Towards sustainable transportation: A case study analysis of climate-responsive strategies in a developing nation

Case Studies in Thermal Engineering, Journal Year: 2024, Volume and Issue: 55, P. 104117 - 104117

Published: Feb. 12, 2024

According to the Global Climate Risk Index, Pakistan is fifth most vulnerable nation in world climate change. The growing phenomena of change and global warming have increased on a worldwide level. To combat effects change, transition sustainable transportation system essential. Developed countries evaluated costs benefits such transition. However, developing like rarely investigated this matter thoroughly. So, context, paper case study analyzing transport sector Punjab-Pakistan achieve some targets for transportation. analysis carried out by using energy model Low Emission Analysis Platform (LEAP) from 2019 2050. Three scenarios are made, i.e., Business as Usual Scenario (BAUS) following current policies, Efficient Combustion (ECS), Electrical Vehicle (EVS) figure environmental social costs. It concluded that 2050, ECS EVS will reduce carbon dioxide emissions 21.6 18.5 million metric tons equivalent, compared Business-as-Usual Scenario. These savings terms cost be $ 157.1 134.6 Electric This research may help find suitable policy decisions at provincial level enhance sustainability increasing share electric vehicles Punjab, results replicated whole country South Asia.

Language: Английский

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Innovate, conserve, grow: A comprehensive analysis of technological innovation, energy utilization, and carbon emission in BRICS

Natural Resources Forum, Journal Year: 2024, Volume and Issue: unknown

Published: June 30, 2024

Abstract The aim of this study was to embark on a transformative exploration the interplay between technological innovation, renewable energy, economic development, and carbon emissions in BRICS nations, unveiling novel insights that redefine sustainability paradigms contribute global environmental policymaking. This comprehensive spans years 1990–2022, meticulously examining dynamics indicators, energy consumption, generation, progress. dataset's non‐normal distribution prompts use moment quantile regression, providing nuanced with consideration for diverse slopes cross‐sectional dependencies. Validation through “Dumitrescu‐Hurlin panel Causality Test” refines findings, revealing diminishing impact innovation across quantiles. illuminates compelling connection: heightened correlates strongly reduced emissions, particularly evident at lower aligns seamlessly existing research, emphasizing technology's potential sustainability. Conversely, concerning positive association emerges utilization highlighting persistent challenge posed by escalating use. Urgent strategic interventions are underscored address ecological consequences associated rising consumption. intricate relationship electricity production unfolds, renewables' pivotal role mitigating impact. ongoing discussions regarding their indispensable contribution sustainable development. underscores importance prioritizing power initiatives. However, disconcerting surfaces development all quantiles, costs accompanying growth nations. As advances, escalate, presenting substantial challenges imperative balance progress conservation efforts. enriches discourse fostering within nations beyond, marking significant stride toward more environmentally conscious future.

Language: Английский

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Machine Learning for Optimising Renewable Energy and Grid Efficiency

Atmosphere, Journal Year: 2024, Volume and Issue: 15(10), P. 1250 - 1250

Published: Oct. 19, 2024

This research investigates the application of machine learning models to optimise renewable energy systems and contribute achieving Net Zero emissions targets. The primary objective is evaluate how can improve forecasting, grid management, storage optimisation, thereby enhancing reliability efficiency sources. methodology involved various models, including Long Short-Term Memory (LSTM), Random Forest, Support Vector Machines (SVMs), ARIMA, predict generation demand patterns. These were evaluated using metrics such as Mean Absolute Error (MAE) Root Squared (RMSE). Key findings include a 15% improvement in after optimisation 10–20% increase battery efficiency. Forest achieved lowest MAE, reducing prediction error by approximately 8.5%. study quantified CO2 emission reductions source, with wind power accounting for 15,000-ton annual reduction, followed hydropower solar 10,000 7500 tons, respectively. concludes that significantly enhance system performance, measurable errors emissions. improvements could help close “ambition gap” 20%, supporting global efforts meet 1.5 °C Paris Agreement

Language: Английский

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Assessment of land use change and carbon emission: A Log Mean Divisa (LMDI) approach

