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26 pages, 7934 KiB  
Article
Study of Land Surface Changes in Highland Environments for the Sustainable Management of the Mountainous Region in Gilgit-Baltistan, Pakistan
by Amjad Ali Khan, Xian Xue, Hassam Hussain, Kiramat Hussain, Ali Muhammad, Muhammad Ahsan Mukhtar and Asim Qayyum Butt
Sustainability 2024, 16(23), 10311; https://doi.org/10.3390/su162310311 - 25 Nov 2024
Abstract
Highland ecologies are the most susceptible to climate change, often experiencing intensified impacts. Due to climate change and human activities, there were dramatic changes in the alpine domain of the China–Pakistan Economic Corridor (CPEC), which is a vital project of the Belt and [...] Read more.
Highland ecologies are the most susceptible to climate change, often experiencing intensified impacts. Due to climate change and human activities, there were dramatic changes in the alpine domain of the China–Pakistan Economic Corridor (CPEC), which is a vital project of the Belt and Road Initiative (BRI). The CPEC is subjected to rapid infrastructure expansion, which may lead to potential land surface susceptibility. Hence, focusing on sustainable development goals, mainly SDG 9 (industry, innovation, and infrastructure) and SDG 13 (climate action), to evaluate the conservation and management practices for the sustainable and regenerative development of the mountainous region, this study aims to assess change detection and find climatic conditions using multispectral indices along the mountainous area of Gilgit and Hunza-Nagar, Pakistan. It has yielded practical and highly relevant implications. For sustainable and regenerative ecologies, this study utilized 30 × 30 m Landsat 5 (TM), Landsat 7 (ETM+), and Landsat-8/9 (OLI and TIRS), and meteorological data were employed to calculate the aridity index (AI). The results of the AI showed a non-significant decreasing trend (−0.0021/year, p > 0.05) in Gilgit and a significant decreasing trend (−0.0262/year, p < 0.05) in Hunza-Nagar. NDVI distribution shows a decreasing trend (−0.00469/year, p > 0.05), while NDWI has depicted a dynamic trend in water bodies. Similarly, NDBI demonstrated an increasing trend, with rates of 79.89%, 87.69%, and 83.85% from 2008 to 2023. The decreasing values of AI mean a drying trend and increasing drought risk, as the study area already has an arid and semi-arid climate. The combination of multispectral indices and the AI provides a comprehensive insight into how various factors affect the mountainous landscape and climatic conditions in the study area. This study has practical and highly relevant implications for policymakers and researchers interested in research related to land use and land cover change, environmental and infrastructure development in alpine regions. Full article
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<p>Study area map: (<b>a</b>) Pakistan’s map; (<b>b</b>) map of the Gilgit-Baltistan (GB) Province of Pakistan; (<b>c</b>) study area location, with a 10 km buffer along the CPEC route in three districts (Gilgit, Hunza, and Nagar) of Gilgit-Baltistan, Pakistan.</p>
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<p>Distribution of minimum, mean, and maximum NDVIs from 2008 to 2023.</p>
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<p>Spatial pattern of NDVI change due to build-up in the study area from 2008 to 2023.</p>
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<p>Spatial pattern of NDVI change due to build-up in the study area from 2008 to 2023.</p>
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<p>Spatial pattern of NDVI change due to water in the study area from 2008 to 2023.</p>
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<p>Spatial pattern of NDVI change due to water in the study area from 2008 to 2023.</p>
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<p>Distribution of NDWI from 2008 to 2023, with four-year intervals.</p>
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<p>Spatial change in NDWI from 2008 to 2023 and significant at 0.01, 0.05 level.</p>
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<p>Spatial change in NDBI from 2008 to 2023 and significant at 0.01, 0.05 level.</p>
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<p>Distribution of NDBI from 2008 to 2023, with four-year intervals.</p>
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<p>The trend of the aridity index in the study area.</p>
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<p>Shows (<b>a</b>) population dynamics and (<b>b</b>) tourist flow in the study area.</p>
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<p>Temporal variation and linear trend along the CPEC route from 2008 to 2023; annual precipitation in (<b>a</b>) Gilgit and (<b>b</b>) Hunza-Nagar; annual temperature in (<b>c</b>) Gilgit and (<b>d</b>) Hunza-Nagar.</p>
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<p>Temporal variation and linear trend along the CPEC route from 2008 to 2023; annual precipitation in (<b>a</b>) Gilgit and (<b>b</b>) Hunza-Nagar; annual temperature in (<b>c</b>) Gilgit and (<b>d</b>) Hunza-Nagar.</p>
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26 pages, 2100 KiB  
Article
Energy–Economy–Carbon Emissions: Impacts of Energy Infrastructure Investments in Pakistan Under the China–Pakistan Economic Corridor
by Xiue Li, Zhirao Liu and Tariq Ali
Sustainability 2024, 16(23), 10191; https://doi.org/10.3390/su162310191 - 21 Nov 2024
Viewed by 458
Abstract
Energy–economy–environment sustainability is critical in shaping energy policies, especially in developing countries facing energy shortages. Investment in energy infrastructure, such as under the China–Pakistan Economic Corridor (CPEC), provides an opportunity to explore how such investments impact economic growth, environmental quality, and energy security. [...] Read more.
