Impacts of industrial agglomeration on the energy consumption structure’s low-carbon transition process: A spatial and nonlinear perspective DOI Creative Commons
Yuqing Liu

PLoS ONE, Journal Year: 2024, Volume and Issue: 19(9), P. e0307893 - e0307893

Published: Sept. 6, 2024

Based on panel data collected from 2003 to 2020 across 30 provinces in China, the paper employs spatial vector angle method and Durbin model investigate industrial agglomeration’s nonlinear spillover effects energy consumption structure’s low-carbon transition process (Lct). The results indicate following: First, influence of agglomeration Lct exhibits an inverted U-shaped pattern. As degree expands, its effect shifts positive negative. Second, demonstrates effects. It promotes improvement neighboring through However, continuous expansion inhibits congestion Third, heterogeneity test finds that has a significant role promoting samples eastern region, but this is not western middle regions.

Language: Английский

Artificial Intelligence in Energy Economics Research: A Bibliometric Review DOI Creative Commons
Zhilun Jiao, Chenrui Zhang,

Wenwen Li

et al.

Energies, Journal Year: 2025, Volume and Issue: 18(2), P. 434 - 434

Published: Jan. 20, 2025

Artificial intelligence (AI) is gaining attention in energy economics due to its ability process large-scale data as well make non-linear predictions and providing new development opportunities research subjects for research. The aim of this paper explore the trends application AI over decade spanning 2014–2024 through a systematic literature review, bibliometrics, network analysis. analysis shows that prominent themes are price forecasting, innovations systems, socio-economic impacts, transition, climate change. Potential future directions include supply-chain resilience security, social acceptance public participation, economic inequality technology gap, automated methods policy assessment, circular economy, digital economy. This innovative study contributes understanding from perspective bibliometrics inspires researchers think comprehensively about challenges hotspots.

Language: Английский

Citations

1
Can artificial intelligence reduce energy vulnerability? Evidence from an international perspective DOI
Lan Gao, Jing Wang

Energy Economics, Journal Year: 2025, Volume and Issue: unknown, P. 108491 - 108491

Published: April 1, 2025

Language: Английский

Citations

0
Quantifying global warming potential variations from greenhouse gas emission sources in forest ecosystems DOI Creative Commons
Mohammad Fazle Rabbi, Sándor Kovács

Carbon Research, Journal Year: 2024, Volume and Issue: 3(1)

Published: Oct. 15, 2024

Abstract Forest ecosystems play a crucial role in regulating greenhouse gas (GHG) emissions and mitigating climate change. This research aimed to evaluate the GHG of various sources within forested assess their respective contributions global warming potential (GWP), vital for developing more targeted strategies mitigate change, shaping policies, carbon accounting, sustainable forest management, advancing scientific comprehension ecosystem-climate dynamics. The study comprehensively analysed dioxide (CO 2 ), methane (CH 4 nitrous oxide (N O) EDGAR data deforestation, fires, natural processes such as organic soil decomposition ecosystems. assessment quantified CO equivalent each category from 1990 2022 forecasted till 2030. Our forecast shows that deforestation could reach between 3,990 4,529 metric ton (Mt) by 2030, with fires contributing an additional 750 Mt. Forestland absorption is expected decline -5134.80 Mt There uncertainty surrounding forecasts Organic (829.78 Mt) Other land (-764.53 Mt). In addition, was significant contributor emissions, GWP ranging 4000 4500, highlighting complex interplay human activities atmospheric patterns. Additionally, emit mix GHGs. potency these gases planet varies considerably, CH exhibiting range 500 700 equivalent, 900 1350 These variations depend on fire intensity its overall impact system. acts powerful sink, capturing negative values -7000 -6000. Researchers suggest multifaceted strategy stricter enforcement forestry regulations, investing projects promote sequestration, reforestation. advancements drone technology, satellite imagery, remote sensing advanced analytics can aid detecting change impacts, ultimately paving way neutrality. Graphical

Language: Английский

Citations

3
Impacts of industrial agglomeration on the energy consumption structure’s low-carbon transition process: A spatial and nonlinear perspective DOI Creative Commons
Yuqing Liu

PLoS ONE, Journal Year: 2024, Volume and Issue: 19(9), P. e0307893 - e0307893

Published: Sept. 6, 2024

Based on panel data collected from 2003 to 2020 across 30 provinces in China, the paper employs spatial vector angle method and Durbin model investigate industrial agglomeration’s nonlinear spillover effects energy consumption structure’s low-carbon transition process (Lct). The results indicate following: First, influence of agglomeration Lct exhibits an inverted U-shaped pattern. As degree expands, its effect shifts positive negative. Second, demonstrates effects. It promotes improvement neighboring through However, continuous expansion inhibits congestion Third, heterogeneity test finds that has a significant role promoting samples eastern region, but this is not western middle regions.

Language: Английский

Citations

0