Pub Date : 2024-07-21DOI: 10.1088/2515-7620/ad61c3
Oluwagbemisola D Akinsipe and Daniel M Kammen
From a current impact of under 4% of global greenhouse gas emissions, rapid industrialization and population growth in Africa could dramatically change the continent’s emissions profile. In this study, we develop an analytic framework to quantify future scenarios and project that, in mid- and green-growth scenarios, Africa’s emissions would amount to just 4%–13% of the planned carbon savings in major economies. However, in a high-growth scenario without climate-conscious development, African emissions could jeopardize global mitigation efforts. Less than 20 nations could account for 80%–90% of the continent’s emissions, highlighting the critical role of green growth pathways centered on rapid clean energy adoption in just a few countries to transform the continent’s energy landscape. A 20-fold increase in investment and project completion rates is required to meet the renewable energy targets in these countries’ Nationally Determined Contributions (NDCs). Our analysis underscores the need for nuanced country-specific strategies that prioritize equity and financial support for optimal climate and development progress in Africa.
{"title":"The African fulcrum to bend the curve of the climate crisis to a just transition","authors":"Oluwagbemisola D Akinsipe and Daniel M Kammen","doi":"10.1088/2515-7620/ad61c3","DOIUrl":"https://doi.org/10.1088/2515-7620/ad61c3","url":null,"abstract":"From a current impact of under 4% of global greenhouse gas emissions, rapid industrialization and population growth in Africa could dramatically change the continent’s emissions profile. In this study, we develop an analytic framework to quantify future scenarios and project that, in mid- and green-growth scenarios, Africa’s emissions would amount to just 4%–13% of the planned carbon savings in major economies. However, in a high-growth scenario without climate-conscious development, African emissions could jeopardize global mitigation efforts. Less than 20 nations could account for 80%–90% of the continent’s emissions, highlighting the critical role of green growth pathways centered on rapid clean energy adoption in just a few countries to transform the continent’s energy landscape. A 20-fold increase in investment and project completion rates is required to meet the renewable energy targets in these countries’ Nationally Determined Contributions (NDCs). Our analysis underscores the need for nuanced country-specific strategies that prioritize equity and financial support for optimal climate and development progress in Africa.","PeriodicalId":48496,"journal":{"name":"Environmental Research Communications","volume":"26 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141753977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-18DOI: 10.1088/2515-7620/ad5951
ChiSan Tsai, Yuka Ito, Jiaqi Liu and Tomochika Tokunaga
Land subsidence in low-lying coastal regions results from geological and human factors, causing inundation during high tides. Mitigation measures, like pumping stations and ditch systems, aim to address this challenge. However, their impact on groundwater salinity near tidal rivers is understudied. Using a coupled surface-subsurface model, we investigate this issue in the lower Nabaki River region, Shirako Town, Japan. The simulation reveals adverse effects of pumping stations that induce intrusion of saline water from the tidal river into surrounding groundwater. While they are designed to prevent floods, these stations and ditches may inadvertently raise groundwater vulnerability to saltwater contamination. Despite 2D model limitations, it offers valuable insights into coastal groundwater dynamics and salinization. This study provides important information for policymakers and land managers to better understand the consequences of flood mitigation strategies on groundwater quality in vulnerable coastal areas.
