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A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Air, Climate Change and Sustainability".

Deadline for manuscript submissions: 15 May 2024 | Viewed by 13105

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Dr. Quoc Bao Pham

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Guest Editor

Faculty of Natural Sciences, Institute of Earth Sciences, University of Silesia in Katowice, Będzińska Street 60, 41-200 Sosnowiec, Poland
Interests: hydrology; groundwater; machine learning; water resources management; climate change; GIS; remote sensing
Special Issues, Collections and Topics in MDPI journals

Dr. Antonietta Varasano

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Guest Editor

ITC - Construction Technologies Institute, CNR - Italian National Research, 70124 Bari, Italy
Interests: geospatial web (WebGIS); GIS modelling; hyperspectral remote sensing image analysis; machine learning; deep learning; environment

Dr. Meriame Mohajane

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Guest Editor

ITC - Construction Technologies Institute, CNR - Italian National Research, 70124 Bari, Italy
Interests: land use/land cover modelling; vegetation; forest fire; climate change; prediction; geostatistical analysis; ecological monitoring and assessment; geoinformatics (GIS); multi-/hyperspectral remote sensing; machine learning; deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Currently, climate change is dramatically impacting natural resources and the environment at an ever-unprecedented rate across the world. Low precipitation combined with higher temperatures has consequently led to extreme natural disasters, including drought, deforestation, flood, soil erosion, desertification, etc. Outlining and understanding how climate change affects the Earth’s ecosystems can enable us to provide coherent and sustainable support for policymakers and decision-makers to develop adequate strategies towards the achievement of the Sustainable Development Goals (SDGs) according to their 13 targets related to combating the effects of climate change.

Authors from different fields are invited to submit original manuscripts on topics including (but not limited to):

Climate change;
Environment;
Natural resources;
Sustainable Development Goals (SDGs);
Climate change adaptation;
Natural disasters;
Floods;
Droughts;
Desertification;
Deforestation;
Soil erosion.

Dr. Quoc Bao Pham
Dr. Antonietta Varasano
Dr. Meriame Mohajane
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

environmental assessment
climate change
adaptation
sustainable management
natural resources
environment
earth ecosystems
decision-makers

Published Papers (9 papers)

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Research

23 pages, 2425 KiB

Open AccessArticle

Impact of Green Infrastructure Investment on Urban Carbon Emissions in China

by Jinhui Sang and Lingying Pan

Sustainability 2024, 16(7), 2668; https://doi.org/10.3390/su16072668 - 25 Mar 2024

Viewed by 727

Abstract

Given the increasingly severe global climate change, the reduction in urban greenhouse gas emissions has become the common goal of all nations. As a widely concerned sustainable development strategy, green infrastructure investment (GII) aims to reduce urban carbon emissions, improve the efficiency of resource utilization, and improve environmental quality. However, the construction cycle of green infrastructure is long, and the construction process itself may produce carbon emissions; so, the final effect of GII on urban carbon emissions is unclear, which deserves our in-depth study. Further, is this effect having a time-lag effect? Is there only a simple linear relationship between GII and urban carbon emissions? Based on panel data from 235 Chinese cities from 2006 to 2019, this study conducted an econometric regression analysis using time-lag-effect and threshold-effect models. The results showed the following: (1) GII had a negative inhibitory effect on urban CO₂ emissions. Adding one unit to the GII could reduce urban CO₂ emissions by 0.032 units. (2) GII exhibited a time-lag effect on urban CO₂ emissions, and the greatest reduction in CO₂ emissions occurred in the third lag period. (3) GII had a threshold effect on urban CO₂ emissions based on technological progress (TP). This paper used the static and dynamic panel threshold models to research separately, and obtained the corresponding regression results. In the static panel, the double threshold values for TP were 3.9120 and 6.8035. At different TP levels, GII had an inhibitory effect on CO₂ emissions, but the coefficients were different. However, in the dynamic panel, the threshold value was 3.666. The threshold changed over time and the effect of GII on CO₂ emissions shifted from facilitation to inhibition. Full article

