Pub Date : 2023-12-23DOI: 10.12974/2311-8741.2023.11.08
Xuemeng Liu, Fengtao Guang, Yating Deng
The advancement of green finance plays a pivotal role in filling the financing gap of carbon neutrality and promote the low-carbon transformation. However, there are relatively few empirical studies directly analyzing the nexus of green finance and carbon emission intensity, as well as their impact mechanism, nonlinear effect and spatial effect. Therefore, based on the panel data of 30 provinces and cities in China from 2007 to 2019, using System GMM (SYS-GMM), KHB, panel threshold model and spatial Durbin model (SDM), this paper investigates the effect and impact mechanisms of green finance on carbon emission (CO2). The results show that green finance significantly reduces CO2 intensity, which is still valid after a series of robustness tests. Second, the CO2 emission reduction effect of green finance exert asymmetric effects between financially developed and financially underdeveloped regions, industrially developed and industrially underdeveloped regions. Third, green finance mainly affects carbon emission intensity through factors such as FDI, energy consumption scale, energy intensity, green technology innovation, industrial structure upgrading and energy structure. Finally, CO2 emission reduction effect of green finance demonstrates nonlinear characteristics with diminishing marginal effects and spatial effects. Drawing upon these findings, this paper puts forward specific proposals on developing and innovating green finance to promote CO2 emission reduction and realize carbon neutrality.
{"title":"Does Green Finance Influence CO2 to Achieve Carbon Neutrality in China?","authors":"Xuemeng Liu, Fengtao Guang, Yating Deng","doi":"10.12974/2311-8741.2023.11.08","DOIUrl":"https://doi.org/10.12974/2311-8741.2023.11.08","url":null,"abstract":"The advancement of green finance plays a pivotal role in filling the financing gap of carbon neutrality and promote the low-carbon transformation. However, there are relatively few empirical studies directly analyzing the nexus of green finance and carbon emission intensity, as well as their impact mechanism, nonlinear effect and spatial effect. Therefore, based on the panel data of 30 provinces and cities in China from 2007 to 2019, using System GMM (SYS-GMM), KHB, panel threshold model and spatial Durbin model (SDM), this paper investigates the effect and impact mechanisms of green finance on carbon emission (CO2). The results show that green finance significantly reduces CO2 intensity, which is still valid after a series of robustness tests. Second, the CO2 emission reduction effect of green finance exert asymmetric effects between financially developed and financially underdeveloped regions, industrially developed and industrially underdeveloped regions. Third, green finance mainly affects carbon emission intensity through factors such as FDI, energy consumption scale, energy intensity, green technology innovation, industrial structure upgrading and energy structure. Finally, CO2 emission reduction effect of green finance demonstrates nonlinear characteristics with diminishing marginal effects and spatial effects. Drawing upon these findings, this paper puts forward specific proposals on developing and innovating green finance to promote CO2 emission reduction and realize carbon neutrality.","PeriodicalId":507810,"journal":{"name":"Journal of Environmental Science and Engineering Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139163049","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-18DOI: 10.12974/2311-8741.2023.11.06
Hone-Jay Chu, Tatas Tatas, Cheng-Wei Lin Wei Lin, Thomas Burbey
Land subsidence due to groundwater over-exploitation is a serious problem worldwide. Acquiring total pumping volumes to assess the stresses imposed that lead to subsidence is often difficult to quantify because groundwater extraction is often an unregulated water source. Consequently, pumping volumes represent a critical step for water resource managers to develop a strategic plan for mitigating land subsidence. In this investigation, we develop a time-dependent spatial regression (TSR) model to estimate monthly pumping volume over a ten-year period based on electricity consumption data. The estimated pumped volume is simplified as the spatial function of the electricity consumption and the electric power used by the water pump. Results show that the TSR approach can reduce the errors by 38% over linear regression models. The TSR model is applied to the Choshui alluvial fan in west-central Taiwan, where hundreds of thousands of unregulated pumping wells exist. The results show that groundwater peak extraction across the region occurs from January to May. Monthly pumping volume, and rainfall information are available to provide a better understanding of seasonal patterns and long-term changes of subsidence. Thus, the temporal regional subsidence patterns are found to respond to variations in pumping volume and rainfall.
{"title":"Rapid spatio-temporal pumping volume estimation from electricity consumption big data","authors":"Hone-Jay Chu, Tatas Tatas, Cheng-Wei Lin Wei Lin, Thomas Burbey","doi":"10.12974/2311-8741.2023.11.06","DOIUrl":"https://doi.org/10.12974/2311-8741.2023.11.06","url":null,"abstract":"Land subsidence due to groundwater over-exploitation is a serious problem worldwide. Acquiring total pumping volumes to assess the stresses imposed that lead to subsidence is often difficult to quantify because groundwater extraction is often an unregulated water source. Consequently, pumping volumes represent a critical step for water resource managers to develop a strategic plan for mitigating land subsidence. In this investigation, we develop a time-dependent spatial regression (TSR) model to estimate monthly pumping volume over a ten-year period based on electricity consumption data. The estimated pumped volume is simplified as the spatial function of the electricity consumption and the electric power used by the water pump. Results show that the TSR approach can reduce the errors by 38% over linear regression models. The TSR model is applied to the Choshui alluvial fan in west-central Taiwan, where hundreds of thousands of unregulated pumping wells exist. The results show that groundwater peak extraction across the region occurs from January to May. Monthly pumping volume, and rainfall information are available to provide a better understanding of seasonal patterns and long-term changes of subsidence. Thus, the temporal regional subsidence patterns are found to respond to variations in pumping volume and rainfall.","PeriodicalId":507810,"journal":{"name":"Journal of Environmental Science and Engineering Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139173235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-16DOI: 10.12974/2311-8741.2023.11.07
F. Mir
Persistent organic pollutants (POPs) are often referred to as "silent killers" due to their bio accumulative and long-term persistence. These can be found in every living thing, from plants to animals to people. These are to culprits for several environmental and human health problems. POPs are a leading cause of diabetes, obesity, endocrine disruption, cancer, cardiovascular disease, reproductive problems, and environmental damage. POP pollution and dangers are of concern to scientists, governments, and NGOs alike. This article reviews the most recent findings about the effects of POP contamination on human health and the natural environment.
{"title":"Persistent Organic Pollutants in Environment and Human Health","authors":"F. Mir","doi":"10.12974/2311-8741.2023.11.07","DOIUrl":"https://doi.org/10.12974/2311-8741.2023.11.07","url":null,"abstract":"Persistent organic pollutants (POPs) are often referred to as \"silent killers\" due to their bio accumulative and long-term persistence. These can be found in every living thing, from plants to animals to people. These are to culprits for several environmental and human health problems. POPs are a leading cause of diabetes, obesity, endocrine disruption, cancer, cardiovascular disease, reproductive problems, and environmental damage. POP pollution and dangers are of concern to scientists, governments, and NGOs alike. This article reviews the most recent findings about the effects of POP contamination on human health and the natural environment.","PeriodicalId":507810,"journal":{"name":"Journal of Environmental Science and Engineering Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139177124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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