Energy–economy–environment sustainability is critical in shaping energy policies, especially in developing countries facing energy shortages. Investment in energy infrastructure, such as under the China–Pakistan Economic Corridor (CPEC), provides an opportunity to explore how such investments impact economic growth, environmental quality, and energy security. This study examines the energy, economic, and environmental effects of CPEC’s energy investments in Pakistan, covering a range of power sources, including coal, hydro, solar, wind, and nuclear energy. Utilizing data from 31 CPEC energy projects and employing the GTAP-E-Power model, this research assesses these impacts through seven scenarios, comprehensively analyzing the heterogeneity of different power sources. Our findings reveal that while all types of CPEC energy infrastructure investments contribute to increasing the share of zero-emissions electricity to 49.1% and reducing CO2 emissions by 18.61 million tons, the economic impacts vary significantly by energy source. The study suggests that it is crucial to prioritize renewable energy investments while addressing immediate power shortages to balance economic growth with environmental sustainability. Policymakers should also consider the potential inter-sectoral substitution effects when applying significant shocks to specific sectors. This analysis informs future energy investment decisions under CPEC and offers insights for other Belt and Road Initiative (BRI) countries aiming to optimize their energy strategies for sustainable development. Full article
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<p>The framework of the methodology, database, and procedure. The database is aggregated using GTAPagg2, while updates and simulations are performed using GEMPACK. Please refer to [<a href="#B28-sustainability-16-10191" class="html-bibr">28</a>] for the GTAP-E-Power Model.</p>
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<p>Nested electric power substitution in the GTAP-E-Power model and CO<sub>2</sub> releasing energy commodities. The sub-sectors of electricity are detailed in <a href="#app1-sustainability-16-10191" class="html-app">Appendix A</a> <a href="#sustainability-16-10191-t0A7" class="html-table">Table A7</a>. Source: Adapted from the GTAP-E-Model [<a href="#B28-sustainability-16-10191" class="html-bibr">28</a>].</p>
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<p>Overview of CPEC energy infrastructure investment. (<b>a</b>) Installed capacity (MW) added to different electricity generation sources. (<b>b</b>) Share of installed capacity (%) added to each province of Pakistan. (<b>c</b>) Estimated cost (USD million) for different electricity generation sources. (<b>d</b>) Share of estimated cost (%) for each province of Pakistan. Source: Calculated based on the project-level information in <a href="#app1-sustainability-16-10191" class="html-app">Appendix A</a> <a href="#sustainability-16-10191-t0A4" class="html-table">Table A4</a>, <a href="#sustainability-16-10191-t0A5" class="html-table">Table A5</a> and <a href="#sustainability-16-10191-t0A6" class="html-table">Table A6</a>.</p>
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<p>Change in energy output and price. (<b>a</b>) Percentage change in the output of electricity sub-sectors and non-electricity sectors (%). (<b>b</b>) Value change in the output of electricity sub-sectors and non-electricity sectors (USD million). (<b>c</b>) Percentage change in the price of electricity sub-sectors and non-electricity sectors (%). All changes are relative to the situation in the base year. Since HydroP and GasP in Pakistan are zero, there are no results for them. Source: Calculated based on simulation results from the GTAP-E-Power model.</p>
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<p>Change in energy structure. (<b>a</b>) Output value share of electricity generated from zero-emissions power sources (NuclearBL, HydroBL, WindBL, and SolarP) and fuel-fired power sources (CoalBL, GasBL, OilBL, other BL, and OilP). (<b>b</b>) Output value share of electricity generated from each power source. “Pre” refers to the situation before shocks in the base year. Source: Calculated based on simulation results from the GTAP-E-Power model.</p>
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<p>Change in CO<sub>2</sub> emissions from fuel energy commodities. (<b>a</b>) Percentage change in CO<sub>2</sub> emissions (%) from coal, oil, gas, p_c, and gas supply in Pakistan. (<b>b</b>) Absolute change in CO<sub>2</sub> emissions (Mts) from coal, oil, gas, p_c, and gas supply in Pakistan. The last two rows refer to the total absolute change in CO<sub>2</sub> emissions in Pakistan and the world, respectively. All changes are relative to the situation in the base year. Source: Calculated based on simulation results from the GTAP-E-Power model.</p>
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<p>Change in CO<sub>2</sub> emissions from production across different sectors. (<b>a</b>) CO<sub>2</sub> emissions in the base year before shocks (Mts). (<b>b</b>) CO<sub>2</sub> emissions in scenario S7 (Mts). This figure represents CO<sub>2</sub> emissions from firm activities, covering 80% of Pakistan’s total emissions. The remaining 20% comes from consumption. Coal, oil, gas, p_c, and gas supply are the five fuel energy commodities that release CO<sub>2</sub>. Here, the nodes on the left represent different sectors (the sources) that use these energy commodities and thus emit CO<sub>2</sub>, while the nodes on the right correspond to the specific energy commodities (the target). The production of these energy commodities also emits (embodied) CO<sub>2</sub>, but the emissions are relatively very small. Source: Calculated based on simulation results from the GTAP-E-Power model.</p>
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<p>Change in non-energy sectors. (<b>a</b>) Percentage change in the output of non-energy sectors (%). (<b>b</b>) Percentage change in the price of non-energy sectors (%). All changes are relative to the situation in the base year. Source: Calculated based on simulation results from the GTAP-E-Power model.</p>
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23 pages, 28771 KiB  
Article
Land Use Changes and Future Land Use Scenario Simulations of the China–Pakistan Economic Corridor under the Belt and Road Initiative
by Yuanjie Deng, Hang Chen and Yifeng Hai
Sustainability 2024, 16(20), 8842; https://doi.org/10.3390/su16208842 - 12 Oct 2024
Viewed by 866
Abstract
The China–Pakistan Economic Corridor (CPEC), as an important part of the Belt and Road Initiative, is of great significance for the promotion of sustainable development in the region through the study of land use change and the simulation of future multi-scenarios. Based on [...] Read more.
The China–Pakistan Economic Corridor (CPEC), as an important part of the Belt and Road Initiative, is of great significance for the promotion of sustainable development in the region through the study of land use change and the simulation of future multi-scenarios. Based on the multi-period land use data of the CPEC, this study firstly analyzed the spatial and temporal land use changes in the CPEC from 2000 to 2020 by using GIS technology, and, secondly, simulated the land use patterns of the CPEC under four scenarios, namely, natural development, investment priority, ecological protection, and harmonious development, in 2040 by using the Markov-FLUS model with comprehensive consideration of natural, socio-economic, and other driving factors. The results show the following: (1) The urban land, forest land, and grassland in the CPEC from 2000 to 2020 show an increasing trend, while the farmland, unutilized land, and water area categories show a decreasing trend. In terms of land use transfer changes, the most frequently transferred out is the conversion of unutilized land to grassland. (2) The FLUS model has high accuracy in simulating the land use pattern of the CPEC, and its applicability in the CPEC area is strong and can be used to simulate the future land use pattern of the CPEC. (3) Among the four different land use scenarios, the harmonious development scenario strikes a better balance between infrastructure construction, economic development, and ecological protection, and can provide a scientific basis for future land management in the CPEC, in order to highlight the importance of promoting economic growth and ecological protection and ultimately realize sustainable development. Full article
(This article belongs to the Section Resources and Sustainable Utilization)
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<p>Overview of the study area.</p>
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<p>Technical route.</p>
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<p>Trends in the ecological and economic evolution and development of the CEPC under future multi-scenario simulations.</p>
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<p>Land use changes in the CPEC, 2000–2020.</p>
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<p>Sankey diagram of land use change, 2000–2020.</p>
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<p>Land use transfer matrix for the CPEC, 2000–2020.</p>
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<p>Current land use status and simulation results of the CPEC in 2020.</p>
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<p>Drivers of land use change in the CPEC.</p>
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<p>Multi-scenario simulation of land use shares for 2020 and 2040.</p>
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<p>Four hypothetical scenarios were designed using Markovian forecasting to project future land use demand in the CPEC.</p>
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<p>Local zoom-in maps of land use in the China-Pakistan Economic Corridor in 2020 (<b>a1</b>–<b>a3</b>); Local zoom-in maps of land use in the China-Pakistan Economic Corridor in 2040 under the Natural Development (<b>A1</b>–<b>A3</b>), Investment Priority (<b>B1</b>–<b>B3</b>), Ecological Protection (<b>C1</b>–<b>C3</b>), and Harmonious Development (<b>D1</b>–<b>D3</b>) scenarios.</p>
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25 pages, 34633 KiB  
Article
Identification of Potential Landslides in the Gaizi Valley Section of the Karakorum Highway Coupled with TS-InSAR and Landslide Susceptibility Analysis
by Kaixiong Lin, Guli Jiapaer, Tao Yu, Liancheng Zhang, Hongwu Liang, Bojian Chen and Tongwei Ju
Remote Sens. 2024, 16(19), 3653; https://doi.org/10.3390/rs16193653 - 30 Sep 2024
Viewed by 1044
Abstract
Landslides have become a common global concern because of their widespread nature and destructive power. The Gaizi Valley section of the Karakorum Highway is located in an alpine mountainous area with a high degree of geological structure development, steep terrain, and severe regional [...] Read more.