{"title":"The effects of land subsidence and its mitigating measures on shallow groundwater salinization in the low-lying coastal plain of East Japan","authors":"ChiSan Tsai, Yuka Ito, Jiaqi Liu and Tomochika Tokunaga","doi":"10.1088/2515-7620/ad5951","DOIUrl":"https://doi.org/10.1088/2515-7620/ad5951","url":null,"abstract":"Land subsidence in low-lying coastal regions results from geological and human factors, causing inundation during high tides. Mitigation measures, like pumping stations and ditch systems, aim to address this challenge. However, their impact on groundwater salinity near tidal rivers is understudied. Using a coupled surface-subsurface model, we investigate this issue in the lower Nabaki River region, Shirako Town, Japan. The simulation reveals adverse effects of pumping stations that induce intrusion of saline water from the tidal river into surrounding groundwater. While they are designed to prevent floods, these stations and ditches may inadvertently raise groundwater vulnerability to saltwater contamination. Despite 2D model limitations, it offers valuable insights into coastal groundwater dynamics and salinization. This study provides important information for policymakers and land managers to better understand the consequences of flood mitigation strategies on groundwater quality in vulnerable coastal areas.","PeriodicalId":48496,"journal":{"name":"Environmental Research Communications","volume":"338 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141740101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-16DOI: 10.1088/2515-7620/ad5b3e
Donglin Zong, Yefu Zhou, Jing Zhou, Xiaokang Hu, Tao Wang
Studies on the impact of land-use patterns on soil health and sustainability have indicated that land-use changes and unsuitable agricultural practices are key driving factors in the degradation of soil. However, the impact of land-use patterns on soil microbial diversity is not entirely consistent or known, and the specific effects of environmental factors need to be further considered. This study explored the impact of three different land-use patterns—rotation land (RL), garden land (GL), and uncultivated land (UL)—on soil health in a farming region by analyzing the soil physicochemical properties and the diversity of the soil bacterial and fungal communities. In this study, the results showed that the soil pH of GL was significantly lower than that of RL and UL, total nitrogen was lowest in GL, and available potassium and soil organic carbon were higher in RL and GL than in UL. The impact of the land-use patterns on microbial diversity was somewhat inconsistent, but greater on soil bacteria than fungi, with 17 bacterial and 4 fungal metabolic pathways showing significant differences. In particular, a decrease in the relative abundance of dominant bacteria was observed in GL. The land-use patterns had little impact on fungal functional genes; however, plant pathogen-related fungi were significantly higher in GL than in RL and UL. Overall, these results indicate that while the soil basic nutrients in different land-use patterns were high, long-term single planting (GL) still had a negative impact on the health and sustainability of the soil, especially owing to low soil pH. Therefore, when evaluating the effect of different planting systems on soil health, it is necessary to consider the true effect of local agricultural measures on soil properties and microbial community composition, and monitor for microbial diseases in the field to determine the impact of land-use patterns on crop production.
{"title":"Land use patterns influence in the soil microbial composition","authors":"Donglin Zong, Yefu Zhou, Jing Zhou, Xiaokang Hu, Tao Wang","doi":"10.1088/2515-7620/ad5b3e","DOIUrl":"https://doi.org/10.1088/2515-7620/ad5b3e","url":null,"abstract":"Studies on the impact of land-use patterns on soil health and sustainability have indicated that land-use changes and unsuitable agricultural practices are key driving factors in the degradation of soil. However, the impact of land-use patterns on soil microbial diversity is not entirely consistent or known, and the specific effects of environmental factors need to be further considered. This study explored the impact of three different land-use patterns—rotation land (RL), garden land (GL), and uncultivated land (UL)—on soil health in a farming region by analyzing the soil physicochemical properties and the diversity of the soil bacterial and fungal communities. In this study, the results showed that the soil pH of GL was significantly lower than that of RL and UL, total nitrogen was lowest in GL, and available potassium and soil organic carbon were higher in RL and GL than in UL. The impact of the land-use patterns on microbial diversity was somewhat inconsistent, but greater on soil bacteria than fungi, with 17 bacterial and 4 fungal metabolic pathways showing significant differences. In particular, a decrease in the relative abundance of dominant bacteria was observed in GL. The land-use patterns had little impact on fungal functional genes; however, plant pathogen-related fungi were significantly higher in GL than in RL and UL. Overall, these results indicate that while the soil basic nutrients in different land-use patterns were high, long-term single planting (GL) still had a negative impact on the health and sustainability of the soil, especially owing to low soil pH. Therefore, when evaluating the effect of different planting systems on soil health, it is necessary to consider the true