(This article belongs to the Special Issue Climate Change and Environmental Resource Conservation for Sustainable Development)

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30 pages, 18455 KiB

Open AccessArticle

Mitigation and Adaptation Strategies for Different Urban Fabrics to Face Increasingly Hot Summer Days Due to Climate Change

by Paola Lassandro, Sara Antonella Zaccaro and Silvia Di Turi

Sustainability 2024, 16(5), 2210; https://doi.org/10.3390/su16052210 - 06 Mar 2024

Viewed by 528

Abstract

As global warming and heat waves are becoming more frequent and severe, cities, with their different morphological districts, must be at the forefront of environmental challenges. Notably, many Mediterranean towns maintain the original medieval urban fabric and the regular one. The research focuses on the development of a methodology with the application of high-resolution 3D modelling software ENVI-met V5.1 to analyze the microclimatic effects of mitigation and adaptation strategies derived from the study of medieval and regular urban fabric. The aim is to address contemporary challenges such as heat waves and urban heat island (UHI) effects in modern cities. By studying outdoor energy behavior in a southern Italian city (Bari), the research proposes scenarios for urban settlements in the face of climate change. This approach provides recommendations for creating more climate-resilient urban environments both in the historic and modern city. The use of trees with large crowns and tall shrubs and the inclusion of fountain jets are strategies to achieve sky view factor and air temperatures in the modern city similar to those in the historical fabric. Increasing albedo values and the use of green roofs prove to be further strategies for improving outdoor climatic conditions. Full article

(This article belongs to the Special Issue Climate Change and Environmental Resource Conservation for Sustainable Development)

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22 pages, 11074 KiB

Open AccessArticle

Analysis of Supply–Demand Relationship of Cooling Capacity of Blue–Green Landscape under the Direction of Mitigating Urban Heat Island

by Shengyu Guan, Shuang Liu, Xin Zhang, Xinlei Du, Zhifang Lv and Haihui Hu

Sustainability 2023, 15(14), 10919; https://doi.org/10.3390/su151410919 - 12 Jul 2023

Cited by 1 | Viewed by 883

Abstract

Urban blue–green landscapes (UBGLs) have an important impact on the mitigation of UHIs. Clarifying the supply/demand relationship of the UBGLs’ cooling effect can serve as an indicator for high-quality urban development. We established the cooling capacity supply–demand evaluation systems of UBGLs by using multi-source data and a suitable landscape mesh size. Furthermore, we utilized the coupling coordination degree (CCD) model and the linear regression equation method to explore the spatial distribution of and variation in UBGLs’ cooling efficiency. The results showed the following: (1) according to the UBGL/SUHI landscape pattern index and the Pearson correlation coefficient of the land surface temperature (LST), the optimal mesh size was found to be 1200 m. (2) According to the unitary linear regression calculation, the matching of the cooling capacity supply and demand in the context of Qunli New Town showed obvious polarization; furthermore, Hanan new town and old town are more balanced than Qunli new town. (3) According to the spatiotemporal dynamic evolution of CCD, the proportion of moderate coordination- advancing cooling efficiency is the highest, reaching 35.3%. Second are moderate imbalance–hysteretic cooling efficiency (18.4%) and moderate imbalance–systematic balanced development (13.7%), with the old city highly coordinated area as the center and the coupling coordination type (gradually outward) turning into a state of serious imbalance, and then back into a state of high coordination. The findings of the investigations enriched a new viewpoint and practical scientific basis for UBGL system planning and cooling efficiency equity realizations. Full article

(This article belongs to the Special Issue Climate Change and Environmental Resource Conservation for Sustainable Development)

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19 pages, 1374 KiB

Open AccessArticle

Study on the Influence Mechanism and Adjustment Path of Climate Risk on China’s High-Quality Economic Development

by Jingfeng Zhao and Fan Sun

Sustainability 2023, 15(12), 9773; https://doi.org/10.3390/su15129773 - 19 Jun 2023

