Comparative analysis of CO2 emissions and economic performance in the United States and China: Navigating sustainable development in the climate change era

Geoscience Frontiers, Journal Year: 2024, Volume and Issue: 15(5), P. 101843 - 101843

Published: April 16, 2024

Economic growth has brought global climate change into the spotlight, and CO2 emissions demonstrate significant challenges in reducing environmental shifts worldwide. Globally, United States China contribute greatest amount of emissions. The purpose this study is to examine relationship between different types economic by using a modeling approach. We analyze total emissions, coal oil share growth. This provides unique insights how simultaneously reduce sustain A bootstrap autoregressive distributed lag (BARDL) simulation method utilized long- short-run effects repressors on Coal are found have positive effect short run but negative impact over long States. needs implement stronger measures balance with for sustainable development. In contrast, both run. Thus, can continue from while maintaining Chinese policy be adapted implemented maintain carbon reduction. valuable policymakers seeking reduction, emphasizing need better understand emissions'

Language: Английский

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Geospatial modelling of seasonal water and electricity consumption in Doha's residential buildings using multiscale geographically weighted regression (MGWR) and Bootstrap analysis

Sustainable Cities and Society, Journal Year: 2024, Volume and Issue: 113, P. 105654 - 105654

Published: July 9, 2024

Ensuring sustainable water and electricity consumption in urban residential buildings is a growing challenge worldwide, particularly rapidly developing regions with harsh climates. This study examines the seasonal variation of Doha, Qatar, exploring interconnectedness land use/land cover (LULC) socio-demographic characteristics household consumption. For this purpose, we employed statistical analysis (i.e. Pearson correlation Bootstrap analysis) advanced geostatistical models, including Geographically Weighted Regression (GWR) Multiscale (MGWR), to analyze monitor spatial variations The methods involved assessing relationship between surface temperature (LST), water-electricity consumption, analyzing impact demographic variables. Key findings indicate significant spatiotemporal influenced by changes LULC such as size structure. highlight need for integrated planning energy policies that consider impacts enhance efficiency sustainability settings. Furthermore, results underscore importance addressing complex interplay development resource policy-making.

Language: Английский

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Comfort or cash? Lessons from the COVID-19 pandemic's impact on energy insecurity and energy limiting behavior in households

Energy Research & Social Science, Journal Year: 2024, Volume and Issue: 113, P. 103528 - 103528

Published: April 3, 2024

The COVID-19 pandemic has exacerbated the incidence of energy poverty in US. Existing literature mainly captures financial indicators during pandemic, inability to pay bills and disconnection utility service. However, alone cannot identify full extent poverty, as they miss out on limiting behavior. In this study, we conducted a survey eleven months into two US cities how people's behaviors have changed pandemic. collected information subjective including perceived household limit, ability cool home summer, tradeoff between consumption other necessities. Overall, found lower-income households reported disproportionately worse worsened status before where were off begin with, experienced disproportionally negative effects 33 % more than higher-income households. Comparing results from regions, saw 27 respondents both Chicago Phoenix report difficulty cooling their homes summer 2019, despite only having <25 Phoenix's degree days. To effectively eradicate insecurity, regions need measures clearly target them with assistance. We conclude that comprehensively understanding needs local populations is key providing timely, sufficient, human-centered assistance disasters beyond.

Language: Английский

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Global urban activity changes from COVID-19 physical distancing restrictions

Scientific Data, Journal Year: 2025, Volume and Issue: 12(1)

Published: Jan. 17, 2025

During the COVID-19 pandemic changes in human activity became widespread through official policies and organically response to virus's transmission, which turn, impacted environment economy. The has been described as a natural experiment that tested how social economic disruptions different components of global Earth System. To move this beyond hypotheses, locally-resolved, globally-available measures how, where, when changed are critically needed. Here we use satellite-derived nighttime lights quantify map daily atypical for each urban area globally two years after onset using machine learning anomaly detectors. Metrics characterizing from pre-COVID baseline settlements quality assurance reported. This dataset, TRacking Anomalous induced changEs NTL (TRACE-NTL), is first resolve all metropolitan regions globally, daily. It suitable support variety post-pandemic studies assess impact environmental systems.