effect of local agricultural measures on soil properties and microbial community composition, and monitor for microbial diseases in the field to determine the impact of land-use patterns on crop production.","PeriodicalId":48496,"journal":{"name":"Environmental Research Communications","volume":"19 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141881140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Formulating effective policies to address or mitigate deforestation requires a comprehensive understanding of the contributing factors. This study examines the drivers of deforestation from 2001 to 2020 in the Tangier-Tetouan-Al Hoceima (TTA) region, a northern Moroccan area distinguished by the country’s highest deforestation rate. Through an extensive review of existing literature and employing Geist and Lambin’s deforestation framework, we identified five key causes: infrastructure extension, agricultural expansion, logging, wildfires as direct causes, and demographic factors as an indirect cause. Data on deforestation and its contributing factors were sourced from diverse databases, including Global Forest Change (GFC), Global Land Analysis and Discovery (GLAD), Burned Area Product (MODIS Fire_CCI51), World Population, Forest Proximate People (FPP), and National Forest Inventory (NFI) datasets. Pixel-level analysis of GFC data indicated that wildfires are the primary driver of deforestation in the region, accounting for 35.2%, followed by agricultural expansion (30.6%), logging (13.2%), and infrastructure extension (10.1%). The remaining 10.9% of losses were attributed to other disturbances, such as illegal extraction, pests, and dieback. Spatial patterns were further analyzed through Exploratory Spatial Data Analysis (ESDA) methods at a 1 km2 gridded scale, revealing strong clustering for all studied factors. Spatial relationships were explored using the bivariate local Moran’s index, which highlighted the highest spatial dependence between deforestation and fires (I = 0.21). Correlations between deforestation and other factors, including agricultural expansion, logging, infrastructure extension, and demographic pressure, were assessed at 0.18, 0.17, 0.08, and 0.05, respectively. Landscape pressures (LSP), encompassing deforestation, agricultural expansion, fires, infrastructure extension, and demographic pressure, were analyzed using the local Geary index, revealing a positive correlation in approximately 59% of spatial units. Last, a composite map of LSP clusters and an explanatory diagram illustrating dominant patterns in the TTA region were generated based on the results from local Geary’s multivariate and local Moran’s univariate tests.
{"title":"Deforestation drivers in northern Morocco: an exploratory spatial data analysis","authors":"Hamid Boubekraoui, Yazid Maouni, Abdelilah Ghallab, Mohamed Draoui and Abdelfettah Maouni","doi":"10.1088/2515-7620/ad5ad6","DOIUrl":"https://doi.org/10.1088/2515-7620/ad5ad6","url":null,"abstract":"Formulating effective policies to address or mitigate deforestation requires a comprehensive understanding of the contributing factors. This study examines the drivers of deforestation from 2001 to 2020 in the Tangier-Tetouan-Al Hoceima (TTA) region, a northern Moroccan area distinguished by the country’s highest deforestation rate. Through an extensive review of existing literature and employing Geist and Lambin’s deforestation framework, we identified five key causes: infrastructure extension, agricultural expansion, logging, wildfires as direct causes, and demographic factors as an indirect cause. Data on deforestation and its contributing factors were sourced from diverse databases, including Global Forest Change (GFC), Global Land Analysis and Discovery (GLAD), Burned Area Product (MODIS Fire_CCI51), World Population, Forest Proximate People (FPP), and National Forest Inventory (NFI) datasets. Pixel-level analysis of GFC data indicated that wildfires are the primary driver of deforestation in the region, accounting for 35.2%, followed by agricultural expansion (30.6%), logging (13.2%), and infrastructure extension (10.1%). The remaining 10.9% of losses were attributed to other disturbances, such as illegal extraction, pests, and dieback. Spatial patterns were further analyzed through Exploratory Spatial Data Analysis (ESDA) methods at a 1 km2 gridded scale, revealing strong clustering for all studied factors. Spatial relationships were explored using the bivariate local Moran’s index, which highlighted the highest spatial dependence between deforestation and fires (I = 0.21). Correlations between deforestation and other factors, including agricultural expansion, logging, infrastructure extension, and demographic pressure, were assessed at 0.18, 0.17, 0.08, and 0.05, respectively. Landscape pressures (LSP), encompassing deforestation, agricultural expansion, fires, infrastructure extension, and demographic pressure, were analyzed using the local Geary index, revealing a positive correlation in approximately 59% of spatial units. Last, a composite map of LSP clusters and an explanatory diagram illustrating dominant patterns in the TTA region were generated based on the results from local Geary’s multivariate and local Moran’s univariate tests.","PeriodicalId":48496,"journal":{"name":"Environmental Research Communications","volume":"57 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141613946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-10DOI: 10.1088/2515-7620/ad5c60
Xujie Hu and Wanglin Yan