Cited by 1 | Viewed by 778

Abstract

The quantitative analysis of the economic impact of climate risk is an effective means of understanding and taking reasonable preventative steps in relation to the climate-related economic crisis. This paper takes panel data from China’s 31 provinces for 2009 to 2021, combined with a regulating intermediary effect model, to determine the climate risk faced in China and its influence mechanism on high-quality economic development, in an attempt to determine how to adjust the path. The results show that, first, when using a different regression model, we see that climate risks pose a significantly inhibiting effect on high-quality economic development in China. Secondly, when the climate risk increases by 1%, high-quality economic development drops by 0.0115%. When the climate risk increases by 1%, this leads to a 14.9672% increase in the likelihood of natural disasters, causing high-quality economic development to be indirectly reduced by 0.1300%. Thirdly, green innovation has a multidimensional effect; it can both directly and indirectly impact the negative effects of inhibition, and indirect adjustment has a greater effect than direct adjustment. Such regulation has a greater effect on the input than on the output. Therefore, we should seek to more accurately understand the dangers of climate risk, effectively improve the five aspects of development, and strengthen the input of green innovation and thus the output of high-quality economic development in China. Full article

(This article belongs to the Special Issue Climate Change and Environmental Resource Conservation for Sustainable Development)

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24 pages, 3508 KiB

Open AccessArticle

Regional Differences, Dynamic Evolution and Driving Factors Analysis of PM_2.5 in the Yangtze River Economic Belt

by Weiguang Wang and Yangyang Wang

Sustainability 2023, 15(4), 3381; https://doi.org/10.3390/su15043381 - 13 Feb 2023

Cited by 1 | Viewed by 1315

Abstract

The proposal of a “dual-carbon” goal puts forward higher requirements for air pollution control. Identifying the spatial-temporal characteristics, regional differences, dynamic evolution, and driving factors of PM_2.5 are the keys to formulating targeted haze reduction measures and ameliorating air quality. Therefore, adopting the Dagum Gini Coefficient and its decomposition method, the Kernel Density Estimation model, and spatial quantile regression model, this study analyzes the regional differences, dynamic evolution, and driving factors of PM_2.5 concentrations (PM_2.5) in the Yangtze River Economic Belt (YREB) and the upstream, midstream, and downstream (the three regions) from 2003 to 2018. The study shows that: (1) PM_2.5 in the YREB was characterized by increasing first and then decreasing, with evident heterogeneity and spatial agglomeration characteristics. (2) Inter-regional differences and intensity of trans-variation were the primary sources of PM_2.5 differences. (3) The density curve of PM_2.5 shifted to the left in the YREB and the upstream, midstream, and midstream, suggesting that PM_2.5 has declined. (4) Industrial service level (IS) and financial expenditure scale (FES) exerted a significant and negative effect on PM_2.5 across the quantiles. On the contrary, population density (PD) showed a significant and positive influence. Except for the 75th quantile, the technology level (TEC) significantly inhibited PM_2.5. The remaining variables had a heterogeneous impact on PM_2.5 at different quantiles. The above results suggest that regional joint prevention and control mechanisms, collaborative governance mechanisms, and comprehensive policy mix mechanisms should be established to cope with PM_2.5 pollution and achieve green, sustainable economic development of the YREB. Full article

(This article belongs to the Special Issue Climate Change and Environmental Resource Conservation for Sustainable Development)

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14 pages, 1875 KiB

Open AccessArticle

Soil Erosion Modelling and Accumulation Using RUSLE and Remote Sensing Techniques: Case Study Wadi Baysh, Kingdom of Saudi Arabia

by Nuaman Ejaz, Mohamed Elhag, Jarbou Bahrawi, Lifu Zhang, Hamza Farooq Gabriel and Khalil Ur Rahman

