365; https://doi.org/10.3390/su18168365 - 14 Aug 2026

Abstract

Against the backdrop of the global low-carbon transition and the rapid expansion of sustainable finance, green bonds have become an important capital-market instrument for financing environmentally beneficial projects and supporting corporate green transformation. Our research is based on panel data from A-share-listed companies [...] Read more.

Against the backdrop of the global low-carbon transition and the rapid expansion of sustainable finance, green bonds have become an important capital-market instrument for financing environmentally beneficial projects and supporting corporate green transformation. Our research is based on panel data from A-share-listed companies in China between 2013 and 2023, employing a multi-time point Difference-in-Differences (DID) model and a DID model extended with the Dual Machine Learning (DML) estimation method for empirical testing. It further examines potential transmission channels and firm-level heterogeneity. The results show a robust positive association between green bond financing and corporate green technology efficiency. Green bond financing is also associated with weaker financing constraints and more green patent applications. These findings provide firm-level evidence for improving the effectiveness of green bond financing in facilitating corporate green transformation. Full article

(This article belongs to the Section Economic and Business Aspects of Sustainability)

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24 pages, 1393 KB

Open AccessArticle

Digital Capability and the Developmental Returns to Renewable Energy: Cross-Country Panel Evidence on SDG Performance

by Mohammed Saharti

Sustainability 2026, 18(16), 8364; https://doi.org/10.3390/su18168364 - 14 Aug 2026

Abstract

This study asks when renewable energy translates into sustainable-development progress. The central contribution is to show that the aggregate renewable-energy share is a compositionally ambiguous indicator whose developmental meaning depends on a country’s stage of energy modernisation. In an unbalanced panel of 154 [...] Read more.

This study asks when renewable energy translates into sustainable-development progress. The central contribution is to show that the aggregate renewable-energy share is a compositionally ambiguous indicator whose developmental meaning depends on a country’s stage of energy modernisation. In an unbalanced panel of 154 countries (2000–2022) linking the Sustainable Development Report SDG Index to World Bank indicators, two-way fixed-effects estimates with Driscoll–Kraay standard errors show the renewable share to be negatively associated with SDG performance on average. Three results demonstrate that this reflects traditional biomass rather than modern renewable energy: the renewable share correlates −0.81 with clean-cooking access; the renewable association turns from negative in biomass-dependent economies to positive in modern-energy economies; and a modern-renewable-electricity measure carries no negative coefficient, with the penalty concentrated in the social goals, where household air pollution and fuel-collection burdens fall. Digitalisation is positively associated with SDG performance and similarly conditions the renewable association, but it is best read as a correlated marker of modernisation rather than an independent causal lever. All estimates are conditional associations rather than causal effects. The findings imply that renewable-energy investment yields larger developmental returns when sequenced with clean-cooking and digital-infrastructure programmes, particularly in non-high-income countries. Full article

(This article belongs to the Special Issue Integrating Sustainable Development Goals (SDGs) into the Transformation of the World Economy)

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32 pages, 4035 KB

Open AccessArticle

A Carbon Efficiency Traceability Monitoring Model for Discrete Manufacturing Workshops with Event Concurrency

by Zhiqiang Pan, Shuo Zhu, Zhigang Jiang, Xin Chen and Hua Zhang

Sustainability 2026, 18(16), 8363; https://doi.org/10.3390/su18168363 - 14 Aug 2026

Abstract

Carbon efficiency, measuring effective output per unit of carbon emissions, is vital for managing low-carbon workshops and advancing sustainable manufacturing. However, production processes often face concurrent discrete events (e.g., equipment failures, parameter adjustments) and numerous emission factors with complex relationships, making it hard [...] Read more.

Carbon efficiency, measuring effective output per unit of carbon emissions, is vital for managing low-carbon workshops and advancing sustainable manufacturing. However, production processes often face concurrent discrete events (e.g., equipment failures, parameter adjustments) and numerous emission factors with complex relationships, making it hard to identify dominant factors and event impact degrees, thus lacking direction for operation and maintenance decisions. This paper proposes a deductive monitoring model to analyze carbon efficiency changes under event concurrency. First, for traceability, a multi-resolution enhanced carbon efficiency information transfer network is proposed. It classifies emission factors into time-driven and event-driven accounting, and under the Parallel Discrete Event System Specification framework, adopts a multi-resolution approach with high- and low-resolution models for hierarchical aggregation from equipment to workshop, establishing a traceability path to specific factors. Second, for unclear impact degrees, a dynamic monitoring model for concurrent events is designed. A state-driven dynamic carbon efficiency accounting method automatically settles upon equipment state switching, and a rule-driven priority deduction strategy enables independent accounting of each event’s impact degrees in a determined order. A case study on a machine tool spindle production workshop validates the proposed model. Under baseline conditions, the relative accounting errors for 8 h cumulative carbon emissions and effective output are approximately 1.05% and 0.89%, respectively. In concurrent event scenarios, the model achieves deterministic trajectory reproducibility across 30 independent deduction runs and enables independent impact-degree decomposition, whereas traditional discrete event simulation exhibits trajectory ambiguity. Furthermore, testing under 42 multi-parameter perturbation combinations demonstrates traceability path integrity and accurate root-cause localization, delivering a transparent and reliable quantitative basis for low-carbon maintenance decisions. Full article

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