Language: Английский

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Electricity Demand Forecasting Using Deep Polynomial Neural Networks and Gene Expression Programming During COVID-19 Pandemic

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(5), P. 2843 - 2843

Published: March 6, 2025

The power-generation mix of future grids will be quite diversified with the ever-increasing share renewable energy technologies. Therefore, prediction electricity demand become crucial for resource optimization and grid stability. Machine learning- artificial intelligence-based methods are widely studied by researchers to tackle forecasting problem. However, since COVID-19 pandemic broke out, new challenges have surfaced research. In such a short amount time, significant shifts emerged in trends, making it apparent that possibility similar crises escalated complexity management problems. Motivated circumstances, this research presents an hour-ahead day-ahead benchmark using Deep Polynomial Neural Networks (DNN) Gene Expression Programming (GEP) methods. DNN GEP algorithms utilize on-site consumption data collected from university hospital over two years temporal granularity 15-minute intervals. Quarter-hourly meteorological, calendar, daily data, including cases cumulative divided four restriction levels, were also considered. These datasets used not only predict but investigate impact on hospital. nRMSE results show outperforms 8.27% 14.32%, respectively. For computational times, appears much faster than 82.83% 78.56% forecasting,

Language: Английский

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An Overview of Energy and Exergy Analysis for Green Hydrogen Power Systems

Green energy and technology, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 24

Published: Jan. 1, 2024

Language: Английский

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The link between electricity consumption and stock market during the pandemic in Türkiye: a novel high-frequency approach

Environmental Science and Pollution Research, Journal Year: 2024, Volume and Issue: 31(11), P. 17311 - 17323

Published: Feb. 10, 2024

Abstract This article examines the relationship between electricity consumption and stock market in Turkish economy during COVID-19 pandemic. A novel high-frequency model is used, incorporating hourly energy Borsa Istanbul (BIST) National index variables. To determine effect of on vice versa, a VAR-based spillover approach, time-varying Granger causality, Bayesian VAR analysis are employed. The findings reveal positive weak but it has nature an emerging context post-COVID-19 period economy.

Language: Английский

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Review on Global Carbon Neutrality Development Based on Big Data Research in the Era of COVID-19: Challenges and Opportunities

Waste and Biomass Valorization, Journal Year: 2024, Volume and Issue: 15(9), P. 5093 - 5103

Published: April 16, 2024

Language: Английский

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Energy

Elsevier eBooks, Journal Year: 2024, Volume and Issue: unknown, P. 237 - 249

Published: Jan. 1, 2024

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Quantifying the impact of Covid-19 on the energy consumption in the low-income housing in Greater London

Journal of Physics Conference Series, Journal Year: 2023, Volume and Issue: 2600(13), P. 132002 - 132002

Published: Nov. 1, 2023

Abstract Covid-19 has caused great challenges to the energy sector, particularly in residential buildings with low-income households. This study investigates impact of confinement measures due outbreak on demand seven archetype Greater London. Three levels for occupant schedules are proposed and compared base case before Covid-19. The archetypes, their boundary conditions, input parameters set up according statistics from English Housing Survey (EHS) sample data housing. scenario (normal life without measures) is validated against measured consumption National Energy Efficiency Data-Framework (NEED) statistics. results show that electricity significantly lower than heating hot water all archetypes. By comparing full lockdown scenario, indicate (kWh) archetypes increases, average, by 10%, total increases 13%. highlights importance introducing detailed occupancy profiles multi-zone building simulation models during a pandemic leads greater shift towards home working, which may increase risk fuel poverty

Language: Английский

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