Landslides have become a common global concern because of their widespread nature and destructive power. The Gaizi Valley section of the Karakorum Highway is located in an alpine mountainous area with a high degree of geological structure development, steep terrain, and severe regional soil erosion, and landslide disasters occur frequently along this section, which severely affects the smooth flow of traffic through the China-Pakistan Economic Corridor (CPEC). In this study, 118 views of Sentinel-1 ascending- and descending-orbit data of this highway section are collected, and two time-series interferometric synthetic aperture radar (TS-InSAR) methods, distributed scatter InSAR (DS-InSAR) and small baseline subset InSAR (SBAS-InSAR), are used to jointly determine the surface deformation in this section and identify unstable slopes from 2021 to 2023. Combining these data with data on sites of historical landslide hazards in this section from 1970 to 2020, we constructed 13 disaster-inducing factors affecting the occurrence of landslides as evaluation indices of susceptibility, carried out an evaluation of regional landslide susceptibility, and identified high-susceptibility unstable slopes (i.e., potential landslides). The results show that DS-InSAR and SBAS-InSAR have good agreement in terms of deformation distribution and deformation magnitude and that compared with single-orbit data, double-track SAR data can better identify unstable slopes in steep mountainous areas, providing a spatial advantage. The landslide susceptibility results show that the area under the curve (AUC) value of the artificial neural network (ANN) model (0.987) is larger than that of the logistic regression (LR) model (0.883) and that the ANN model has a higher classification accuracy than the LR model. A total of 116 unstable slopes were identified in the study, 14 of which were determined to be potential landslides after the landslide susceptibility results were combined with optical images and field surveys. These 14 potential landslides were mapped in detail, and the effects of regional natural disturbances (e.g., snowmelt) and anthropogenic disturbances (e.g., mining projects) on the identification of potential landslides using only SAR data were assessed. The results of this research can be directly applied to landslide hazard mitigation and prevention in the Gaizi Valley section of the Karakorum Highway. In addition, our proposed method can also be used to map potential landslides in other areas with the same complex topography and harsh environment. Full article
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Graphical abstract

Graphical abstract
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<p>Location of the study area; (<b>a</b>,<b>b</b>) show where the study area is located; (<b>c</b>) shows the spatial distribution of historical landslide hazards in the study area; and (<b>d</b>,<b>e</b>) are field photographs of the highway surroundings.</p>
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<p>Pearson correlation test for landslide environmental factors; where * ** *** represents the level of significance.</p>
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<p>Landslide susceptibility evaluation indicators: (<b>a</b>) Elevation, (<b>b</b>) Curvature, (<b>c</b>) Slope, (<b>d</b>) TWI, (<b>e</b>) Aspect, (<b>f</b>) Soil type, (<b>g</b>) Land cover, (<b>h</b>) Snowmelt, (<b>i</b>) River Distance, (<b>j</b>) MNDWI, (<b>k</b>) Fault Distance, (<b>l</b>) NDBI, (<b>m</b>) Earthquake Distance.</p>
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<p>Spatial connectivity map of SBAS-InSAR and DS-InSAR interferometric image pairs, where (<b>a</b>) is the ascending orbit SBAS-InSAR connectivity map; (<b>b</b>) is the descending orbit SBAS-InSAR connectivity map; (<b>c</b>) is the ascending orbit DS-InSAR connectivity map; and (<b>d</b>) is the descending orbit DS-InSAR connectivity map. The dots in the figure indicate the numbers of the interferometric images, and the lines indicate the number of image pairs.</p>
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<p>Technical process.</p>
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<p>Monitoring deformation in the Gaizi Valley section of the Karakorum Highway using SBAS-InSAR and DS-InSAR; (<b>A</b>,<b>C</b>) are the results of ascending-orbit SBAS-InSAR and DS-InSAR data; (<b>B</b>,<b>D</b>) are the results of descending-orbit SBAS-InSAR and DS-InSAR data; The black rectangular areas (a)–(i) in each subfigure represent aggregated areas of unstable slopes; and the background of the figure is a hillshaded view showing the mountains.</p>
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<p>Histograms of DS-InSAR and SBAS-InSAR deformation points; (<b>a</b>) ascending SBAS-InSAR; (<b>b</b>) descending SBAS-InSAR; (<b>c</b>) ascending DS-InSAR; (<b>d</b>) descending DS-InSAR.</p>
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<p>LR and ANN model ROC curves.</p>
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<p>Landslide susceptibility map of the ANN model (<b>a</b>) vs. that of the LR model (<b>b</b>).</p>
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<p>Distribution of potential landslides in the study area.</p>
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<p>Detailed characterization of potential landslides: (1)–(12) potential landslides (white circled lines); (13)–(14) unstable slopes that are not potential landslides (red circled lines); where (<b>a</b>) demonstrates the spatial relationship between potential landslides and TS-InSAR; (<b>b</b>) Demonstrate the spatial relationship between potential landslides and landslide susceptibility; and (<b>c</b>) is a high-resolution image of potential landslides.</p>
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<p>Time deformation characteristics of potential landslides at GZHG4: (<b>a</b>) SBAS-InSAR deformation rate for the ascending track; (<b>b</b>) SBAS-InSAR deformation rate for the descending track; (<b>c</b>) DS-InSAR deformation rate for the ascending track; (<b>d</b>) DS-InSAR deformation rate for the descending track; (<b>e</b>) cumulative deformation totals at points P1, P2, and P3 in the ascending orbit direction; (<b>f</b>) cumulative deformation totals at points P1, P2, and P3 in the descending orbit direction.</p>
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<p>Field validation of potential landslide sites (GZHG5): (<b>a</b>) ascending DS-InSAR deformation distribution; (<b>b</b>) cumulative deformation at points P1–P3; (<b>c</b>) map of the overall profile of the slope; (<b>d</b>) manual excavation at the foot of the slope; (<b>e</b>) accumulation of material washed down the slope.</p>
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<p>Nonpotential landslides in a melting snow gully: (<b>a</b>) location of the study area where the snowmelt zone is located; (<b>b</b>) high-resolution image of the unstable area; (<b>c</b>) distribution of landslide hazard susceptibility; (<b>d</b>) ascending SBAS-InSAR deformation rate map; (<b>e</b>) descending SBAS-InSAR deformation rate map; (<b>f</b>,<b>g</b>) field site photographs.</p>