Unexpected events can have profound impacts on urban resource supply and consumption. The Great East Japan Earthquake (3.11 hereafter) triggered not only the planned blackout in the Tokyo Metropolitan Region soon after the disaster but also the energy shift to fossil fuels to recover from the disfunction of Fukushima nuclear power plants. Previous research has mainly focused on the direct energy consumption and carbon emissions of different sectors while the intensity and extensity of the impact on industries and the environment have never been empirically addressed. This study explores energy-use efficiency and carbon emissions in Tokyo from 2011 to 2015 through a lens of nexus using environmentally extended input-output analysis and community-wide carbon analytic approaches. Results show that the energy consumption is the largest exporter and importer of carbon emissions, whereas energy losses and carbon emissions caused by energy conversion and transmission are almost twice as much as those caused by the direct parts. Strong nexus effects among building and material, transportation, and energy consumption were observed. The 3.11 greatly impacted the energy structure and carbon emission patterns because of the increased consumption of coal for electricity. The share of energy consumption and carbon emissions by raw materials for construction also increased because of the increased demand for the reparation and reconstruction of buildings and transport systems. This structural change provided new scientific evidence for governments to implement decarbonization policies while preparing for unprecedented events.
{"title":"Quantifying the impact of earthquakes on urban energy consumption and carbon emissions in Tokyo from a nexus perspective","authors":"Xujie Hu and Wanglin Yan","doi":"10.1088/2515-7620/ad5c60","DOIUrl":"https://doi.org/10.1088/2515-7620/ad5c60","url":null,"abstract":"Unexpected events can have profound impacts on urban resource supply and consumption. The Great East Japan Earthquake (3.11 hereafter) triggered not only the planned blackout in the Tokyo Metropolitan Region soon after the disaster but also the energy shift to fossil fuels to recover from the disfunction of Fukushima nuclear power plants. Previous research has mainly focused on the direct energy consumption and carbon emissions of different sectors while the intensity and extensity of the impact on industries and the environment have never been empirically addressed. This study explores energy-use efficiency and carbon emissions in Tokyo from 2011 to 2015 through a lens of nexus using environmentally extended input-output analysis and community-wide carbon analytic approaches. Results show that the energy consumption is the largest exporter and importer of carbon emissions, whereas energy losses and carbon emissions caused by energy conversion and transmission are almost twice as much as those caused by the direct parts. Strong nexus effects among building and material, transportation, and energy consumption were observed. The 3.11 greatly impacted the energy structure and carbon emission patterns because of the increased consumption of coal for electricity. The share of energy consumption and carbon emissions by raw materials for construction also increased because of the increased demand for the reparation and reconstruction of buildings and transport systems. This structural change provided new scientific evidence for governments to implement decarbonization policies while preparing for unprecedented events.","PeriodicalId":48496,"journal":{"name":"Environmental Research Communications","volume":"13 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141613948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-09DOI: 10.1088/2515-7620/ad5b3b
Devanil Choudhury, Debashis Nath, Wen Chen
The response of the Asian Summer Monsoon (ASM) circulation to the Representative Concentration Pathway 4.5 and 8.5 (RCP4.5 and RCP8.5) forcing scenarios is examined using the CESM1 state-of-the-art global circulation model from 2021 to 2050. The projections show that monsoon precipitation will increase over East Asia, the North Pacific Ocean, the Indian Peninsula, and the Bay of Bengal under the RCP4.5 scenario. Conversely, the South Indian Ocean, West Asia, the Middle East, and the Central Pacific Ocean exhibit a decreasing trend in precipitation. Under the RCP8.5 scenario, precipitation is projected to increase over a wider swath of the Indian Ocean and the Middle East Asia. In the RCP4.5 scenario, the low-level wind circulation is likely to strengthen over the entire northern Indian Ocean, extending to the South China Sea, thereby increasing moisture transport from the Indian Ocean to peninsular India and the South China Sea. Conversely, in the RCP8.5 scenario, easterly winds strengthen over the South Indian Ocean, leading to an increase in moisture transport from the equatorial West Pacific Ocean to the Indian Ocean. A weak (strong) cyclonic circulation in response to the east-centered (west-centered) low sea level pressure trend over the North Pacific in RCP4.5 (RCP8.5) scenario is projected to help maintaining a strong (weak) ASM circulation from the India to east Asia. Internal climate variability is also calculated, revealing that the North Pacific Ocean near the Bering Sea is likely to play a dominating role and contribute significantly to the future ASM dynamics. In both scenarios, internal variability is found to substantially contribute to changes in monsoon circulation over the Indian Ocean.