Sustainability 2023, 15(4), 3218; https://doi.org/10.3390/su15043218 - 09 Feb 2023

Cited by 10 | Viewed by 1874

Abstract

This study examines the sediment retention in Wadi Baysh using the Revised Universal Soil Loss Equation (RUSLE) and TerrSet models, accompanied by integrated remote sensing and Geographic Information System (GIS) techniques. The contribution of this study is mainly associated with the application of TerrSet integrated with high resolution datasets to precisely estimate sediments load, which provide useful information to operate dams and improve the operational efficiency of dams. The Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) data are utilized to delineate the basin and have been used as an input to the TerrSet model. The rainfall erosivity (R factor) was calculated using the Climate Hazards Center Infrared Precipitation with Stations (CHIRPS) in the research area during 2015–2020. The soil erodibility (K factor) and LULC categorization are calculated using the digital soil map of the world (DSMW) and Sentinel-2 datasets, respectively. The R factor calculated for Wadi Baysh ranges between 91.35 and 115.95 MJ mm/ha/h/year, while the estimated K factor ranges from 0.139 to 0.151 t ha h/ha M. The Support Vector Machine (SVM) method categorized LULC of the study area into four major classes including barren land (81% of the total area), built-up area (11%), vegetation (8%), and water bodies (1%). Results from the sediment retention module (TerrSet) indicated that each year, 57.91 million tons of soil loss occurred in the basin. The data show that soil loss is greater in the northeast and south, whereas it is typical in the middle of Wadi Baysh. It is concluded from the current analyses that the dam lake of Wadi Baysh, located downstream, will be filled soon in the coming few years if sediment loads are carried to the lake at the same rate. Surface dam operators can obtain a full understanding of sedimentation and take proactive measures to reduce its influence on dam operations by leveraging TerrSet’s sophisticated capabilities. Full article

(This article belongs to the Special Issue Climate Change and Environmental Resource Conservation for Sustainable Development)

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12 pages, 6845 KiB

Open AccessArticle

Evaluating Sediment Yield Response to Watershed Management Practices (WMP) by Employing the Concept of Sediment Connectivity

by Hadi Nazaripouya, Mehdi Sepehri, Abbas Atapourfard, Bagher Ghermezcheshme, Celso Augusto Guimarães Santos, Mehdi Khoshbakht, Sarita Gajbhiye Meshram, Vikas Kumar Rana, Nguyen Thi Thuy Linh, Quoc Bao Pham and Duong Tran Anh

Sustainability 2023, 15(3), 2346; https://doi.org/10.3390/su15032346 - 27 Jan 2023

Cited by 1 | Viewed by 1465

Abstract

Watershed management practices (WMP) are widely used in catchments as a measure to reduce soil erosion and sediment-related problems. We used a paired catchment in the Gonbad region of Hamadan province, Iran, to evaluate sediment yield response to watershed management practices (WMP) by employing the concept of sediment connectivity (SC). To do this, the SC index as a representation of sediment yield was firstly simulated for the control catchment that there is no WMP. In the next step, the SC index was simulated for impacted catchment, including some WMP, i.e., seeding, pit-seeding, and exclosure. After assessing the accuracy of the produced SC maps using filed observations and erosion plots, the SC maps using quantile-quantile plot (Q-Q plot) were compared to achieve the role of WMP in reducing the rate of sediment yield. The Q-Q plot showed that there is a strong similarity between the SC of catchments, it can be concluded that the WMP has no significant impact on the reducing rate of the sediment yield in this study. Full article

(This article belongs to the Special Issue Climate Change and Environmental Resource Conservation for Sustainable Development)

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19 pages, 4387 KiB

Open AccessArticle

Assessment and Prediction of the Water Quality Index for the Groundwater of the Ghiss-Nekkor (Al Hoceima, Northeastern Morocco)

by Yassine El Yousfi, Mahjoub Himi, Hossain El Ouarghi, Mourad Aqnouy, Said Benyoussef, Hicham Gueddari, Hanane Ait Hmeid, Abdennabi Alitane, Mohamed Chaibi, Muhammad Zahid, Narjisse Essahlaoui, Sliman Hitouri, Ali Essahlaoui and Abdallah Elaaraj