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<p>Landslides induced by engineering facilities at a mine site: (<b>a</b>) location of the engineering works at the mine site; (<b>b</b>) high-resolution imagery; (<b>c</b>) landslide susceptibility distribution; (<b>d</b>) ascending SBAS-InSAR deformation rate maps; (<b>e</b>) field photographs.</p>
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7 pages, 489 KiB  
Proceeding Paper
Accelerating Green Energy Transition under China–Pakistan Economic Corridor 2.0
by Zona Usmani, Sadia Satti and Muhammad Zulfiqar
Eng. Proc. 2024, 75(1), 31; https://doi.org/10.3390/engproc2024075031 - 29 Sep 2024
Viewed by 674
Abstract
This study investigates the role of the China–Pakistan Economic Corridor (CPEC) in expediting energy transition in Pakistan, specifically during its second phase of development, i.e., CPEC 2.0. The study provides an overview of energy projects under CPEC, detailing the diverse sources contributing to [...] Read more.
This study investigates the role of the China–Pakistan Economic Corridor (CPEC) in expediting energy transition in Pakistan, specifically during its second phase of development, i.e., CPEC 2.0. The study provides an overview of energy projects under CPEC, detailing the diverse sources contributing to the energy mix, highlighting China’s significant investments in green energy and its pivotal role in global renewable energy transition. A mixed-method approach is applied; the research integrates secondary data analysis with consultative discussions and key informant interviews. Findings underscore China’s pivot towards green investment, exemplified by significant commitments to clean energy infrastructure. The paper further analyzes challenges and opportunities for Pakistan under CPEC 2.0, emphasizing the imperative nature of regulatory consistency, debt restructuring, and the cultivation of public–private partnerships. Recommendations encompass policy coherence, debt management strategies, and collaboration among pertinent ministries to ensure sustainable and inclusive growth facilitated by CPEC. Full article
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<p>Framework of the consultative discussions.</p>
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<p>Top sectors for green credits according to CBIRC.</p>
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27 pages, 3734 KiB  
Review
Known and Unknown Environmental Impacts Related to Climate Changes in Pakistan: An Under-Recognized Risk to Local Communities
by Muhammad Adnan, Baohua Xiao, Shaheen Bibi, Peiwen Xiao, Peng Zhao, Haiyan Wang, Muhammad Ubaid Ali and Xianjin An
Sustainability 2024, 16(14), 6108; https://doi.org/10.3390/su16146108 - 17 Jul 2024
Cited by 3 | Viewed by 2256
Abstract
This study prioritized initiatives within the China–Pakistan Economic Corridor (CPEC), foreign funding, and the associated environmental and national issues. Additionally, it analyzed these factors’ effects on improving infrastructure, commerce, and economic cooperation between China and Pakistan. Besides that, it also studies the current [...] Read more.
This study prioritized initiatives within the China–Pakistan Economic Corridor (CPEC), foreign funding, and the associated environmental and national issues. Additionally, it analyzed these factors’ effects on improving infrastructure, commerce, and economic cooperation between China and Pakistan. Besides that, it also studies the current climatic, economic, and political challenges, mainly focused on water and agriculture issues. Climate, economic, and political issues affect the environment. These concerns deserve global attention. Pakistan mainly relies on agriculture, and its water scarcity predisposes it to economic losses, urbanization, and many socioeconomic problems. Climate change and the current flood have devastated the agriculture sector. Water scarcity affects agriculture too and significantly impacts the economy and food resources. The nation has not previously experienced such a profoundly distressing epoch. Pakistan has faced several environmental, economic, and political challenges; specifically, the fields of agriculture and water present notable apprehensions. Unfavorable climatic conditions impede the attainment of sustainable agriculture in Pakistan. Considering the strong reliance of agriculture on water resources, it is crucial to acknowledge that industrialization has resulted in substantial water contamination due to the presence of microplastics and heavy metals. Moreover, the South Asian region experiences a significant scarcity of water resources. Besides that, CPEC is the solution for the financial issues, but it is a big challenge for environmental degradation in the current stage, especially since foreign funding is a key challenge for increasing corruption and bringing more burden on the economy. Unfortunately, foreign funding is not good for Pakistan. To ensure safety, security, and sustainability, CPEC projects should follow environmental regulations. This study provides a new list of CPEC initiative priority tasks that more openly disrupt the initiative, serve the whole project, and give appropriate recommendations for future research and policy-making. Full article
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<p>SDGs are the only approach to tackling climate change, modified from [<a href="#B7-sustainability-16-06108" class="html-bibr">7</a>].</p>
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<p>(<b>a</b>) Map showing climate and hunger vulnerability scores; (<b>b</b>) mapping exposure to harm from air pollution. Exposure to air pollution (PM2.5) combined with poverty shows how local boundaries are crossed and how local people may be affected (Reprinted from refs. [<a href="#B51-sustainability-16-06108" class="html-bibr">51</a>], with permission of the publisher).</p>
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<p>Comparison between conventional water management and foreign funding.</p>
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<p>Essential to closely monitor the ongoing fluctuations in the CPEC project.</p>
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<p>Essential elements for developing and scaling beneficial effects, with examples from the four case-study technologies, (modified from [<a href="#B146-sustainability-16-06108" class="html-bibr">146</a>]).</p>
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<p>Policy concerns and related issues.</p>
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23 pages, 38019 KiB  
Article
Seismic Performance of Cross-Shaped Partially Encased Steel–Concrete Composite Columns: Experimental and Numerical Investigations
by Qiuyu Xu, Yong Liu and Jingfeng Wang
Buildings 2024, 14(7), 1932; https://doi.org/10.3390/buildings14071932 - 25 Jun 2024
Cited by 1 | Viewed by 670
Abstract
Special-shaped partially encased steel–concrete composite (PEC) columns could not only improve the aesthetic effect and room space use efficiency, but also exhibit good mechanical performance under static load when used in multi-story residential and office buildings. However, research on the seismic performance of [...] Read more.