Heliyon, Journal Year: 2024, Volume and Issue: 10(3), P. e25669 - e25669

Published: Feb. 1, 2024

Changes in land use have a notable influence on carbon emissions since they can affect the levels of stored both soil and vegetation. To effectively analyze factors influencing from change, Log Mean Divisa (LMDI) method is commonly employed. The LMDI decomposition analysis that dissects changes into different factors, including shifts patterns, population growth, economic activity, energy intensity. This approach enables identification specific drivers emission development targeted policy interventions to address them. explore relationship between emissions, method, case study be conducted. involves selecting particular region or country experiencing change examining driving these transformations. Subsequently, applied decompose within selected country, thereby pinpointing major contributors changes. In our study, we observed necessity regulating consumption greenhouse gas urban communities through sustainable practices technologies. research highlighted variations consumption, renewable utilization, public transportation usage among cities China. Moreover, demonstrated patterns their associated alongside findings analysis, which explored based patterns. illuminates importance understanding employing as valuable analytical tool. It underscores significance technologies mitigating areas provides insights role shaping outcomes.

Language: Английский

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Assessing the Impacts of Eco-innovations, Economic Growth, Urbanization on Ecological Footprints in G-11: Exploring the Sustainable Development Policy Options

Journal of the Knowledge Economy, Journal Year: 2024, Volume and Issue: unknown

Published: Feb. 6, 2024

Language: Английский

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Application of power-law committee machine to combine five machine learning algorithms for enhanced oil recovery screening

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: April 22, 2024

Abstract One of the main challenges in screening enhanced oil recovery (EOR) techniques is class imbalance problem, where number different EOR not equal. This problem hinders generalization data-driven methods used to predict suitable for candidate reservoirs. The purpose this paper propose a novel approach overcome above challenge by taking advantage Power-Law Committee Machine (PLCM) technique optimized Particle Swam Optimization (PSO) combine output five cutting-edge machine learning with types algorithms. PLCM method has been previous studies screening. models include Artificial Neural Network (ANN), CatBoost, Random Forest (RF), K-Nearest Neighbors (KNN), and Support Vector (SVM). CatBoost first time work methods. role PSO find optimal values coefficients exponents power-law model. In study, bigger dataset than those studies, including 2563 successful worldwide experiences, was gathered. A improves prevents overfitting. hyperparameters individual machine-learning were tuned using fivefold cross-validation technique. results showed that all could unseen cases an average score 0.868. Among models, KNN SVM had highest scores value 0.894 0.892, respectively. Nonetheless, after combining method, predictions improved 0.963, which substantial increase. Finally, feature importance analysis conducted out most influential parameters on output. novelty having shown ability construct accurate model class-imbalance issue utilizing models. According analysis, gravity formation porosity recognized as

Language: Английский

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Using innovation and entrepreneurship for creating edge in service firms: A review research of tourism and hospitality industry

Journal of Innovation & Knowledge, Journal Year: 2024, Volume and Issue: 9(4), P. 100572 - 100572

Published: Sept. 25, 2024

Language: Английский

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Numerical investigation of carbon dioxide capture using nanofluids via machine learning

Journal of Cleaner Production, Journal Year: 2024, Volume and Issue: 450, P. 141916 - 141916

Published: March 24, 2024

Language: Английский

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Integrating Social Media-Driven Service Innovation and Sustainable Leadership: Advancing Sustainable Practices in Tourism and Hospitality

Sustainability, Journal Year: 2025, Volume and Issue: 17(2), P. 399 - 399

Published: Jan. 7, 2025

This study examines the impact of digital era, particularly increasing prevalence social media, on sustainable tourism and hospitality development, how industry leverages media to foster sustainability within sector. However, researchers policymakers have paid little attention this aspect. Research usage development still needs be revised made novel. Drawing organizational learning theory resource-based view, associations between service innovations, aiming develop tourism. Our research findings reveal a promising positive relationship which contributes Sustainable leadership also moderates relationship. significantly existing knowledge in field, with implications for academia, researchers, government entities focused digitalization supporting innovation, preparing future challenges.

Language: Английский

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Estimating intercity heavy truck mobility flows using the deep gravity framework

Transportation Research Part E Logistics and Transportation Review, Journal Year: 2023, Volume and Issue: 179, P. 103320 - 103320

Published: Oct. 10, 2023

Language: Английский

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