Sustainability 2023, 15(1), 402; https://doi.org/10.3390/su15010402 - 26 Dec 2022

Cited by 10 | Viewed by 2266

Abstract

Water quality index (WQI) is the primary method applied to characterize water quality in the world. The current study employed the statistical analysis and multilayer perceptron (MLP) approaches for predicting groundwater quality in the Ghiss-Nekkor aquifer, northeast of Al Hoceima, Morocco. Fifty sampled groundwater were identified and analyzed for major anions and cations throughout May 2019. Several physicochemical parameters of all the samples were identified in this investigation, such as TDS, pH, EC, Na, K, Ca, Mg, HCO₃, NO₃, Br, SO₄, and Cl. The entropy-weighted groundwater quality index (EWQI) was calculated from these parameters. The WQI procedure determined the suitability of groundwater for consumption. The WQI value varied from 90.98 to 337.28. The EC, TDS, WQI, and Cl⁻ spatial distribution showed that EC and Cl⁻ are associated with poor groundwater quality. A single sample (W16) represented unsuitable water for drinking purposes and offered a WQI value of 337.28, indicating poor drinking quality due to seawater intrusion, overexploitation, and harsh weather conditions. The majority of the values obtained for the parameters exceeded the recommended limit of the World Health Organization (WHO)’s guidelines for consumption. The findings show that using parameters is a straightforward method for predicting water quality indexes with sufficient and suitable precision. The MLP model shows good predictive performances in terms of the coefficient of determination R², mean absolute error (MAE), and root-mean-square error (RMSE) with values of 0.9885, 5.8031, and 4.7211, respectively. The ANN approach was applied to develop a model that can accurately predict WQI utilizing mineralization, TH, NO3, and NO2 as inputs. The MAE for the model’s performance was calculated to be 4.72. A Bland–Altman test was used to validate that the model is suitable. Following the test, it was determined that the model is appropriate for predicting WQI, with an error of just 0.1%. Full article

(This article belongs to the Special Issue Climate Change and Environmental Resource Conservation for Sustainable Development)

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24 pages, 4665 KiB

Open AccessArticle

Comparison of CLDAS and Machine Learning Models for Reference Evapotranspiration Estimation under Limited Meteorological Data

by Long Qian, Lifeng Wu, Xiaogang Liu, Yaokui Cui and Yongwen Wang

Sustainability 2022, 14(21), 14577; https://doi.org/10.3390/su142114577 - 06 Nov 2022

Cited by 2 | Viewed by 1287

Abstract

The accurate calculation of reference evapotranspiration (ET₀) is the fundamental basis for the sustainable use of water resources and drought assessment. In this study, we evaluate the performance of the second-generation China Meteorological Administration Land Data Assimilation System (CLDAS) and two simplified machine learning models to estimate ET₀ when meteorological data are insufficient in China. The results show that, when a weather station lacks global solar radiation (R_s) data, the machine learning methods obtain better results in their estimation of ET₀. However, when the meteorological station lacks relative humidity (RH) and 2 m wind speed (U₂) data, using RH_CLD and U_2CLD from the CLDAS to estimate ET₀ and to replace the meteorological station data obtains better results. When all the data from the meteorological station are missing, estimating ET₀ using the CLDAS data still produces relevant results. In addition, the PM–CLDAS method (a calculation method based on the Penman–Monteith formula and using the CLDAS data) exhibits a relatively stable performance under different combinations of meteorological inputs, except in the southern humid tropical zone and the Qinghai–Tibet Plateau zone. Full article

(This article belongs to the Special Issue Climate Change and Environmental Resource Conservation for Sustainable Development)

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