Special-shaped partially encased steel–concrete composite (PEC) columns could not only improve the aesthetic effect and room space use efficiency, but also exhibit good mechanical performance under static load when used in multi-story residential and office buildings. However, research on the seismic performance of special-shaped PEC columns is insufficient and urgently needed. To investigate the seismic performance of cross-shaped partially encased steel–concrete composite (CPEC) columns, three CPEC columns were designed and tested under combined constant axial load and lateral cyclic load. The test results show that the CPEC columns had good load capacity and ductility, and that the columns failed because of concrete crushing and steel flange buckling after the yielding of the steel flange. The plump hysteresis loops indicated that the CPEC column also had good energy dissipation capacity. Due to the constraint of hydraulic jacks, increasing the load ratio would decrease the effective length, thereby increasing the load capacity of the CPEC column and decreasing the ductility. A finite element model was also established to simulate the response of the CPEC columns, and the simulated results agree well with the experimental results. Thereafter, an extensive parametric analysis was performed to study the influences of different parameters on the seismic performance of CPEC columns. For the CPEC column with an ideal hinged boundary condition at the top, its lateral load capacity gradually decreases with the growth of the load ratio and link spacing and increases with the rise of the steel yield strength, concrete compressive strength, flange and web thickness, and sectional aspect ratio. This research could provide a basis for future theoretical analyses and engineering application. Full article
(This article belongs to the Special Issue Novel Steel and Steel-Concrete Composite Structures)
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<p>Cross-sections of the special-shaped PEC column.</p>
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<p>Detailed information of the CPEC columns.</p>
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<p>Experimental setup.</p>
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<p>Horizontal loading procedure.</p>
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<p>Diagram of strain gauges and LVDTs.</p>
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<p>Cracks development and failure pattern of CPEC-5-100-0.2-X. (<b>a</b>) Surface S1 at 1/70<span class="html-italic">H</span>. (<b>b</b>) Surface N1 at 1/50<span class="html-italic">H</span>. (<b>c</b>) Surface N1 at 1/40<span class="html-italic">H</span>. (<b>d</b>) Surface S3 at 1/25<span class="html-italic">H</span>. (<b>e</b>) Surface S3 at 1/22<span class="html-italic">H</span>.</p>
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<p>Failure modes of the CPEC specimens. (<b>a</b>) CPEC-0.2. (<b>b</b>) CPEC-0.35. (<b>c</b>) CPEC-0.5.</p>
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<p>Hysteretic curves and skeleton curves of all specimens. (<b>a</b>) CPEC-0.2. (<b>b</b>) CPEC-0.35. (<b>c</b>) CPEC-0.5. (<b>d</b>) Skeleton curves of all specimens.</p>
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<p>Hysteretic curves and skeleton curves of all specimens. (<b>a</b>) CPEC-0.2. (<b>b</b>) CPEC-0.35. (<b>c</b>) CPEC-0.5. (<b>d</b>) Skeleton curves of all specimens.</p>
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<p>Characteristic points on skeleton curves.</p>
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<p>Strength degradation factor versus displacement curves of CPEC columns.</p>
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<p>Stiffness degradation curves of the CPEC columns.</p>
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<p>Calculation diagram of hysteretic energy dissipation curve.</p>
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<p>Cumulative energy consumption of the CPEC columns.</p>
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<p>Displacement–strain curves of CPEC-0.35. (<b>a</b>) Strain gauge 2 on steel flange. (<b>b</b>) Strain gauge 4 on steel flange. (<b>c</b>) Strain gauge 23 on link.</p>
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<p>Finite element model.</p>
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<p>Stress–strain relationship curve of concrete under uniaxial load cycle.</p>
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<p>Comparisons of experimental and simulated hysteresis and skeleton curves.</p>
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<p>Comparisons of experimental and simulated hysteresis and skeleton curves.</p>
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<p>Comparisons of experimental and simulated failure mode of CPEC-0.35.</p>
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<p>Influence of design parameters on the skeleton curves of CPEC columns.</p>
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<p>FE models with different element sizes.</p>
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<p>Simulated results comparison between the models with different element sizes.</p>
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17 pages, 3641 KiB  
Article
Changes in Surface and Terrestrial Waters in the China–Pakistan Economic Corridor Due to Climate Change and Human Activities
by Jiayu Bao, Yanfeng Wu, Xiaoran Huang, Peng Qi, Ye Yuan, Tao Li, Tao Yu, Ting Wang, Pengfei Zhang, Vincent Nzabarinda, Sulei Naibi, Jingyu Jin, Gang Long and Shuya Yang
Remote Sens. 2024, 16(8), 1437; https://doi.org/10.3390/rs16081437 - 18 Apr 2024
Viewed by 1062
Abstract
The surface water area (SWA) and terrestrial water storage (TWS) are both essential metrics for assessing regional water resources. However, the combined effects of climate change and human activities on the dynamics of the SWA and TWS have not been extensively researched within [...] Read more.