{"title":"Asian summer monsoon responses under RCP4.5 and RCP8.5 scenarios in CESM large ensemble simulations","authors":"Devanil Choudhury, Debashis Nath, Wen Chen","doi":"10.1088/2515-7620/ad5b3b","DOIUrl":"https://doi.org/10.1088/2515-7620/ad5b3b","url":null,"abstract":"The response of the Asian Summer Monsoon (ASM) circulation to the Representative Concentration Pathway 4.5 and 8.5 (RCP4.5 and RCP8.5) forcing scenarios is examined using the CESM1 state-of-the-art global circulation model from 2021 to 2050. The projections show that monsoon precipitation will increase over East Asia, the North Pacific Ocean, the Indian Peninsula, and the Bay of Bengal under the RCP4.5 scenario. Conversely, the South Indian Ocean, West Asia, the Middle East, and the Central Pacific Ocean exhibit a decreasing trend in precipitation. Under the RCP8.5 scenario, precipitation is projected to increase over a wider swath of the Indian Ocean and the Middle East Asia. In the RCP4.5 scenario, the low-level wind circulation is likely to strengthen over the entire northern Indian Ocean, extending to the South China Sea, thereby increasing moisture transport from the Indian Ocean to peninsular India and the South China Sea. Conversely, in the RCP8.5 scenario, easterly winds strengthen over the South Indian Ocean, leading to an increase in moisture transport from the equatorial West Pacific Ocean to the Indian Ocean. A weak (strong) cyclonic circulation in response to the east-centered (west-centered) low sea level pressure trend over the North Pacific in RCP4.5 (RCP8.5) scenario is projected to help maintaining a strong (weak) ASM circulation from the India to east Asia. Internal climate variability is also calculated, revealing that the North Pacific Ocean near the Bering Sea is likely to play a dominating role and contribute significantly to the future ASM dynamics. In both scenarios, internal variability is found to substantially contribute to changes in monsoon circulation over the Indian Ocean.","PeriodicalId":48496,"journal":{"name":"Environmental Research Communications","volume":"2016 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141570764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-08DOI: 10.1088/2515-7620/ad5ccd
Jiajin Wang, Jie Guo, Chunxue Wang, Yanmei Pang
In recent years, the Chengdu-Chongqing Economic Circle (CCEC) has experienced frequent heat events, significantly impacting labor productivity. The CCEC is an important economic growth pole in western China. Therefore, an in-depth study of the impact of heat stress on labor productivity holds great significance for climate change adaptation and enhancing economic efficiency. Based on the relationship between the wet-bulb globe temperature (WBGT) and labor productivity of different industries, the labor productivity loss caused by heat in the CCEC was estimated using the observation data of the meteorological station and the projection results of the BCC-CSM2-MR model from the Coupled Model Intercomparison Project Phase 6 (CMIP6). The results showed that the impact of heat on the labor productivity of different industries in the CCEC mainly occurs from June to August, with the largest impact on agriculture, followed by industry, and the smallest impact on service sectors. Losses from heat stress to labor productivity in agriculture, industry, and services showed a significant increasing trend from 1980 to 2020 but a decreasing trend in comprehensive labor productivity loss. From 2020–2100, labor productivity losses in different industries due to heat stress show an increasing and then decreasing trend in the low emissions scenario, productivity losses in the medium emissions scenario are characterized by an increasing and then sustained change, and labor productivity losses in the high emissions scenario show a sustained increasing trend from 2020. By the end of the 21st century, the increase in labor productivity losses across different industries under the high emission scenario is approximately 15%–23%, and the large value center shifts slightly to the west. In most areas, the losses of agricultural, industrial, service, and comprehensive labor productivity exceed 45%, 32%, 20%, and 24%, respectively.
