Greenhouse gas emissions from hydroelectric reservoirs: mechanistic understanding of influencing factors and future prospect

Kumar, Amit; Chaturvedi, Ashish; Joshi, Neeshu; Mondal, Raju; Malyan, Sandeep K.

doi:10.1007/s11356-023-25717-y

Cited by 3 publications

(4 citation statements)

References 96 publications

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“…Review of crucial approaches and methods for the prediction of GHG emissions associated with reservoirs, investigating also life cycle assessment, uncertainty sources, and knowledge gaps. [23] Operation strategies.…”

Section: Ghg Emissionsmentioning

confidence: 99%

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Digitalization, Industry 4.0, Data, KPIs, Modelization and Forecast for Energy Production in Hydroelectric Power Plants: A Review

Pepe,

Zanoli

2024

Energies

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Intelligent water usage is required in order to target the challenging goals for 2030 and 2050. Hydroelectric power plants represent processes wherein water is exploited as a renewable resource and a source for energy production. Hydroelectric power plants usually include reservoirs, valves, gates, and energy production devices, e.g., turbines. In this context, monitoring and maintenance policies together with control and optimization strategies, at the different levels of the automation hierarchy, may represent strategic tools and drivers for energy efficiency improvement. Nowadays, these strategies rely on different basic concepts and elements, which must be assessed and investigated in order to provide a reliable background. This paper focuses on a review of the state of the art associated with these basic concepts and elements, i.e., digitalization, Industry 4.0, data, KPIs, modelization, and forecast.

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Section: Ghg Emissionsmentioning

confidence: 99%

“…Energy revolution for the hydropower sector [22]; • Analysis of the greenhouse gas (GHG) emissions in the hydropower sector [23];…”

Section: Introductionmentioning

confidence: 99%

Digitalization, Industry 4.0, Data, KPIs, Modelization and Forecast for Energy Production in Hydroelectric Power Plants: A Review

Pepe,

Zanoli

2024

Energies

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“…T o u n d e r s t a n d h o w t h e u r b a n f o r m a f f e c t C E r e q u i r e s a c a p a b i l i t y t o a c c u r a t e l y m o d e l greenhouse gas concentration, as well as a comprehensive dataset to capture factors influencing CE at the individual and regional levels (Kumar et al, 2023). However, it has long been challenging to model the complex urban environmental phenomena which are highly variable in time and space (Jordan and Mitchell, 2015;Helm et al, 2020).…”

Section: Urban Form and Cementioning

confidence: 99%

Predicting Neighborhood-Level Residential Carbon Emission from Street-view Images Using Computer Vision and Machine Learning

Shi,

Xiang,

Ying

et al. 2024

Preprint

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Measuring and predicting Carbon Emission (CE) is important to enabling the main culprit of various urgent environmental issues including global warming. However, prior studies did not fully incorporate the impact of micro-level urban streetscapes, which might lead to biased prediction of urban CE. To fill the gap, we developed an effective framework to predict residential CE in urban areas from widely existing and publicly available street-view images (SVI) using machine learning. First, we used a semantic segmentation algorithm to classify more than 30 streetscape elements from SVI images to describe the built environment whose features might affect residential and transportation CE. Second, based on the streetscapes quantified, we trained a 10-fold cross-validation method with various machine learning models to predict the CE at the 1KM grid level using CE data from the PlanetData. We found first, built environment features such as sidewalks, roads, fences, buildings, and walls are significantly correlated with the residential CE. Second, the presence of buildings and subtle streetscape features (e.g., walls, fences) indicates higher-density residential areas which are related to more residential CE. Third, vegetation (e.g., trees and grass) are reversely related to residential CE. Our findings shed light on the feasibility of using a single and open data source (i.e., the SVI) to effectively model neighborhood-level CE for regions across diverse urban forms. Our framework is useful for urban planners to inform new town development and urban regeneration towards the low CE goals.

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“…Understanding how the urban form affects CEs requires the capability to accurately model greenhouse gas concentrations. It also requires a comprehensive dataset to capture factors influencing CEs at the individual and regional levels [20]. However, it has long been challenging to model the complex urban environment, which is highly variable in space and time [21,22].…”

Section: Introduction 1urban Form and Cesmentioning

confidence: 99%

Predicting Neighborhood-Level Residential Carbon Emissions from Street View Images Using Computer Vision and Machine Learning

Shi,

Xiang,

Ying

et al. 2024

Remote Sensing

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Predicting urban-scale carbon emissions (CEs) is crucial in drawing implications for various urgent environmental issues, including global warming. However, prior studies have overlooked the impact of the micro-level street environment, which might lead to biased prediction. To fill this gap, we developed an effective machine learning (ML) framework to predict neighborhood-level residential CEs based on a single data source, street view images (SVIs), which are publicly available worldwide. Specifically, more than 30 streetscape elements were classified from SVIs using semantic segmentation to describe the micro-level street environment, whose visual features can indicate major socioeconomic activities that significantly affect residential CEs. A ten-fold cross-validation was deployed to train ML models to predict the residential CEs at the 1 km grid level. We found, first, that random forest (R2 = 0.8) outperforms many traditional models, confirming that visual features are non-negligible in explaining CEs. Second, more building, wall, and fence views indicate higher CEs. Third, the presence of trees and grass is inversely related to CEs. Our findings justify the feasibility of using SVIs as a single data source to effectively predict neighborhood-level residential CEs. The framework is applicable to large regions across diverse urban forms, informing urban planners of sustainable urban form strategies to achieve carbon-neutral goals, especially for the development of new towns.

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Greenhouse gas emissions from hydroelectric reservoirs: mechanistic understanding of influencing factors and future prospect

Cited by 3 publications

References 96 publications

Digitalization, Industry 4.0, Data, KPIs, Modelization and Forecast for Energy Production in Hydroelectric Power Plants: A Review

Digitalization, Industry 4.0, Data, KPIs, Modelization and Forecast for Energy Production in Hydroelectric Power Plants: A Review

Predicting Neighborhood-Level Residential Carbon Emission from Street-view Images Using Computer Vision and Machine Learning

Predicting Neighborhood-Level Residential Carbon Emissions from Street View Images Using Computer Vision and Machine Learning

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