The surface water area (SWA) and terrestrial water storage (TWS) are both essential metrics for assessing regional water resources. However, the combined effects of climate change and human activities on the dynamics of the SWA and TWS have not been extensively researched within the context of the CPEC. To fill this gap, we first analyzed the annual changes in the SWA and TWS in the China–Pakistan Economic Corridor (CPEC) region in recent decades using the methods of correlation analysis and Geodetector. Our findings indicate that Sindh exhibited the highest increase in the SWA at 8.68 ha/km2, whereas FATA showed the least increase at 0.2 ha/km2 from 2002 to 2018. Punjab exhibited a significant decrease in TWS, with a slope of −0.48 cm/year. Azad Kashmir followed with a decrease in TWS at a rate of −0.36 cm/year. Khyber Pakhtunkhwa and FATA exhibited an insignificant increase in TWS, with values of 0.02 cm/year and 0.11 cm/year, respectively. TWS was significantly positively correlated with the SWA in Balochistan and Khyber Pakhtunkhwa. However, other regions showed inconsistent changes; in particular, a decline was observed in Gilgit–Baltistan. The changes in TWS in Balochistan were primarily influenced by the SWA and climate change, while TWS changes in FATA were mainly affected by climate change. In addition, human activities had a primary impact on the TWS changes in Azad Kashmir, Punjab, and Sindh. The influencing factors of TWS changes in different regions of the CPEC mainly involved a dual-factor enhancement and the nonlinear weakening of single factors. These results highlight that under the effect of climate change and human activities, TWS may not increase as surface water area increases. This study contributes to a better understanding of water resource dynamics and can aid in the development of strategies for the efficient and sustainable use of water resources in the CPEC. Full article
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<p>Geographical location of the China–Pakistan Economic Corridor as well as the spatial distribution of the elevation, river network, and dams within the study area.</p>
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<p>The interaction type between two independent variables. (Min(q(X1), q(X2) means to find the minimum value between q(X1) and q(X2); Max(q(X1), q(X2)) means to find the maximum value between q(X1) and q(X2); q(X1∩X2) means q(X1), q(X2) is interactive; q(X1) + q(X2) is used to calculate the sum of q(X1) and q(X2)). In our study, SWA and TWS were classified as attributes, whereas the cropland area, the surface area of the dam, annual evaporation, population density, annual precipitation, and the annual average temperature were identified as factors.</p>
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<p>Interannual variations and trends of annual SWA during 2002–2018 in the CPEC: (<b>a</b>–<b>c</b>) the SWA per unit land (km<sup>2</sup>), changes in the slope of SWA, and the significance level of the SWA change, respectively.</p>
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<p>Interannual variations and trends in terrestrial water storage from 2002 to 2018: (<b>a</b>) the slope of TWS; (<b>b</b>) the significance level of TWS change trend.</p>
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<p>The linear regression trends between TWS and SWA in the CPEC from 2002 to 2018: (<b>a</b>) the linear regression trends between TWS and SWA; (<b>b</b>) the R square of linear regression trends between TWS and SWA.</p>
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<p>The correlation coefficient between TWS and driving factors in the CPEC: ** and * refer to the significant level with <span class="html-italic">p</span> &lt; 0.01 and <span class="html-italic">p</span> &lt; 0.05, respectively. CROP refers to the area of cropland, DAM represents the surface area of the dam, EVA represents the annual evaporation, POP represents population density, PRE represents annual precipitation, and TEM represents the annual average temperature.</p>
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<p>The contribution of driving factors to TWS changes in the CPEC (the wider the line, the greater the weight): CROP represents the area of cropland, EVA represents the annual evaporation, POP represents population density, PRE represents annual precipitation, and TEM represents the annual average temperature.</p>
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<p>Contributions of driving factors interaction to TWS in the CPEC: (<b>a</b>) Azad Kashmir; (<b>b</b>) Balochistan; (<b>c</b>) FATA; (<b>d</b>) Gilgit-Baltistan; (<b>e</b>) Kashi; (<b>f</b>) Sindh; (<b>g</b>) Khyber Pakhtunkhwa; (<b>h</b>) Punjab. CROP represents the area of cropland, EVA represents the annual evaporation, POP represents population density, PRE represents annual precipitation, and TEM represents the annual average temperature.</p>
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<p>SWA, TWS, and the Standardized Precipitation–Evapotranspiration Index: (<b>a</b>) Azad Kashmir; (<b>b</b>) Balochistan; (<b>c</b>) FATA; (<b>d</b>) Gilgit–Baltistan; (<b>e</b>) Kashi; (<b>f</b>) Sindh; (<b>g</b>) Khyber Pakhtunkhwa; (<b>h</b>) Punjab. The SPEI is the Standardized Precipitation–Evapotranspiration Index. The dashed lines indicate the dam’s construction year.</p>
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<p>Trends of population density in Central Asia at the national scale from the fourth version of the Gridded Population of the World from 2002 to 2018: (<b>a</b>) the density of population; (<b>b</b>) the significance level of the population density change trend; (<b>c</b>) spatial distribution of land cover types in 2018: 10: cropland (rainfed), 20: cropland (irrigated or post-flooding), 30: mosaic cropland–natural vegetation (coverage rate greater than 50% natural vegetation (tree, shrub, and herbaceous cover) and less than 50% cropland).</p>
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<p>The relationships between TWS trends, PSWA trends, SSWA trends, population density in 2018, and cropland area in 2018 at the 0.5° grid scale. (<b>a</b>) TWS trend and the cropland area in 2018; (<b>b</b>) TWS trend and the cropland area in 2018; (<b>c</b>) PSWA trend and the population density in 2018; (<b>d</b>) PSWA trend and the population density in 2018; (<b>e</b>) SSWA trend and thr cropland area in 2018; (<b>f</b>) SSWA trend and the population density in 2018.</p>
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17 pages, 2047 KiB  
Article
Optimizing Logistics and Transportation Locations in the China–Pakistan Economic Corridor: A Strategic Risk Assessment
by Muhammad Ilyas, Zhihong Jin and Irfan Ullah
Appl. Sci. 2024, 14(5), 1738; https://doi.org/10.3390/app14051738 - 21 Feb 2024
Cited by 3 | Viewed by 1429
Abstract
Logistics centers (LCs) have become a critical component of supply chain networks, playing an essential role in the development and implementation of logistics and supply chain management strategies. Recognizing the importance of LCs, Pakistan and China have initiated an extensive plan to establish [...] Read more.
Logistics centers (LCs) have become a critical component of supply chain networks, playing an essential role in the development and implementation of logistics and supply chain management strategies. Recognizing the importance of LCs, Pakistan and China have initiated an extensive plan to establish and expand an LC system as part of the China–Pakistan Economic Corridor (CPEC) initiative. However, the implementation of this plan has faced challenges due to the inadequate prioritization of factors used to identify LCs. This research proposes a structured framework for selecting LC locations, employing a combination of fuzzy logic and the technique for order of preference by similarity to the ideal solution (TOPSIS). These widely used methods address various challenges encountered in location selection. The findings highlight crucial logistics hubs in China and Pakistan, emphasizing factors such as port accessibility, freight demand, and transportation costs. The prioritization of criteria for LC selection is determined through the evaluation of variables and alternatives. The proposed framework enhances decision-making based on multiple criteria by addressing uncertainty and subjective assessments. Full article
(This article belongs to the Section Transportation and Future Mobility)
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<p>FD–TOPSIS methodology.</p>
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<p>Locations of logistics centers in Pakistan.</p>
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<p>Defuzzification values for factors.</p>
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<p>Factors and their ranks.</p>
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<p>Cost and location values.</p>
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<p>Security and management values.</p>
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23 pages, 5483 KiB  
Article
Flood Risk Assessment Based on Hydrodynamic Model—A Case of the China–Pakistan Economic Corridor
by Xiaolin Sun, Ke Jin, Hui Tao, Zheng Duan and Chao Gao
Water 2023, 15(24), 4295; https://doi.org/10.3390/w15244295 - 16 Dec 2023
Viewed by 2216
Abstract
Under global warming, flooding has become one of the most destructive natural disasters along the China–Pakistan Economic Corridor (CPEC), which significantly jeopardizes the construction and ongoing stability of the CPEC. The assessment of regional flood potential is, therefore, crucial for effective flood prevention [...] Read more.