{"title":"Impact of global warming on labor productivity in the Chengdu-Chongqing economic circle, China","authors":"Jiajin Wang, Jie Guo, Chunxue Wang, Yanmei Pang","doi":"10.1088/2515-7620/ad5ccd","DOIUrl":"https://doi.org/10.1088/2515-7620/ad5ccd","url":null,"abstract":"In recent years, the Chengdu-Chongqing Economic Circle (CCEC) has experienced frequent heat events, significantly impacting labor productivity. The CCEC is an important economic growth pole in western China. Therefore, an in-depth study of the impact of heat stress on labor productivity holds great significance for climate change adaptation and enhancing economic efficiency. Based on the relationship between the wet-bulb globe temperature (WBGT) and labor productivity of different industries, the labor productivity loss caused by heat in the CCEC was estimated using the observation data of the meteorological station and the projection results of the BCC-CSM2-MR model from the Coupled Model Intercomparison Project Phase 6 (CMIP6). The results showed that the impact of heat on the labor productivity of different industries in the CCEC mainly occurs from June to August, with the largest impact on agriculture, followed by industry, and the smallest impact on service sectors. Losses from heat stress to labor productivity in agriculture, industry, and services showed a significant increasing trend from 1980 to 2020 but a decreasing trend in comprehensive labor productivity loss. From 2020–2100, labor productivity losses in different industries due to heat stress show an increasing and then decreasing trend in the low emissions scenario, productivity losses in the medium emissions scenario are characterized by an increasing and then sustained change, and labor productivity losses in the high emissions scenario show a sustained increasing trend from 2020. By the end of the 21st century, the increase in labor productivity losses across different industries under the high emission scenario is approximately 15%–23%, and the large value center shifts slightly to the west. In most areas, the losses of agricultural, industrial, service, and comprehensive labor productivity exceed 45%, 32%, 20%, and 24%, respectively.","PeriodicalId":48496,"journal":{"name":"Environmental Research Communications","volume":"18 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141570762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-05DOI: 10.1088/2515-7620/ad5ad9
Jinge Zhang, Chunxiang Li, Tianbao Zhao
Predicting future mean precipitation poses significant challenges due to uncertainties among climate models, complicating water resource management. In this study, we introduce a novel methodology to mitigate uncertainty in future mean precipitation projections over China on a grid-by-grid basis. By constraining precipitation parameters of the Gamma distribution, we establish emergent constraints on parameters, revealing significant correlations between historical and future simulations. Our analysis spans the periods 2040–2069 and 2070–2099 under low-to-moderate and high emission scenarios. We observe reductions in uncertainty across most regions of China, with constrained mean precipitation indicating increases in monsoon regions and decreases in non-monsoon zones relative to raw projections. Notably, the observed 30%–40% increase in mean precipitation for the whole of China underscores the efficacy of our methodology. These observationally constrained results provide valuable insights into current precipitation projections, offering actionable information for water resource planning and climate adaptation strategies amidst future uncertainties.
{"title":"Qualifying uncertainty of precipitation projections over China: mitigating uncertainty with emergent constraints","authors":"Jinge Zhang, Chunxiang Li, Tianbao Zhao","doi":"10.1088/2515-7620/ad5ad9","DOIUrl":"https://doi.org/10.1088/2515-7620/ad5ad9","url":null,"abstract":"Predicting future mean precipitation poses significant challenges due to uncertainties among climate models, complicating water resource management. In this study, we introduce a novel methodology to mitigate uncertainty in future mean precipitation projections over China on a grid-by-grid basis. By constraining precipitation parameters of the Gamma distribution, we establish emergent constraints on parameters, revealing significant correlations between historical and future simulations. Our analysis spans the periods 2040–2069 and 2070–2099 under low-to-moderate and high emission scenarios. We observe reductions in uncertainty across most regions of China, with constrained mean precipitation indicating increases in monsoon regions and decreases in non-monsoon zones relative to raw projections. Notably, the observed 30%–40% increase in mean precipitation for the whole of China underscores the efficacy of our methodology. These observationally constrained results provide valuable insights into current precipitation projections, offering actionable information for water resource planning and climate adaptation strategies amidst future uncertainties.","PeriodicalId":48496,"journal":{"name":"Environmental Research Communications","volume":"14 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141570763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-03DOI: 10.1088/2515-7620/ad5b3d
Jenila Vincent M and Varalakshmi P
Extracting individual buildings from satellite images is crucial for various urban applications, including population estimation, urban planning, and other related fields. However, Extracting building footprints from remote sensing data is a challenging task because of scale differences, complex structures and different types of building. Addressing these issues, an approach that can efficiently detect buildings in images by generating a segmentation mask for each instance is proposed in this paper. This approach incorporates the Regional Convolutional Neural Network (MASK-RCNN), which combines Faster R-CNN for object mask prediction and boundary box recognition and was evaluated against other models like YOLOv5, YOLOv7 and YOLOv8 in a comparative study to assess its effectiveness. The findings of this study reveals that our proposed method achieved the highest accuracy in building extraction. Furthermore, we performed experiments on well-established datasets like WHU and INRIA, and our method consistently outperformed other existing methods, producing reliable results.