Under global warming, flooding has become one of the most destructive natural disasters along the China–Pakistan Economic Corridor (CPEC), which significantly jeopardizes the construction and ongoing stability of the CPEC. The assessment of regional flood potential is, therefore, crucial for effective flood prevention and relief measures. In light of this, our study applied MIKE 11 hydrodynamic model for the Indus River Basin of Pakistan to achieve a comprehensive analysis of the flood-affected locations and depths under typical scenarios. The flood risk zones along the CPEC were evaluated using the indicator system method in conjunction with the combination weighting method. The results show that the hydrodynamic model has a Nash–Sutcliffe efficiency of 0.86, allowing for the investigation of floods at more precise temporal and spatial scales. Punjab, Sindh, and Balochistan Provinces are the main inundation areas under a 100-year flood scenario, with inundation depths ranging from 1 to 4 m. The coastal regions of Sindh and Hafizabad in Punjab witnessed the most severe floods, with maximum inundation depths exceeding 8 m. Flooding predominantly impacts the southeastern region of the CPEC. The medium- to high-risk zones comprise 25.56% of the region, while high-risk areas constitute 4.18%. Particularly, the eastern and southern regions of Punjab, along with the central and southern regions of Sindh, have been pinpointed as high-risk areas, primarily due to their dense population and riverine characteristics. Overall, our findings provide a scientific basis for informed decision making pertaining to disaster reduction and flood prevention. Full article
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<p>Sketch map of the China-Pakistan Economic Corridor.</p>
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<p>Comparison of observed and simulated water levels in validation period.</p>
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<p>Flooding depths in various areas of the CPEC under a 100-year flood scenario.</p>
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<p>Comparison of the observed flooded area during peak rainfall period with simulated flooded area.</p>
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<p>Four-day average flooding depth under a 100-year flood scenario.</p>
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<p>Normalized flood hazard assessment under a 100-year flood scenario.</p>
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<p>Normalization of vulnerability indicators.</p>
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<p>Vulnerability assessment.</p>
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<p>Normalization of sensitivity indicators.</p>
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<p>Sensitivity assessment.</p>
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<p>Flood risk assessment.</p>
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<p>Number of flooding events.</p>
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<p>Affected population in 2010.</p>
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12 pages, 609 KiB  
Article
Cyclopentenylcytosine (CPE-C): In Vitro and In Vivo Evaluation as an Antiviral against Adenoviral Ocular Infections
by Eric G. Romanowski, Kathleen A. Yates and Y. Jerold Gordon
Molecules 2023, 28(13), 5078; https://doi.org/10.3390/molecules28135078 - 29 Jun 2023
Cited by 1 | Viewed by 1200
Abstract
Adenoviruses are the major cause of ocular viral infections worldwide. Currently, there is no approved antiviral treatment for these eye infections. Cyclopentenylcytosine (CPE-C) is an antiviral that has demonstrated activity against more than 20 viruses. The goals of the current study were to [...] Read more.
Adenoviruses are the major cause of ocular viral infections worldwide. Currently, there is no approved antiviral treatment for these eye infections. Cyclopentenylcytosine (CPE-C) is an antiviral that has demonstrated activity against more than 20 viruses. The goals of the current study were to determine the in vitro and in vivo antiviral activity of CPE-C as well as its ocular toxicity. Antiviral activity was evaluated in vitro using standard plaque reduction assays to determine the 50% effective concentrations (EC50s) and in vivo in the Ad5/NZW rabbit ocular replication model. Ocular toxicity was determined in uninfected rabbit eyes following topical ocular application. The in vitro EC50s for CPE-C ranged from 0.03 to 0.059 μg/mL for nine adenovirus types that commonly infect the eye. Ocular toxicity testing determined CPE-C to be non-irritating or practically non-irritating by Draize scoring. In vivo, 3% CPE-C topically administered 4X or 2X daily for 7 days to adenovirus-infected eyes demonstrated effective antiviral activity compared with the negative control and comparable antiviral activity to the positive control, 0.5% cidofovir, topically administered twice daily for 7 days. We conclude CPE-C was relatively non-toxic to rabbit eyes and demonstrated potent anti-adenoviral activity in vitro and in vivo. Full article
(This article belongs to the Special Issue Recent Advances in Antiviral Drugs Discovery)
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<p>This figure presents the chemical structures of (<b>A</b>) CPE-C and (<b>B</b>) cidofovir. Both are nucleoside analogs of cytosine.</p>
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<p>This figure presents the percentages of daily HAdV5-positive cultures per Total for each group. 3% CPE-C 4X/day, 3% CPE-C 2X/day, and 0.5% cidofovir 2X/day significantly reduced the number of HAdV5-positive cultures per group on days 1, 3, 4, 5, 7, and 14 (<span class="html-italic">p</span> ≤ 0.0471, FET) compare with the saline control. There were no significant differences among the antiviral treatments on any of the culture days. In addition, 0.5% cidofovir 2X/day had significantly fewer HAdV5-positive cultures on day 9 than the saline control (<span class="html-italic">p</span> = 0.0197, FET).</p>
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15 pages, 373 KiB  
Article
Staff Members’ Experience of Italian Shelters for LGBTQIA+ Homeless and Runaway People: An Exploratory Study
by Elena Tubertini, Agostino Carbone and Massimo Santinello
Int. J. Environ. Res. Public Health 2023, 20(13), 6214; https://doi.org/10.3390/ijerph20136214 - 24 Jun 2023
Cited by 1 | Viewed by 2249
Abstract
Background: Some LGBTQIA+ people, after coming out, experience marginalization and homelessness due to rejection and discrimination from their family and community. The increase in support requests led to the creation of LGBTQIA+ temporary shelter homes worldwide. This study aims to explore the functioning [...] Read more.
Background: Some LGBTQIA+ people, after coming out, experience marginalization and homelessness due to rejection and discrimination from their family and community. The increase in support requests led to the creation of LGBTQIA+ temporary shelter homes worldwide. This study aims to explore the functioning and effectiveness of shelters, analyzing the experiences of staff members in Italy. Methods: Focus groups were held with a total of 15 staff members (age range: 32–53) working in three shelters for LGBTQIA+ people. Data were analyzed qualitatively through the grounded theory methodology. Results: Data coding showed five final core categories: (1) user characteristics; (2) staff characteristics; (3) community relations; (4) activities carried out by services; (5) criteria for intervention assessment and staff satisfaction. Results revealed some criticalities in the effectiveness of these services, particularly the difficulty in achieving autonomy for users, a weakness attributable to the non-exhaustive training of staff members and the funding discontinuity. Conclusion: To improve the efficacy of shelters, this study emphasizes the necessity to (a) carry out an analysis of the vulnerability of the local LGBTQIA+ community, (b) establish a stable network with local services (NHS system), and (c) implement staff members’ psychological training. Full article
(This article belongs to the Section Global Health)
14 pages, 254 KiB  
Article
The Health Silk Road: A Double-Edged Sword? Assessing the Implications of China’s Health Diplomacy
by Shaoyu Yuan
World 2023, 4(2), 333-346; https://doi.org/10.3390/world4020021 - 1 Jun 2023
Cited by 6 | Viewed by 6047
Abstract
The Health Silk Road (HSR) of the Belt and Road Initiative (BRI) of China aims to enhance public health and foster international cooperation in the healthcare sector. HSR objectives include strengthening healthcare infrastructure, expanding China’s global health leadership, and enhancing international health cooperation. [...] Read more.
The Health Silk Road (HSR) of the Belt and Road Initiative (BRI) of China aims to enhance public health and foster international cooperation in the healthcare sector. HSR objectives include strengthening healthcare infrastructure, expanding China’s global health leadership, and enhancing international health cooperation. The aim of this study was to examine the HSR and its implications for global health and international relations by using expert opinion analysis on known major HSR initiatives. We analyzed the objectives of HSR, including improving healthcare infrastructure, enhancing global health cooperation, and expanding China’s global health leadership. Additionally, as a case study, an in-depth analysis of the China-Pakistan collaboration on healthcare under the China-Pakistan Economic Corridor (CPEC) was conducted. This research posits that the HSR has a mix of positive and negative implications. Positive impacts of HSR include improved healthcare services, infrastructure, and capacity-building in participating countries. The main challenges include the quality and sustainability of the infrastructure and services provided, debt sustainability, transparency of projects, and China’s geopolitical influence. This research identified five motives behind China’s HSR: economic interests, diplomatic influence, reputation building, regional stability, and health security. The summary centers on CPEC and the WHO/Global collaboration. This research contributes to a nuanced understanding of the HSR’s multifaceted impacts and underscores the importance of open dialogue, cooperation, and the sharing of best practices among stakeholders. By assessing the motives, implications, and concerns of the HSR, this study offers valuable insights for policymakers, global health practitioners, and scholars, highlighting the significance of international collaboration. Full article
18 pages, 1375 KiB  
Article
Green Practices in Mega Development Projects of China–Pakistan Economic Corridor
by Shakir Ullah, Sergey Barykin, Ma Jianfu, Taher Saifuddin, Mohammed Arshad Khan and Ruben Kazaryan
Sustainability 2023, 15(7), 5870; https://doi.org/10.3390/su15075870 - 28 Mar 2023
Cited by 6 | Viewed by 4454
Abstract
This research aimed to investigate the green practices in the mega construction project of the China–Pakistan Economic Corridor (CPEC). Over recent years, there has been an increasing need for adopting and implementing more green and sustainable practices, leading to national and international sustainable [...] Read more.
This research aimed to investigate the green practices in the mega construction project of the China–Pakistan Economic Corridor (CPEC). Over recent years, there has been an increasing need for adopting and implementing more green and sustainable practices, leading to national and international sustainable and green environmental agendas. To address the issue, green project practices were considered an independent variable comprising green design, procurement, and construction. The dependent variables were environmental performance and economic performance. Primary data were collected from respondents working on the CPEC project. A representative sample of 276 respondents was used. The analysis was conducted using PLS-SEM. The results indicated that green design significantly influences economic performance, green procurement has a positive and significant effect on environmental performance, and green construction has a positive and significant impact on both environmental and economic and financial performance. The research showed that construction management at CPEC should adopt all facets of green project practices together, reducing negative environmental effects, increasing environmental benefits, and improving long-term economic performance in the area. Full article
(This article belongs to the Section Economic and Business Aspects of Sustainability)
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<p>Conceptual framework of the study. Source: Authors’ elaboration.</p>
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<p>Model fitness test.</p>
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<p>Structural equation results.</p>
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19 pages, 1066 KiB  
Article
The Mediating Roles of Economic, Socio-Cultural, and Environmental Factors to Predict Tourism Market Development by Means of Regenerative Travel: An Infrastructural Perspective of China–Pakistan Economic Corridor (CPEC)
by Maria Zulfaqar, Shahid Bashir, Samer Mohammed Ahmed Yaghmour, Jamshid Ali Turi and Musaib Hussain
Sustainability 2023, 15(6), 5025; https://doi.org/10.3390/su15065025 - 12 Mar 2023
Cited by 7 | Viewed by 3239
Abstract
Even though the significance of the China–Pakistan economic corridor (CPEC) is frequently discussed on various international forums, its economic, socio-cultural, and environmental impacts in a geographically constrained area have not yet been studied precisely. Consequently, the goal of this study is to look [...] Read more.
Even though the significance of the China–Pakistan economic corridor (CPEC) is frequently discussed on various international forums, its economic, socio-cultural, and environmental impacts in a geographically constrained area have not yet been studied precisely. Consequently, the goal of this study is to look into how CPEC Infrastructural Development (CPECID) would regenerate the tourism market in Gilgit Baltistan (GB), a Pakistani administrative territory. The basic data gathered via a convenience sample strategy is subjected to a quantitative analysis approach. In total, 336 inhabitants of GB participated in a closed-ended online survey that was used to gather data. The results showed that CPECID has a favorable influence on regenerative tourist growth and development in the area and that this link is partially mediated by economic, socio-cultural, and environmental impacts. The study’s conclusions have important implications for authorities creating regenerative tourist promotion plans, in addition to adding to the body of knowledge on tourism. Full article
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<p>Map of Gilgit Baltistan [<a href="#B28-sustainability-15-05025" class="html-bibr">28</a>].</p>
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<p>Conceptual model.</p>
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<p>Measurement Model for the study.</p>
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<p>Structural model with inner model path coefficient values and <span class="html-italic">p</span>-values.</p>
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