{"title":"Extraction of building footprint using MASK-RCNN for high resolution aerial imagery","authors":"Jenila Vincent M and Varalakshmi P","doi":"10.1088/2515-7620/ad5b3d","DOIUrl":"https://doi.org/10.1088/2515-7620/ad5b3d","url":null,"abstract":"Extracting individual buildings from satellite images is crucial for various urban applications, including population estimation, urban planning, and other related fields. However, Extracting building footprints from remote sensing data is a challenging task because of scale differences, complex structures and different types of building. Addressing these issues, an approach that can efficiently detect buildings in images by generating a segmentation mask for each instance is proposed in this paper. This approach incorporates the Regional Convolutional Neural Network (MASK-RCNN), which combines Faster R-CNN for object mask prediction and boundary box recognition and was evaluated against other models like YOLOv5, YOLOv7 and YOLOv8 in a comparative study to assess its effectiveness. The findings of this study reveals that our proposed method achieved the highest accuracy in building extraction. Furthermore, we performed experiments on well-established datasets like WHU and INRIA, and our method consistently outperformed other existing methods, producing reliable results.","PeriodicalId":48496,"journal":{"name":"Environmental Research Communications","volume":"67 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141551063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-03DOI: 10.1088/2515-7620/ad5b3f
Guneet Sandhu, Olaf Weber, Michael O Wood, Horatiu A Rus and Jason Thistlethwaite
Extant literature reveals limited examination of risk management strategies and tools to support decision-making for sustainable water management in the private sector in Ontario, Canada. Moreover, a gap persists in understanding how water risks are prioritized and managed in the private sector. Addressing these gaps, this transdisciplinary study applied a novel normative-analytical risk governance theoretical framework to water security risks, which combines analytical risk estimation with normative priorities and insights of practitioners, to examine contextually-attuned water risk management strategies and develop a decision-support tool. Using mixed methods, the study first employed a survey to elicit practitioner priorities for seven water risk indicators and investigated water risk management approaches. Then, interviews were conducted to obtain in-depth understanding about the priorities, strategies, opportunities, and role of trust in water risk management. The study found that a combination of regulatory, voluntary, and multi-stakeholder participatory approaches is needed, contingent on the severity of water risks, sector, location, and context. Moreover, the criteria of flexibility, efficiency, strategic incentives, and economic and regulatory signals, are essential. Finally, using secondary data analysis, the study integrated interdisciplinary risk data with practitioner priorities to develop a first-of-a-kind decision-support tool for water risk management in Ontario, ‘WATR-DST’. WATR-DST is an automated tool that applies the study’s findings and assists multi-sector water-related decisions, practices, and investments by providing contextually-attuned risk information in a user-friendly format. Based on the user inputs (location, sector, and source type), it displays the severity of seven water risks, qualitative themes under public and media attention, and recommends water risk management strategies. Thus, the study contributes to knowledge in sustainability management, risk analysis, and environmental management by demonstrating the novel application of the normative-analytical framework for water risk management in the private sector. WATR-DST is a key contribution envisioned to improve multi-sector water-related decisions in Ontario.
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